Parameters and Calculations Reference
Comprehensive Documentation of Economic Model Variables
Economic analysis and ROI calculations for a 1% treaty and decentralized framework for drug assessment (dFDA)
Abstract
By redirecting 1% of global military spending to hyper-efficient pragmatic clinical trials, humanity can achieve 514 years of medical research in 20 years and shift the cure of every disease forward by 8.2 years, saving 416 million lives and generating $1.2 quadrillion in value.
Keywords
war-on-disease, 1-percent-treaty, medical-research, public-health, peace-dividend, decentralized-trials, dfda, dih, victory-bonds, health-economics, cost-benefit-analysis, clinical-trials, drug-development, regulatory-reform, military-spending, peace-economics, decentralized-governance, wishocracy, blockchain-governance, impact-investing
1 Overview
This appendix provides comprehensive documentation of all parameters and calculations used in the economic analysis of a 1% treaty and decentralized framework for drug assessment.
Total parameters: 331
- External sources (peer-reviewed): 133
- Calculated values: 105
- Core definitions: 93
2 Calculated Values
Parameters derived from mathematical formulas and economic models.
2.1 Combined Peace and Health Dividends for ROI Calculation
Value: $155B
Combined peace and health dividends for ROI calculation
Inputs:
- Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $114B
- Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $41.5B
\[ Dividend_{ann} = Cost_{soc,ann} + Benefit_{gross,ann} = \$113.55B + \$41.50B = \$155.05B \]
Methodology: ../appendix/peace-dividend-calculations#peace-dividend-composition
✓ High confidence
2.1.1 Sensitivity Analysis
Sensitivity Indices for Combined Peace and Health Dividends for ROI Calculation
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Peace Dividend Annual Societal Benefit | 0.7305 | Strong driver |
| dFDA R&D Gross Savings Annual | 0.3480 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.1.2 Monte Carlo Distribution
Simulation Results Summary: Combined Peace and Health Dividends for ROI Calculation
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $155B |
| Mean (expected value) | $154B |
| Median (50th percentile) | $152B |
| Standard Deviation | $23.1B |
| 90% Confidence Interval | [$119B, $195B] |
The histogram shows the distribution of Combined Peace and Health Dividends for ROI Calculation across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.1.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Combined Peace and Health Dividends for ROI Calculation will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.2 Total Annual Decentralized Framework for Drug Assessment Operational Costs
Value: $40M
Total annual Decentralized Framework for Drug Assessment operational costs (sum of all components: $15M + $10M + $8M + $5M + $2M)
Inputs:
- Decentralized Framework for Drug Assessment Maintenance Costs: $15M (95% CI: $10M - $22M)
- Decentralized Framework for Drug Assessment Staff Costs: $10M (95% CI: $7M - $15M)
- Decentralized Framework for Drug Assessment Infrastructure Costs: $8M (95% CI: $5M - $12M)
- Decentralized Framework for Drug Assessment Regulatory Coordination Costs: $5M (95% CI: $3M - $8M)
- Decentralized Framework for Drug Assessment Community Support Costs: $2M (95% CI: $1M - $3M)
\[ OPEX_{total} = \$15M \text{ (plat)} + \$10M \text{ (staff)} + \$8M \text{ (infra)} + \$5M \text{ (reg)} + \$2M \text{ (comm)} = \$40M \]
Methodology: ../appendix/dfda-cost-benefit-analysis#opex-breakdown
✓ High confidence
2.2.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Decentralized Framework for Drug Assessment Operational Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA OPEX Platform Maintenance | 0.3542 | Moderate driver |
| dFDA OPEX Staff | 0.2355 | Weak driver |
| dFDA OPEX Infrastructure | 0.2060 | Weak driver |
| dFDA OPEX Regulatory | 0.1469 | Weak driver |
| dFDA OPEX Community | 0.0576 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.2.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Decentralized Framework for Drug Assessment Operational Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $40M |
| Mean (expected value) | $39.9M |
| Median (50th percentile) | $39M |
| Standard Deviation | $8.21M |
| 90% Confidence Interval | [$27.3M, $55.6M] |
The histogram shows the distribution of Total Annual Decentralized Framework for Drug Assessment Operational Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.2.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Decentralized Framework for Drug Assessment Operational Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.3 Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings
Value: $41.5B
Annual Decentralized Framework for Drug Assessment benefit from R&D savings (trial cost reduction, secondary component)
Inputs:
- Annual Global Spending on Clinical Trials 📊: $83B (95% CI: $60B - $110B)
- Decentralized Framework for Drug Assessment Trial Cost Reduction Percentage: 50% (95% CI: 40% - 65%)
\[ Benefit_{DFDA,ann} = Trials_{ann} \times Reduction = \$83.00B \times 50.0\% = \$41.50B \]
Methodology: ../appendix/dfda-cost-benefit-analysis#cost-reduction
✓ High confidence
2.3.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Clinical Trials Spending Annual | 0.7426 | Strong driver |
| Trial Cost Reduction % | 0.6577 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.3.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $41.5B |
| Mean (expected value) | $41.3B |
| Median (50th percentile) | $40.7B |
| Standard Deviation | $8.02B |
| 90% Confidence Interval | [$29.1B, $55.6B] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.3.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.4 Expected Treaty ROI (Risk-Adjusted)
Value: 11.9k
Expected ROI for 1% treaty accounting for political success probability uncertainty. Monte Carlo samples POLITICAL_SUCCESS_PROBABILITY from beta(0.1%, 10%) distribution to generate full expected value distribution. Central value uses 1% probability.
Inputs:
- Treaty ROI - Lag Elimination (PRIMARY) 🔢: 1.19M ratio
- Political Success Probability 📊: 1% (95% CI: 0.1% - 10%)
\[ E[ROI] = ROI_{conditional} \times P_{success} = ROI_{treaty} \times 0.01 \]
Methodology: Direct Calculation
? Low confidence
2.4.1 Sensitivity Analysis
Sensitivity Indices for Expected Treaty ROI (Risk-Adjusted)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Political Success Probability | 0.9989 | Strong driver |
| Treaty ROI Lag Elimination | 0.0224 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.4.2 Monte Carlo Distribution
Simulation Results Summary: Expected Treaty ROI (Risk-Adjusted)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 11.9k |
| Mean (expected value) | 12.6k |
| Median (50th percentile) | 1.99k |
| Standard Deviation | 22.3k |
| 90% Confidence Interval | [1.17k, 61.4k] |
The histogram shows the distribution of Expected Treaty ROI (Risk-Adjusted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.4.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Expected Treaty ROI (Risk-Adjusted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.5 Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)
Value: $41.5B
Annual net savings from R&D cost reduction only (gross savings minus operational costs, excludes regulatory delay value)
Inputs:
- Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $41.5B
- Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
\[ Savings_{net} = \$41.5B - \$0.04B = \$41.46B \]
Methodology: ../appendix/dfda-cost-benefit-analysis#net-savings
✓ High confidence
2.5.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA R&D Gross Savings Annual | 1.0008 | Strong driver |
| dFDA Annual OPEX | -0.0010 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.5.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $41.5B |
| Mean (expected value) | $41.3B |
| Median (50th percentile) | $40.7B |
| Standard Deviation | $8.02B |
| 90% Confidence Interval | [$29.1B, $55.5B] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.5.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.6 Decentralized Framework for Drug Assessment Total NPV Annual OPEX
Value: $40M
Total NPV annual opex (Decentralized Framework for Drug Assessment core + DIH initiatives)
Inputs:
- Decentralized Framework for Drug Assessment Core Platform Annual OPEX: $18.9M (95% CI: $11M - $26.5M)
- DIH Broader Initiatives Annual OPEX: $21.1M (95% CI: $14M - $32M)
\[ C_{op} = \$0.01895B + \$0.02110B = \$0.04005B \text{ (annual operational cost)} \]
Methodology: ../appendix/dfda-cost-benefit-analysis#npv-costs
✓ High confidence
2.6.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Annual OPEX
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| DIH NPV Annual OPEX Initiatives | 0.5419 | Strong driver |
| dFDA NPV Annual OPEX | 0.4592 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.6.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Annual OPEX
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $40M |
| Mean (expected value) | $39.9M |
| Median (50th percentile) | $39.1M |
| Standard Deviation | $8.04M |
| 90% Confidence Interval | [$27.5M, $55.4M] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Annual OPEX across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.6.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Annual OPEX will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.7 NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)
Value: $275B
NPV of Decentralized Framework for Drug Assessment R&D savings only with 5-year adoption ramp (10-year horizon, most conservative financial estimate)
Inputs:
- Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) 🔢: $41.5B
- Standard Discount Rate for NPV Analysis: 3%
\[ PV_{benefits} = \sum_{t=1}^{10} \frac{NetSavings_{RD} \times \min(t,5)/5}{(1+r)^t} \approx \$249.3B \text{ (5-year linear adoption ramp)} \]
Methodology: ../appendix/dfda-cost-benefit-analysis#npv-benefit
✓ High confidence
2.7.1 Sensitivity Analysis
Sensitivity Indices for NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA Net Savings R&D Only Annual | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.7.2 Monte Carlo Distribution
Simulation Results Summary: NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $275B |
| Mean (expected value) | $274B |
| Median (50th percentile) | $270B |
| Standard Deviation | $53.3B |
| 90% Confidence Interval | [$193B, $369B] |
The histogram shows the distribution of NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.7.3 Exceedance Probability
This exceedance probability chart shows the likelihood that NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.8 NPV Net Benefit (R&D Only, Conservative)
Value: $275B
NPV net benefit using R&D savings only (most conservative financial estimate, excludes regulatory delay health value)
Inputs:
\[ Benefit_{NPV} = \sum_{t=1}^{10} \frac{NetSavings_{RD} \times \min(t,5)/5}{(1+r)^t} \approx \$249.3B \text{ (5-year linear adoption ramp)} \]
Methodology: ../appendix/dfda-cost-benefit-analysis#npv-net-benefit
✓ High confidence
2.8.1 Sensitivity Analysis
Sensitivity Indices for NPV Net Benefit (R&D Only, Conservative)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA NPV Benefit R&D Only | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.8.2 Monte Carlo Distribution
Simulation Results Summary: NPV Net Benefit (R&D Only, Conservative)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $275B |
| Mean (expected value) | $274B |
| Median (50th percentile) | $270B |
| Standard Deviation | $53.3B |
| 90% Confidence Interval | [$193B, $369B] |
The histogram shows the distribution of NPV Net Benefit (R&D Only, Conservative) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.8.3 Exceedance Probability
This exceedance probability chart shows the likelihood that NPV Net Benefit (R&D Only, Conservative) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.9 Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years
Value: $342M
Present value of annual opex over 10 years (NPV formula)
Inputs:
- Decentralized Framework for Drug Assessment Total NPV Annual OPEX 🔢: $40M
- Standard Discount Rate for NPV Analysis: 3%
- Standard Time Horizon for NPV Analysis: 10 years
\[ PV_{opex} = \$0.04005B \times \frac{1 - 1.08^{-10}}{0.08} \approx \$0.269B \]
Methodology: ../appendix/dfda-cost-benefit-analysis#npv-calculation
✓ High confidence
2.9.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA NPV Annual OPEX Total | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.9.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $342M |
| Mean (expected value) | $340M |
| Median (50th percentile) | $333M |
| Standard Deviation | $68.6M |
| 90% Confidence Interval | [$235M, $473M] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.9.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.10 Decentralized Framework for Drug Assessment Total NPV Cost
Value: $611M
Total NPV cost (upfront + PV of annual opex)
Inputs:
- Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years 🔢: $342M
- Decentralized Framework for Drug Assessment Total NPV Upfront Costs 🔢: $270M
\[ TotalCost_{NPV} = \$0.26975B + \$0.269B \approx \$0.54B \]
Methodology: ../appendix/dfda-cost-benefit-analysis#npv-total-cost
✓ High confidence
2.10.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Cost
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA NPV Pv Annual OPEX | 0.5417 | Strong driver |
| dFDA NPV Upfront Cost Total | 0.4585 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.10.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Cost
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $611M |
| Mean (expected value) | $609M |
| Median (50th percentile) | $595M |
| Standard Deviation | $127M |
| 90% Confidence Interval | [$415M, $853M] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.10.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.11 Decentralized Framework for Drug Assessment Total NPV Upfront Costs
Value: $270M
Total NPV upfront costs (Decentralized Framework for Drug Assessment core + DIH initiatives)
Inputs:
- Decentralized Framework for Drug Assessment Core Platform Build Cost: $40M (95% CI: $25M - $65M)
- DIH Broader Initiatives Upfront Cost: $230M (95% CI: $150M - $350M)
\[ C_0 = \$0.040B + \$0.22975B = \$0.26975B \text{ (upfront cost)} \]
Methodology: ../appendix/dfda-cost-benefit-analysis#npv-costs
✓ High confidence
2.11.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Upfront Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| DIH NPV Upfront Cost Initiatives | 0.8338 | Strong driver |
| dFDA NPV Upfront Cost | 0.1662 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.11.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Upfront Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $270M |
| Mean (expected value) | $269M |
| Median (50th percentile) | $262M |
| Standard Deviation | $58.1M |
| 90% Confidence Interval | [$181M, $380M] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Upfront Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.11.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Upfront Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.12 Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings
Value: $41.5B
Annual Decentralized Framework for Drug Assessment benefit from R&D savings (trial cost reduction, secondary component)
Inputs:
- Annual Global Spending on Clinical Trials 📊: $83B (95% CI: $60B - $110B)
- Decentralized Framework for Drug Assessment Trial Cost Reduction Percentage: 50% (95% CI: 40% - 65%)
\[ Benefit_{gross,ann} = Trials_{ann} \times Reduction = \$83.00B \times 50.0\% = \$41.50B \]
Methodology: ../appendix/dfda-cost-benefit-analysis#cost-reduction
✓ High confidence
2.12.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Clinical Trials Spending Annual | 0.7426 | Strong driver |
| Trial Cost Reduction % | 0.6577 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.12.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $41.5B |
| Mean (expected value) | $41.3B |
| Median (50th percentile) | $40.7B |
| Standard Deviation | $8.02B |
| 90% Confidence Interval | [$29.1B, $55.6B] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.12.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.13 Daily R&D Savings from Trial Cost Reduction
Value: $114M
Daily R&D savings from trial cost reduction (opportunity cost of delay)
Inputs:
\[ Savings_{daily} = \frac{\$41.5B}{365} = \$113.7M \]
Methodology: ../appendix/dfda-cost-benefit-analysis#daily-opportunity-cost-of-inaction
✓ High confidence
2.13.1 Sensitivity Analysis
Sensitivity Indices for Daily R&D Savings from Trial Cost Reduction
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA Benefit R&D Only Annual | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.13.2 Monte Carlo Distribution
Simulation Results Summary: Daily R&D Savings from Trial Cost Reduction
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $114M |
| Mean (expected value) | $113M |
| Median (50th percentile) | $112M |
| Standard Deviation | $22M |
| 90% Confidence Interval | [$79.8M, $152M] |
The histogram shows the distribution of Daily R&D Savings from Trial Cost Reduction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.13.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Daily R&D Savings from Trial Cost Reduction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.14 ROI from Decentralized Framework for Drug Assessment R&D Savings Only
Value: 451 ratio
ROI from Decentralized Framework for Drug Assessment R&D savings only (10-year NPV, most conservative estimate)
Inputs:
- Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $41.5B
- Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
- Standard Discount Rate for NPV Analysis: 3%
- Decentralized Framework for Drug Assessment Total NPV Upfront Costs 🔢: $270M
\[ ROI_{RD} = \frac{\$249.3B}{\$0.54B} \approx 463 \]
Methodology: ../appendix/dfda-cost-benefit-analysis#roi-simple
✓ High confidence
2.14.1 Sensitivity Analysis
Sensitivity Indices for ROI from Decentralized Framework for Drug Assessment R&D Savings Only
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA Annual OPEX | -5.7813 | Strong driver |
| dFDA NPV Upfront Cost Total | 4.3602 | Strong driver |
| dFDA R&D Gross Savings Annual | 1.3190 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.14.2 Monte Carlo Distribution
Simulation Results Summary: ROI from Decentralized Framework for Drug Assessment R&D Savings Only
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 451 |
| Mean (expected value) | 456 |
| Median (50th percentile) | 455 |
| Standard Deviation | 65.3 |
| 90% Confidence Interval | [351, 566] |
The histogram shows the distribution of ROI from Decentralized Framework for Drug Assessment R&D Savings Only across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.14.3 Exceedance Probability
This exceedance probability chart shows the likelihood that ROI from Decentralized Framework for Drug Assessment R&D Savings Only will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.15 Decentralized Framework for Drug Assessment Simple ROI Without NPV Adjustment
Value: 1.04k ratio
Simple ROI without NPV adjustment (gross savings / annual opex)
Inputs:
- Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $41.5B
- Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
\[ ROI_{DFDA} = \frac{Benefit_{gross,ann}}{Cost_{DFDA,ann}} = \frac{\$41.50B}{\$40.0M} = 1{,}038 \]
Methodology: ../appendix/dfda-cost-benefit-analysis#roi-simple
✓ High confidence
2.15.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Simple ROI Without NPV Adjustment
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA Annual OPEX | -1.4068 | Strong driver |
| dFDA R&D Gross Savings Annual | 1.3295 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.15.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Simple ROI Without NPV Adjustment
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 1.04k |
| Mean (expected value) | 1.05k |
| Median (50th percentile) | 1.05k |
| Standard Deviation | 148 |
| 90% Confidence Interval | [809, 1.30k] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Simple ROI Without NPV Adjustment across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.15.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Simple ROI Without NPV Adjustment will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.16 Decentralized Framework for Drug Assessment Maximum Trials per Year
Value: 75.4k trials/year
Maximum trials per year possible with trial capacity multiplier
Inputs:
- Current Global Clinical Trials per Year 📊: 3.30k trials/year (95% CI: 2.64k trials/year - 3.96k trials/year)
- Trial Capacity Multiplier 🔢: 22.8 ratio
\[ Capacity_{DFDA} = Trials_{curr} \times Multiplier = 3{,}300 \times 22.85 = 75{,}392 \]
✓ High confidence
2.16.1 Sensitivity Analysis
Sensitivity Indices for Decentralized Framework for Drug Assessment Maximum Trials per Year
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Trial Capacity Multiplier | 0.8750 | Strong driver |
| Current Trials Per Year | -0.1229 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.16.2 Monte Carlo Distribution
Simulation Results Summary: Decentralized Framework for Drug Assessment Maximum Trials per Year
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 75.4k |
| Mean (expected value) | 76.8k |
| Median (50th percentile) | 76.3k |
| Standard Deviation | 8.26k |
| 90% Confidence Interval | [62.4k, 93.6k] |
The histogram shows the distribution of Decentralized Framework for Drug Assessment Maximum Trials per Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.16.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Maximum Trials per Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.17 Patients Fundable Annually
Value: 43.4M patients/year
Number of patients fundable annually at RECOVERY trial cost
Inputs:
- Annual Clinical Trial Patient Subsidies 🔢: $21.7B
- Recovery Trial Cost per Patient 📊: $500 (95% CI: $350 - $700)
\[ Fundable_{ann} = \frac{Treasury_{ann}}{Cost} = \frac{\$21.70B}{\$500} = 43.4M \]
Methodology: ../economics/economics#funding-allocation
✓ High confidence
2.17.1 Sensitivity Analysis
Sensitivity Indices for Patients Fundable Annually
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Recovery Trial Cost Per Patient | -1.3662 | Strong driver |
| DIH Treasury Trial Subsidies Annual | 0.4126 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.17.2 Monte Carlo Distribution
Simulation Results Summary: Patients Fundable Annually
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 43.4M |
| Mean (expected value) | 44.1M |
| Median (50th percentile) | 43.9M |
| Standard Deviation | 4.52M |
| 90% Confidence Interval | [36.2M, 53.4M] |
The histogram shows the distribution of Patients Fundable Annually across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.17.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Patients Fundable Annually will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.18 Annual Funding for Pragmatic Clinical Trials
Value: $21.7B
Annual funding for pragmatic clinical trials (treaty funding minus VICTORY Incentive Alignment Bond payouts and IAB political incentive mechanism)
Inputs:
- Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2B
- Annual VICTORY Incentive Alignment Bond Payout: $2.72B
- Annual IAB Political Incentive Funding 🔢: $2.72B
\[ ResearchFunding = \$27.18B - \$2.718B - \$2.718B = \$21.744B \]
✓ High confidence
2.18.1 Sensitivity Analysis
Sensitivity Indices for Annual Funding for Pragmatic Clinical Trials
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Victory Bond Annual Payout | 0.3333 | Moderate driver |
| Iab Political Incentive Funding Annual | 0.3333 | Moderate driver |
| Treaty Annual Funding | 0.3333 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.18.2 Monte Carlo Distribution
Simulation Results Summary: Annual Funding for Pragmatic Clinical Trials
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $21.7B |
| Mean (expected value) | $21.7B |
| Median (50th percentile) | $21.6B |
| Standard Deviation | $1.55B |
| 90% Confidence Interval | [$19.6B, $23.9B] |
The histogram shows the distribution of Annual Funding for Pragmatic Clinical Trials across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.18.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Funding for Pragmatic Clinical Trials will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.19 Annual Clinical Trial Patient Subsidies
Value: $21.7B
Annual clinical trial patient subsidies (all medical research funds after Decentralized Framework for Drug Assessment operations)
Inputs:
- Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
- Annual Funding for Pragmatic Clinical Trials 🔢: $21.7B
\[ TrialSubsidies = \$24.462B - \$0.04B = \$24.422B \]
Methodology: ../economics/economics#funding-allocation
✓ High confidence
2.19.1 Sensitivity Analysis
Sensitivity Indices for Annual Clinical Trial Patient Subsidies
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| DIH Treasury To Medical Research Annual | 1.0051 | Strong driver |
| dFDA Annual OPEX | -0.0053 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.19.2 Monte Carlo Distribution
Simulation Results Summary: Annual Clinical Trial Patient Subsidies
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $21.7B |
| Mean (expected value) | $21.6B |
| Median (50th percentile) | $21.6B |
| Standard Deviation | $1.55B |
| 90% Confidence Interval | [$19.5B, $23.9B] |
The histogram shows the distribution of Annual Clinical Trial Patient Subsidies across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.19.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Clinical Trial Patient Subsidies will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.20 Total DALYs Lost from Disease Eradication Delay
Value: 7.94B DALYs
Total Disability-Adjusted Life Years lost from disease eradication delay (PRIMARY estimate)
Inputs:
- Years of Life Lost from Disease Eradication Delay 🔢: 7.07B years
- Years Lived with Disability During Disease Eradication Delay 🔢: 873M years
\[ DALY_{total} = 7.03B \text{ (YLL)} + 0.87B \text{ (YLD)} = 7.90B \]
Methodology: ../appendix/regulatory-mortality-analysis#daly-calculation
~ Medium confidence
2.20.1 Sensitivity Analysis
Sensitivity Indices for Total DALYs Lost from Disease Eradication Delay
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Disease Eradication Delay Yll | 0.5888 | Strong driver |
| Disease Eradication Delay Yld | 0.4270 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.20.2 Monte Carlo Distribution
Simulation Results Summary: Total DALYs Lost from Disease Eradication Delay
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 7.94B |
| Mean (expected value) | 8.01B |
| Median (50th percentile) | 7.91B |
| Standard Deviation | 1.33B |
| 90% Confidence Interval | [5.95B, 10.4B] |
The histogram shows the distribution of Total DALYs Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.20.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total DALYs Lost from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.21 Total Deaths from Disease Eradication Delay
Value: 416M deaths
Total eventually avoidable deaths from delaying disease eradication by 8.2 years (PRIMARY estimate, conservative). Excludes fundamentally unavoidable deaths (primarily accidents ~7.9%).
Inputs:
- Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 8.2 years (SE: ±1 years)
- Global Daily Deaths from Disease and Aging 📊: 150k deaths/day (SE: ±7.50k deaths/day)
\[ D_{total} = 54.75M \text{ (annual)} \times 8.2 \text{ (lag)} \times 92.1\% \text{ (avoidable)} = 413.4M \]
Methodology: ../appendix/regulatory-mortality-analysis#disease-eradication-delay
~ Medium confidence
2.21.1 Sensitivity Analysis
Sensitivity Indices for Total Deaths from Disease Eradication Delay
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Efficacy Lag Years | 0.9194 | Strong driver |
| Global Disease Deaths Daily | 0.0796 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.21.2 Monte Carlo Distribution
Simulation Results Summary: Total Deaths from Disease Eradication Delay
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 416M |
| Mean (expected value) | 418M |
| Median (50th percentile) | 415M |
| Standard Deviation | 71.2M |
| 90% Confidence Interval | [303M, 540M] |
The histogram shows the distribution of Total Deaths from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.21.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Deaths from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.22 Total Economic Loss from Disease Eradication Delay
Value: $1.19 quadrillion
Total economic loss from delaying disease eradication by 8.2 years (PRIMARY estimate, 2024 USD). Values global DALYs at standardized US/International normative rate ($150k) rather than local ability-to-pay, representing the full human capital loss.
Inputs:
- Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
- Standard Economic Value per QALY 📊: $150K (SE: ±$30K)
\[ Loss = 7.90B \times \$150k = \$1.185\text{ quadrillion} \]
Methodology: ../appendix/regulatory-mortality-analysis#economic-valuation
~ Medium confidence
2.22.1 Sensitivity Analysis
Sensitivity Indices for Total Economic Loss from Disease Eradication Delay
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Disease Eradication Delay DALYs | 0.8437 | Strong driver |
| Standard Economic QALY Value Usd | 0.1555 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.22.2 Monte Carlo Distribution
Simulation Results Summary: Total Economic Loss from Disease Eradication Delay
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $1.19 quadrillion |
| Mean (expected value) | $1.24 quadrillion |
| Median (50th percentile) | $1.18 quadrillion |
| Standard Deviation | $426T |
| 90% Confidence Interval | [$595T, $2.07 quadrillion] |
The histogram shows the distribution of Total Economic Loss from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.22.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Economic Loss from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.23 Years Lived with Disability During Disease Eradication Delay
Value: 873M years
Years Lived with Disability during disease eradication delay (PRIMARY estimate)
Inputs:
- Total Deaths from Disease Eradication Delay 🔢: 416M deaths
- Pre-Death Suffering Period During Post-Safety Efficacy Delay 📊: 6 years (95% CI: 4 years - 9 years)
- Disability Weight for Untreated Chronic Conditions 📊: 0.35 weight (SE: ±0.07 weight)
\[ Delay_{dis} = Deaths_{total} \times Deaths \times Chronic = 415.9M \times 6 \times 0.35 = 873.3M \]
Methodology: ../appendix/regulatory-mortality-analysis#daly-calculation
~ Medium confidence
2.23.1 Sensitivity Analysis
Sensitivity Indices for Years Lived with Disability During Disease Eradication Delay
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Regulatory Delay Suffering Period Years | 2.9647 | Strong driver |
| Disease Eradication Delay Deaths Total | -2.7522 | Strong driver |
| Chronic Disease Disability Weight | 0.7729 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.23.2 Monte Carlo Distribution
Simulation Results Summary: Years Lived with Disability During Disease Eradication Delay
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 873M |
| Mean (expected value) | 972M |
| Median (50th percentile) | 848M |
| Standard Deviation | 568M |
| 90% Confidence Interval | [291M, 2.08B] |
The histogram shows the distribution of Years Lived with Disability During Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.23.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Years Lived with Disability During Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.24 Years of Life Lost from Disease Eradication Delay
Value: 7.07B years
Years of Life Lost from disease eradication delay deaths (PRIMARY estimate)
Inputs:
- Total Deaths from Disease Eradication Delay 🔢: 416M deaths
- Global Life Expectancy (2024) 📊: 79 years (SE: ±2 years)
- Mean Age of Preventable Death from Post-Safety Efficacy Delay 📊: 62 years (SE: ±3 years)
\[ YLL = 413.4M \times 17 \text{ (years lost)} = 7.03B \]
Methodology: ../appendix/regulatory-mortality-analysis#daly-calculation
~ Medium confidence
2.24.1 Sensitivity Analysis
Sensitivity Indices for Years of Life Lost from Disease Eradication Delay
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Life Expectancy 2024 | 3.6252 | Strong driver |
| Regulatory Delay Mean Age Of Death | -2.7624 | Strong driver |
| Disease Eradication Delay Deaths Total | 0.1343 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.24.2 Monte Carlo Distribution
Simulation Results Summary: Years of Life Lost from Disease Eradication Delay
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 7.07B |
| Mean (expected value) | 7.04B |
| Median (50th percentile) | 7.06B |
| Standard Deviation | 783M |
| 90% Confidence Interval | [5.66B, 8.30B] |
The histogram shows the distribution of Years of Life Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.24.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Years of Life Lost from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.25 Total Deaths from Disease Eradication + Innovation Acceleration
Value: 898M deaths
Total deaths from disease eradication delay plus innovation acceleration (OPTIMISTIC UPPER BOUND). Represents additional deaths avoided beyond lag elimination through innovation cascade effects: faster development cycles, lower barriers enabling more drugs, earlier phase starts. The 2× multiplier is supported by research showing 50% timeline reductions achievable (Nature 2023) and adaptive trials generating millions of additional life-years (Woods et al. 2024). Based on (150K daily × 365 × 2) × 8.2 years.
Inputs:
- Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 8.2 years (SE: ±1 years)
- Global Daily Deaths from Disease and Aging 📊: 150k deaths/day (SE: ±7.50k deaths/day)
\[ D_{total} = (54.75M \times 2) \times 8.2 = 898M \]
Methodology: ../references#pharmaceutical-innovation-acceleration-economics
? Low confidence
2.25.1 Sensitivity Analysis
Sensitivity Indices for Total Deaths from Disease Eradication + Innovation Acceleration
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Efficacy Lag Years | 0.9194 | Strong driver |
| Global Disease Deaths Daily | 0.0796 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.25.2 Monte Carlo Distribution
Simulation Results Summary: Total Deaths from Disease Eradication + Innovation Acceleration
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 898M |
| Mean (expected value) | 902M |
| Median (50th percentile) | 896M |
| Standard Deviation | 154M |
| 90% Confidence Interval | [655M, 1.17B] |
The histogram shows the distribution of Total Deaths from Disease Eradication + Innovation Acceleration across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.25.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Deaths from Disease Eradication + Innovation Acceleration will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.26 Total Economic Loss from Disease Eradication + Innovation Acceleration
Value: $2.38 quadrillion
Total economic loss from disease eradication delay plus innovation acceleration (OPTIMISTIC UPPER BOUND). The 2× multiplier represents combined timeline and volume effects from eliminating Phase 2-4 cost barriers. Research shows: (1) Timeline acceleration of 50% achievable through AI/tech (Nature 2023), (2) Adaptive trials can reduce costs $2.6B→$2.2B, generating 3.5M additional life-years (Woods et al. 2024, Health Economics), (3) Cost barrier elimination enables more drugs to reach viability. The 2× factor conservatively represents either 2× timeline acceleration OR 1.5× timeline × 1.33× volume. Dynamic efficiency framework suggests optimal manufacturer value share ~20% maximizes long-term population health (Woods 2024).
Inputs:
- Total Economic Loss from Disease Eradication Delay 🔢: $1.19 quadrillion
\[ Loss_{total} = \$1,286T \times 2 = \$2,572T \]
Methodology: ../references#pharmaceutical-innovation-acceleration-economics
? Low confidence
2.26.1 Sensitivity Analysis
Sensitivity Indices for Total Economic Loss from Disease Eradication + Innovation Acceleration
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Disease Eradication Delay Economic Loss | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.26.2 Monte Carlo Distribution
Simulation Results Summary: Total Economic Loss from Disease Eradication + Innovation Acceleration
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $2.38 quadrillion |
| Mean (expected value) | $2.47 quadrillion |
| Median (50th percentile) | $2.37 quadrillion |
| Standard Deviation | $853T |
| 90% Confidence Interval | [$1.19 quadrillion, $4.14 quadrillion] |
The histogram shows the distribution of Total Economic Loss from Disease Eradication + Innovation Acceleration across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.26.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Economic Loss from Disease Eradication + Innovation Acceleration will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.27 Coverage Factor of Treaty Funding vs Decentralized Framework for Drug Assessment OPEX
Value: 680 ratio
Coverage factor of treaty funding vs Decentralized Framework for Drug Assessment opex (sustainability margin)
Inputs:
- Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
- Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2B
\[ Coverage = \$27.18B / \$0.04B = 679x \]
Methodology: ../strategy/roadmap#sustainability
✓ High confidence
2.27.1 Sensitivity Analysis
Sensitivity Indices for Coverage Factor of Treaty Funding vs Decentralized Framework for Drug Assessment OPEX
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| dFDA Annual OPEX | -1.0859 | Strong driver |
| Treaty Annual Funding | 0.1213 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.27.2 Monte Carlo Distribution
Simulation Results Summary: Coverage Factor of Treaty Funding vs Decentralized Framework for Drug Assessment OPEX
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 680 |
| Mean (expected value) | 699 |
| Median (50th percentile) | 693 |
| Standard Deviation | 95.2 |
| 90% Confidence Interval | [538, 895] |
The histogram shows the distribution of Coverage Factor of Treaty Funding vs Decentralized Framework for Drug Assessment OPEX across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.27.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Coverage Factor of Treaty Funding vs Decentralized Framework for Drug Assessment OPEX will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.28 Drug Cost Increase: 1980s to Current
Value: 13.4 ratio
Drug development cost increase from 1980s to current ($194M → $2.6B = 13.4x)
Inputs:
- Drug Development Cost (1980s) 📊: $194M
- Pharma Drug Development Cost (Current System) 📊: $2.60B (95% CI: $1.50B - $4B)
\[ Multiplier_{curr} = \frac{Cost_{curr}}{Cost_{80s}} = \frac{\$2.60B}{\$194.0M} = 13.4 \]
Methodology: Think by Numbers (1962) - Pre-1962 drug development costs and timeline
✓ High confidence
2.28.1 Sensitivity Analysis
Sensitivity Indices for Drug Cost Increase: 1980s to Current
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Pharma Drug Development Cost Current | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.28.2 Monte Carlo Distribution
Simulation Results Summary: Drug Cost Increase: 1980s to Current
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 13.4 |
| Mean (expected value) | 13.4 |
| Median (50th percentile) | 13.1 |
| Standard Deviation | 2.54 |
| 90% Confidence Interval | [9.57, 18] |
The histogram shows the distribution of Drug Cost Increase: 1980s to Current across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.28.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Drug Cost Increase: 1980s to Current will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.29 Drug Cost Increase: Pre-1962 to Current
Value: 52 ratio
Drug development cost increase from pre-1962 to current ($50M → $2.6B = 52x)
Inputs:
- Pharma Drug Development Cost (Current System) 📊: $2.60B (95% CI: $1.50B - $4B)
- Pre-1962 Drug Development Cost 📊: $50M (95% CI: $10M - $50M)
\[ Multiplier_{curr} = \frac{Cost_{curr}}{Cost_{pre62}} = \frac{\$2.60B}{\$50.0M} = 52 \]
Methodology: Think by Numbers (1962) - Pre-1962 drug development costs and timeline
~ Medium confidence
2.29.1 Sensitivity Analysis
Sensitivity Indices for Drug Cost Increase: Pre-1962 to Current
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Pharma Drug Development Cost Current | 1.6305 | Strong driver |
| Pre 1962 Drug Development Cost | -0.9380 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.29.2 Monte Carlo Distribution
Simulation Results Summary: Drug Cost Increase: Pre-1962 to Current
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 52 |
| Mean (expected value) | 56.2 |
| Median (50th percentile) | 53 |
| Standard Deviation | 6.07 |
| 90% Confidence Interval | [52.1, 69.8] |
The histogram shows the distribution of Drug Cost Increase: Pre-1962 to Current across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.29.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Drug Cost Increase: Pre-1962 to Current will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.30 Possible Drug-Disease Combinations
Value: 9.50M combinations
Total possible drug-disease combinations using existing safe compounds
Inputs:
- Safe Compounds Available for Testing: 9.50k compounds (95% CI: 7.00k compounds - 12.0k compounds)
- Trial-Relevant Diseases: 1.00k diseases (95% CI: 800 diseases - 1.20k diseases)
\[ N_{combinations} = N_{compounds} \times N_{diseases} = 9{,}500 \times 1{,}000 = 9{,}500{,}000 \]
Methodology: ../problem/untapped-therapeutic-frontier
✓ High confidence
2.30.1 Sensitivity Analysis
2.31 Therapeutic Frontier Exploration Ratio
Value: 0.342%
Fraction of possible drug-disease space actually tested (<1%)
Inputs:
- Tested Drug-Disease Relationships: 32.5k relationships (95% CI: 15.0k relationships - 50.0k relationships)
- Possible Drug-Disease Combinations 🔢: 9.50M combinations
\[ \text{Exploration Ratio} = \frac{N_{tested}}{N_{possible}} = \frac{32{,}500}{9{,}500{,}000} = 0.342\% \]
Methodology: ../problem/untapped-therapeutic-frontier
✓ High confidence
2.31.1 Sensitivity Analysis
Sensitivity Indices for Therapeutic Frontier Exploration Ratio
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Tested Relationships Estimate | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.31.2 Monte Carlo Distribution
Simulation Results Summary: Therapeutic Frontier Exploration Ratio
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 0.342% |
| Mean (expected value) | 0.339% |
| Median (50th percentile) | 0.329% |
| Standard Deviation | 0.0868% |
| 90% Confidence Interval | [0.21%, 0.514%] |
The histogram shows the distribution of Therapeutic Frontier Exploration Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.31.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Therapeutic Frontier Exploration Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.32 FDA to Oxford RECOVERY Trial Time Multiplier
Value: 36.4 ratio
FDA approval timeline vs Oxford RECOVERY trial (9.1 years ÷ 3 months = 36x slower)
Inputs:
- FDA Phase 1 to Approval Timeline 📊: 9.1 years (95% CI: 6 years - 12 years)
- Oxford RECOVERY Trial Duration 📊: 3 months
\[ \frac{9.1 \text{ years} \times 12 \text{ months/year}}{3 \text{ months}} = 36.4 \]
Methodology: Manhattan Institute - RECOVERY trial 82× cost reduction
✓ High confidence
2.32.1 Sensitivity Analysis
Sensitivity Indices for FDA to Oxford RECOVERY Trial Time Multiplier
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| FDA Phase 1 To Approval Years | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.32.2 Monte Carlo Distribution
Simulation Results Summary: FDA to Oxford RECOVERY Trial Time Multiplier
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 36.4 |
| Mean (expected value) | 36.2 |
| Median (50th percentile) | 35.9 |
| Standard Deviation | 6.99 |
| 90% Confidence Interval | [24.3, 48] |
The histogram shows the distribution of FDA to Oxford RECOVERY Trial Time Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.32.3 Exceedance Probability
This exceedance probability chart shows the likelihood that FDA to Oxford RECOVERY Trial Time Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.33 Total Annual Conflict Deaths Globally
Value: 245k deaths/year
Total annual conflict deaths globally (sum of combat, terror, state violence)
Inputs:
- Annual Deaths from Active Combat Worldwide 📊: 234k deaths/year (95% CI: 180k deaths/year - 300k deaths/year)
- Annual Deaths from State Violence 📊: 2.70k deaths/year (95% CI: 1.50k deaths/year - 5.00k deaths/year)
- Annual Deaths from Terror Attacks Globally 📊: 8.30k deaths/year (95% CI: 6.00k deaths/year - 12.0k deaths/year)
\[ Deaths_{total} = 233,600 \text{ (combat)} + 8,300 \text{ (terror)} + 2,700 \text{ (state)} = 244,600 \]
Methodology: ../problem/cost-of-war#death-accounting
✓ High confidence
2.33.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Conflict Deaths Globally
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Conflict Deaths Active Combat | 0.9276 | Strong driver |
| Global Annual Conflict Deaths Terror Attacks | 0.0461 | Minimal effect |
| Global Annual Conflict Deaths State Violence | 0.0266 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.33.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Conflict Deaths Globally
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 245k |
| Mean (expected value) | 244k |
| Median (50th percentile) | 242k |
| Standard Deviation | 31.5k |
| 90% Confidence Interval | [194k, 302k] |
The histogram shows the distribution of Total Annual Conflict Deaths Globally across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.33.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Conflict Deaths Globally will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.34 Annual Cost of Combat Deaths
Value: $2.34T
Annual cost of combat deaths (deaths × VSL)
Inputs:
- Annual Deaths from Active Combat Worldwide 📊: 234k deaths/year (95% CI: 180k deaths/year - 300k deaths/year)
- Value of Statistical Life 📊: $10M (95% CI: $5M - $15M)
\[ Cost_{human,ann} = Deaths_{combat,ann} \times Value = 233{,}600 \times \$10.0M = \$2.34T \]
Methodology: ../problem/cost-of-war#human-cost
✓ High confidence
2.34.1 Sensitivity Analysis
Sensitivity Indices for Annual Cost of Combat Deaths
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Value Of Statistical Life | 0.9096 | Strong driver |
| Global Annual Conflict Deaths Active Combat | 0.4115 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.34.2 Monte Carlo Distribution
Simulation Results Summary: Annual Cost of Combat Deaths
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $2.34T |
| Mean (expected value) | $2.31T |
| Median (50th percentile) | $2.24T |
| Standard Deviation | $703B |
| 90% Confidence Interval | [$1.25T, $3.57T] |
The histogram shows the distribution of Annual Cost of Combat Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.34.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Cost of Combat Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.35 Annual Cost of State Violence Deaths
Value: $27B
Annual cost of state violence deaths (deaths × VSL)
Inputs:
- Annual Deaths from State Violence 📊: 2.70k deaths/year (95% CI: 1.50k deaths/year - 5.00k deaths/year)
- Value of Statistical Life 📊: $10M (95% CI: $5M - $15M)
\[ Cost_{human,ann} = Deaths_{ann} \times Value = 2{,}700 \times \$10.0M = \$27.00B \]
Methodology: ../problem/cost-of-war#human-cost
✓ High confidence
2.35.1 Sensitivity Analysis
Sensitivity Indices for Annual Cost of State Violence Deaths
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Conflict Deaths State Violence | 0.7358 | Strong driver |
| Value Of Statistical Life | 0.6553 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.35.2 Monte Carlo Distribution
Simulation Results Summary: Annual Cost of State Violence Deaths
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $27B |
| Mean (expected value) | $26.6B |
| Median (50th percentile) | $24.5B |
| Standard Deviation | $11.3B |
| 90% Confidence Interval | [$12B, $48.4B] |
The histogram shows the distribution of Annual Cost of State Violence Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.35.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Cost of State Violence Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.36 Annual Cost of Terror Deaths
Value: $83B
Annual cost of terror deaths (deaths × VSL)
Inputs:
- Annual Deaths from Terror Attacks Globally 📊: 8.30k deaths/year (95% CI: 6.00k deaths/year - 12.0k deaths/year)
- Value of Statistical Life 📊: $10M (95% CI: $5M - $15M)
\[ Cost_{human,ann} = Deaths_{terror,ann} \times Value = 8{,}300 \times \$10.0M = \$83.00B \]
Methodology: ../problem/cost-of-war#human-cost
✓ High confidence
2.36.1 Sensitivity Analysis
Sensitivity Indices for Annual Cost of Terror Deaths
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Value Of Statistical Life | 0.8410 | Strong driver |
| Global Annual Conflict Deaths Terror Attacks | 0.5319 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.36.2 Monte Carlo Distribution
Simulation Results Summary: Annual Cost of Terror Deaths
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $83B |
| Mean (expected value) | $82.1B |
| Median (50th percentile) | $78.9B |
| Standard Deviation | $27B |
| 90% Confidence Interval | [$43.1B, $131B] |
The histogram shows the distribution of Annual Cost of Terror Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.36.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Cost of Terror Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.37 Total Annual Human Life Losses from Conflict
Value: $2.45T
Total annual human life losses from conflict (sum of combat, terror, state violence)
Inputs:
- Annual Cost of Combat Deaths 🔢: $2.34T
- Annual Cost of State Violence Deaths 🔢: $27B
- Annual Cost of Terror Deaths 🔢: $83B
\[ Cost_{human} = \$2,336B \text{ (combat)} + \$83B \text{ (terror)} + \$27B \text{ (state)} = \$2,446B \]
Methodology: ../problem/cost-of-war#human-cost
✓ High confidence
2.37.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Human Life Losses from Conflict
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Human Cost Active Combat | 0.9500 | Strong driver |
| Global Annual Human Cost Terror Attacks | 0.0365 | Minimal effect |
| Global Annual Human Cost State Violence | 0.0152 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.37.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Human Life Losses from Conflict
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $2.45T |
| Mean (expected value) | $2.42T |
| Median (50th percentile) | $2.35T |
| Standard Deviation | $740B |
| 90% Confidence Interval | [$1.31T, $3.75T] |
The histogram shows the distribution of Total Annual Human Life Losses from Conflict across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.37.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Human Life Losses from Conflict will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.38 Total Annual Infrastructure Destruction
Value: $1.88T
Total annual infrastructure destruction (sum of transportation, energy, communications, water, education, healthcare)
Inputs:
- Annual Infrastructure Damage to Communications from Conflict 📊: $298B (95% CI: $209B - $418B)
- Annual Infrastructure Damage to Education Facilities from Conflict 📊: $234B (95% CI: $164B - $328B)
- Annual Infrastructure Damage to Energy Systems from Conflict 📊: $422B (95% CI: $295B - $590B)
- Annual Infrastructure Damage to Healthcare Facilities from Conflict 📊: $166B (95% CI: $116B - $232B)
- Annual Infrastructure Damage to Transportation from Conflict 📊: $487B (95% CI: $340B - $680B)
- Annual Infrastructure Damage to Water Systems from Conflict 📊: $268B (95% CI: $187B - $375B)
\[ Infra_{damage} = \$487B \text{ (trans)} + \$422B \text{ (nrg)} + \$298B \text{ (comms)} + \$268B \text{ (water)} + \$235B \text{ (edu)} + \$166B \text{ (hlth)} = \$1,875B \]
Methodology: ../problem/cost-of-war#infrastructure-damage
✓ High confidence
2.38.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Infrastructure Destruction
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Infrastructure Damage Transportation Conflict | 0.2591 | Weak driver |
| Global Annual Infrastructure Damage Energy Conflict | 0.2248 | Weak driver |
| Global Annual Infrastructure Damage Communications Conflict | 0.1593 | Weak driver |
| Global Annual Infrastructure Damage Water Conflict | 0.1433 | Weak driver |
| Global Annual Infrastructure Damage Education Conflict | 0.1250 | Weak driver |
| Global Annual Infrastructure Damage Healthcare Conflict | 0.0884 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.38.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Infrastructure Destruction
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $1.88T |
| Mean (expected value) | $1.87T |
| Median (50th percentile) | $1.84T |
| Standard Deviation | $319B |
| 90% Confidence Interval | [$1.37T, $2.47T] |
The histogram shows the distribution of Total Annual Infrastructure Destruction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.38.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Infrastructure Destruction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.39 Total Annual Trade Disruption
Value: $616B
Total annual trade disruption (sum of shipping, supply chain, energy prices, currency instability)
Inputs:
- Annual Trade Disruption Costs from Currency Instability 📊: $57.4B (95% CI: $40B - $80B)
- Annual Trade Disruption Costs from Energy Price Volatility 📊: $125B (95% CI: $87B - $175B)
- Annual Trade Disruption Costs from Shipping Disruptions 📊: $247B (95% CI: $173B - $346B)
- Annual Trade Disruption Costs from Supply Chain Disruptions 📊: $187B (95% CI: $131B - $262B)
\[ Trade_{disruption} = \$247B \text{ (ship)} + \$187B \text{ (supply)} + \$125B \text{ (nrg)} + \$57B \text{ (curr)} = \$616B \]
Methodology: ../problem/cost-of-war#trade-disruption
✓ High confidence
2.39.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Trade Disruption
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Trade Disruption Shipping Conflict | 0.4005 | Moderate driver |
| Global Annual Trade Disruption Supply Chain Conflict | 0.3033 | Moderate driver |
| Global Annual Trade Disruption Energy Price Conflict | 0.2037 | Weak driver |
| Global Annual Trade Disruption Currency Conflict | 0.0926 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.39.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Trade Disruption
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $616B |
| Mean (expected value) | $614B |
| Median (50th percentile) | $605B |
| Standard Deviation | $105B |
| 90% Confidence Interval | [$450B, $812B] |
The histogram shows the distribution of Total Annual Trade Disruption across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.39.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Trade Disruption will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.40 Total Annual Direct War Costs
Value: $7.66T
Total annual direct war costs (military spending + infrastructure + human life + trade disruption)
Inputs:
- Total Annual Human Life Losses from Conflict 🔢: $2.45T
- Total Annual Infrastructure Destruction 🔢: $1.88T
- Total Annual Trade Disruption 🔢: $616B
- Global Military Spending in 2024 📊: $2.72T (95% CI: $2.45T - $2.99T)
\[ DirectCosts = \$2,718B \text{ (mil)} + \$1,875B \text{ (infra)} + \$2,446B \text{ (human)} + \$616B \text{ (trade)} = \$7,655B \]
Methodology: ../problem/cost-of-war#direct-costs
✓ High confidence
2.40.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Direct War Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Human Life Losses Conflict | 0.6510 | Strong driver |
| Global Annual Infrastructure Destruction Conflict | 0.2801 | Weak driver |
| Global Military Spending Annual 2024 | 0.1708 | Weak driver |
| Global Annual Trade Disruption Conflict | 0.0922 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.40.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Direct War Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $7.66T |
| Mean (expected value) | $7.61T |
| Median (50th percentile) | $7.51T |
| Standard Deviation | $1.14T |
| 90% Confidence Interval | [$5.89T, $9.64T] |
The histogram shows the distribution of Total Annual Direct War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.40.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Direct War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.41 Total Annual Indirect War Costs
Value: $3.70T
Total annual indirect war costs (opportunity cost + veterans + refugees + environment + mental health + lost productivity)
Inputs:
- Annual Environmental Damage and Restoration Costs from Conflict 📊: $100B (95% CI: $70B - $140B)
- Annual Lost Economic Growth from Military Spending Opportunity Cost 📊: $2.72T (95% CI: $1.90T - $3.80T)
- Annual Lost Productivity from Conflict Casualties 📊: $300B (95% CI: $210B - $420B)
- Annual PTSD and Mental Health Costs from Conflict 📊: $232B (95% CI: $162B - $325B)
- Annual Refugee Support Costs 📊: $150B (95% CI: $105B - $210B)
- Annual Veteran Healthcare Costs 📊: $200B (95% CI: $140B - $280B)
\[ IndirectCosts = \$2.7T \text{ (opp cost)} + \$200B \text{ (vet)} + \$150B \text{ (ref)} + \$100B \text{ (env)} + \$232B \text{ (ptsd)} + \$300B \text{ (hum cap)} = \$3.7T \]
Methodology: ../problem/cost-of-war#indirect-costs
✓ High confidence
2.41.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Indirect War Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Refugee Support Costs | 3.6537 | Strong driver |
| Global Annual Lost Human Capital Conflict | -2.0218 | Strong driver |
| Global Annual Environmental Damage Conflict | -1.4831 | Strong driver |
| Global Annual Lost Economic Growth Military Spending | 0.7342 | Strong driver |
| Global Annual Psychological Impact Costs Conflict | 0.0630 | Minimal effect |
| Global Annual Veteran Healthcare Costs | 0.0541 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.41.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Indirect War Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $3.70T |
| Mean (expected value) | $3.69T |
| Median (50th percentile) | $3.63T |
| Standard Deviation | $628B |
| 90% Confidence Interval | [$2.71T, $4.87T] |
The histogram shows the distribution of Total Annual Indirect War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.41.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Indirect War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.42 Total Annual Cost of War Worldwide
Value: $11.4T
Total annual cost of war worldwide (direct + indirect costs)
Inputs:
- Total Annual Direct War Costs 🔢: $7.66T
- Total Annual Indirect War Costs 🔢: $3.70T
\[ TotalWarCost = \$7,655B \text{ (direct)} + \$3,700B \text{ (indirect)} = \$11,355B \]
Methodology: ../problem/cost-of-war#total-cost
✓ High confidence
2.42.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Cost of War Worldwide
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual War Direct Costs Total | 0.6753 | Strong driver |
| Global Annual War Indirect Costs Total | 0.3731 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.42.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Cost of War Worldwide
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $11.4T |
| Mean (expected value) | $11.3T |
| Median (50th percentile) | $11.2T |
| Standard Deviation | $1.68T |
| 90% Confidence Interval | [$8.74T, $14.3T] |
The histogram shows the distribution of Total Annual Cost of War Worldwide across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.42.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Cost of War Worldwide will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.43 Cost per Life Saved by Medical Research
Value: $16.1K
Cost per life saved by medical research
Inputs:
- Annual Lives Saved by Medical Research Globally 📊: 4.20M lives/year (95% CI: 3.00M lives/year - 6.00M lives/year)
- Global Government Medical Research Spending 📊: $67.5B (95% CI: $54B - $81B)
\[ CostPerLifeSaved = \frac{\$67.5B \times 10^9}{4,200,000} \approx \$16,071 \]
Methodology: ../problem/cost-of-war#grotesque-mathematics
✓ High confidence
2.43.1 Sensitivity Analysis
Sensitivity Indices for Cost per Life Saved by Medical Research
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Lives Saved By Med Research | -0.5171 | Strong driver |
| Global Med Research Spending | -0.4779 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.43.2 Monte Carlo Distribution
Simulation Results Summary: Cost per Life Saved by Medical Research
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $16.1K |
| Mean (expected value) | $16.3K |
| Median (50th percentile) | $16.3K |
| Standard Deviation | $1.21K |
| 90% Confidence Interval | [$14.3K, $18.3K] |
The histogram shows the distribution of Cost per Life Saved by Medical Research across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.43.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Cost per Life Saved by Medical Research will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.44 Total Economic Burden of Disease Globally
Value: $109T
Total economic burden of disease globally (medical + productivity + mortality)
Inputs:
- Global Annual Direct Medical Costs of Disease 📊: $9.90T (95% CI: $7T - $14T)
- Global Annual Economic Value of Human Life Lost to Disease 📊: $94.2T (95% CI: $66T - $132T)
- Global Annual Productivity Loss from Disease 📊: $5T (95% CI: $3.50T - $7T)
\[ Burden_{ann} = Cost_{direct,ann} + Loss_{human,ann} + Loss_{ann} = \$9.90T + \$94.20T + \$5.00T = \$109.10T \]
✓ High confidence
2.44.1 Sensitivity Analysis
Sensitivity Indices for Total Economic Burden of Disease Globally
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Disease Human Life Value Loss Annual | 0.8628 | Strong driver |
| Global Disease Direct Medical Cost Annual | 0.0915 | Minimal effect |
| Global Disease Productivity Loss Annual | 0.0458 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.44.2 Monte Carlo Distribution
Simulation Results Summary: Total Economic Burden of Disease Globally
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $109T |
| Mean (expected value) | $109T |
| Median (50th percentile) | $107T |
| Standard Deviation | $18.6T |
| 90% Confidence Interval | [$79.8T, $144T] |
The histogram shows the distribution of Total Economic Burden of Disease Globally across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.44.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Economic Burden of Disease Globally will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.45 Annual Global Industry Spending on Clinical Trials
Value: $78.5B
Annual global industry spending on clinical trials (Total - Government)
Inputs:
- Annual Global Spending on Clinical Trials 📊: $83B (95% CI: $60B - $110B)
- Annual Global Government Spending on Clinical Trials 📊: $4.50B (95% CI: $3B - $6B)
\[ Trials_{ann} = Trials_{ann} - Trials_{ann} = \$83.00B - \$4.50B = \$78.50B \]
✓ High confidence
2.45.1 Sensitivity Analysis
Sensitivity Indices for Annual Global Industry Spending on Clinical Trials
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Clinical Trials Spending Annual | 1.0775 | Strong driver |
| Global Government Clinical Trials Spending Annual | -0.0781 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.45.2 Monte Carlo Distribution
Simulation Results Summary: Annual Global Industry Spending on Clinical Trials
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $78.5B |
| Mean (expected value) | $78.3B |
| Median (50th percentile) | $77.5B |
| Standard Deviation | $11.1B |
| 90% Confidence Interval | [$60.8B, $99B] |
The histogram shows the distribution of Annual Global Industry Spending on Clinical Trials across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.45.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Global Industry Spending on Clinical Trials will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.46 Per Capita Military Spending Globally
Value: $340
Per capita military spending globally
Inputs:
- Global Military Spending in 2024 📊: $2.72T (95% CI: $2.45T - $2.99T)
- Global Population in 2024 📊: 8.00B of people (95% CI: 7.80B of people - 8.20B of people)
\[ PerCapita_{military} = \$2,718B / 8.0B = \$339.75 \]
Methodology: ../problem/cost-of-war#per-capita
✓ High confidence
2.46.1 Sensitivity Analysis
Sensitivity Indices for Per Capita Military Spending Globally
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Military Spending Annual 2024 | 1.1928 | Strong driver |
| Global Population 2024 | -0.2007 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.46.2 Monte Carlo Distribution
Simulation Results Summary: Per Capita Military Spending Globally
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $340 |
| Mean (expected value) | $339 |
| Median (50th percentile) | $338 |
| Standard Deviation | $20 |
| 90% Confidence Interval | [$310, $368] |
The histogram shows the distribution of Per Capita Military Spending Globally across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.46.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Per Capita Military Spending Globally will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.47 Global Military Spending After 1% Treaty Reduction
Value: $2.69T
Global military spending after 1% treaty reduction
Inputs:
- Global Military Spending in 2024 📊: $2.72T (95% CI: $2.45T - $2.99T)
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ PostTreaty_{military} = \$2,718B \times 0.99 = \$2,690.82B \]
Methodology: ../strategy/treaty-adoption-strategy#post-treaty
✓ High confidence
2.47.1 Sensitivity Analysis
Sensitivity Indices for Global Military Spending After 1% Treaty Reduction
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Military Spending Annual 2024 | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.47.2 Monte Carlo Distribution
Simulation Results Summary: Global Military Spending After 1% Treaty Reduction
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $2.69T |
| Mean (expected value) | $2.68T |
| Median (50th percentile) | $2.67T |
| Standard Deviation | $192B |
| 90% Confidence Interval | [$2.42T, $2.96T] |
The histogram shows the distribution of Global Military Spending After 1% Treaty Reduction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.47.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Global Military Spending After 1% Treaty Reduction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.48 Total Annual Cost of War and Disease with All Externalities
Value: $129T
Total annual cost of war and disease with all externalities (direct + indirect costs for both)
Inputs:
- Total Annual Cost of War Worldwide 🔢: $11.4T
- Total Economic Burden of Disease Globally 🔢: $109T
- Annual Global Spending on Symptomatic Disease Treatment 📊: $8.20T (95% CI: $6.50T - $10T)
\[ Cost_{total} = Cost_{war,total} + Burden_{ann} + Spending_{sympt,ann} = \$11.36T + \$109.10T + \$8.20T = \$128.66T \]
Methodology: ../appendix/humanity-budget-overview
✓ High confidence
2.48.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Cost of War and Disease with All Externalities
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Disease Economic Burden Annual | 0.8855 | Strong driver |
| Global Annual War Total Cost | 0.0803 | Minimal effect |
| Global Symptomatic Disease Treatment Annual | 0.0406 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.48.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Cost of War and Disease with All Externalities
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $129T |
| Mean (expected value) | $128T |
| Median (50th percentile) | $126T |
| Standard Deviation | $21T |
| 90% Confidence Interval | [$95.5T, $168T] |
The histogram shows the distribution of Total Annual Cost of War and Disease with All Externalities across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.48.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Cost of War and Disease with All Externalities will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.49 Total Deaths from Historical Progress Delays
Value: 98.4M deaths
Total deaths from delaying existing drugs over 8.2-year efficacy lag (CONSERVATIVE FLOOR). One-time impact of eliminating Phase 2-4 testing delay for drugs already approved 1962-2024. Based on 12M deaths/year rate × 8.2 years.
Inputs:
- Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 8.2 years (SE: ±1 years)
\[ D_{total} = 12M \times 8.2 = 98.4M \]
Methodology: ../appendix/regulatory-mortality-analysis#historical-progress
✓ High confidence
2.49.1 Sensitivity Analysis
Sensitivity Indices for Total Deaths from Historical Progress Delays
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Efficacy Lag Years | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.49.2 Monte Carlo Distribution
Simulation Results Summary: Total Deaths from Historical Progress Delays
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 98.4M |
| Mean (expected value) | 98.3M |
| Median (50th percentile) | 98.2M |
| Standard Deviation | 11.9M |
| 90% Confidence Interval | [78.3M, 118M] |
The histogram shows the distribution of Total Deaths from Historical Progress Delays across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.49.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Deaths from Historical Progress Delays will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.50 Total Economic Loss from Historical Progress Delays
Value: $251T
Total economic loss from delaying existing drugs over 8.2-year efficacy lag (CONSERVATIVE FLOOR). One-time benefit of eliminating Phase 2-4 delay.
Inputs:
- Global Life Expectancy (2024) 📊: 79 years (SE: ±2 years)
- Total Deaths from Historical Progress Delays 🔢: 98.4M deaths
- Mean Age of Preventable Death from Post-Safety Efficacy Delay 📊: 62 years (SE: ±3 years)
- Standard Economic Value per QALY 📊: $150K (SE: ±$30K)
\[ Loss_{total} = 98.4M \times 17 \times \$150k = \$251T \]
Methodology: ../appendix/regulatory-mortality-analysis#historical-progress
✓ High confidence
2.50.1 Sensitivity Analysis
Sensitivity Indices for Total Economic Loss from Historical Progress Delays
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Regulatory Delay Mean Age Of Death | -1.3565 | Strong driver |
| Global Life Expectancy 2024 | 1.1667 | Strong driver |
| Standard Economic QALY Value Usd | 0.8086 | Strong driver |
| Historical Progress Deaths Total | 0.3807 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.50.2 Monte Carlo Distribution
Simulation Results Summary: Total Economic Loss from Historical Progress Delays
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $251T |
| Mean (expected value) | $251T |
| Median (50th percentile) | $250T |
| Standard Deviation | $60.7T |
| 90% Confidence Interval | [$146T, $361T] |
The histogram shows the distribution of Total Economic Loss from Historical Progress Delays across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.50.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Economic Loss from Historical Progress Delays will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.51 IAB Mechanism Benefit-Cost Ratio
Value: 207 ratio
Benefit-Cost Ratio of the IAB mechanism itself
Inputs:
- 1% treaty Basic Annual Benefits (Peace + R&D Savings) 🔢: $155B
- IAB Mechanism Annual Cost (High Estimate): $750M (95% CI: $160M - $750M)
\[ Cost = \frac{Benefit_{ann}}{Cost_{ann}} = \frac{\$155.05B}{\$750.0M} = 206.73 \]
Methodology: ../appendix/incentive-alignment-bonds-paper#welfare-analysis
✓ High confidence
2.51.1 Sensitivity Analysis
Sensitivity Indices for IAB Mechanism Benefit-Cost Ratio
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Peace Plus R&D Annual Benefits | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.51.2 Monte Carlo Distribution
Simulation Results Summary: IAB Mechanism Benefit-Cost Ratio
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 207 |
| Mean (expected value) | 206 |
| Median (50th percentile) | 203 |
| Standard Deviation | 30.7 |
| 90% Confidence Interval | [159, 261] |
The histogram shows the distribution of IAB Mechanism Benefit-Cost Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.51.3 Exceedance Probability
This exceedance probability chart shows the likelihood that IAB Mechanism Benefit-Cost Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.52 Annual IAB Political Incentive Funding
Value: $2.72B
Annual funding for IAB political incentive mechanism (independent expenditures supporting high-scoring politicians, post-office fellowship endowments, Public Good Score infrastructure)
Inputs:
- Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2B
- IAB Political Incentive Funding Percentage: 10%
\[ IABFunding = \$27.18B \times 0.10 = \$2.718B \]
✓ High confidence
2.52.1 Sensitivity Analysis
Sensitivity Indices for Annual IAB Political Incentive Funding
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Annual Funding | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.52.2 Monte Carlo Distribution
Simulation Results Summary: Annual IAB Political Incentive Funding
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $2.72B |
| Mean (expected value) | $2.71B |
| Median (50th percentile) | $2.70B |
| Standard Deviation | $194M |
| 90% Confidence Interval | [$2.45B, $2.99B] |
The histogram shows the distribution of Annual IAB Political Incentive Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.52.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual IAB Political Incentive Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.53 Ratio of Industry to Government Clinical Trials Spending
Value: 17.4 ratio
Ratio of Industry to Government spending on clinical trials (approx 90/10 split)
Inputs:
- Annual Global Spending on Clinical Trials 📊: $83B (95% CI: $60B - $110B)
- Annual Global Government Spending on Clinical Trials 📊: $4.50B (95% CI: $3B - $6B)
Formula: (TOTAL - GOVT) / GOVT
Methodology: Applied Clinical Trials - Industry vs. Government Clinical Trial Spending Split (90/10)
✓ High confidence
2.53.1 Sensitivity Analysis
Sensitivity Indices for Ratio of Industry to Government Clinical Trials Spending
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Government Clinical Trials Spending Annual | -3.1431 | Strong driver |
| Global Clinical Trials Spending Annual | 2.2107 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.53.2 Monte Carlo Distribution
Simulation Results Summary: Ratio of Industry to Government Clinical Trials Spending
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 17.4 |
| Mean (expected value) | 17.8 |
| Median (50th percentile) | 17.7 |
| Standard Deviation | 1.05 |
| 90% Confidence Interval | [16.2, 19.6] |
The histogram shows the distribution of Ratio of Industry to Government Clinical Trials Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.53.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Industry to Government Clinical Trials Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.54 Medical Research Spending as Percentage of Total Disease Burden
Value: 0.0525%
Medical research spending as percentage of total disease burden
Inputs:
- Global Government Medical Research Spending 📊: $67.5B (95% CI: $54B - $81B)
- Total Annual Cost of War and Disease with All Externalities 🔢: $129T
\[ \frac{\$67.5\text{B}}{\$128.6\text{T}} = 0.052\% \]
Methodology: ../economics/economics
✓ High confidence
2.54.1 Sensitivity Analysis
Sensitivity Indices for Medical Research Spending as Percentage of Total Disease Burden
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Med Research Spending | -0.5317 | Strong driver |
| Global Total Health And War Cost Annual | -0.4628 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.54.2 Monte Carlo Distribution
Simulation Results Summary: Medical Research Spending as Percentage of Total Disease Burden
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 0.0525% |
| Mean (expected value) | 0.0531% |
| Median (50th percentile) | 0.053% |
| Standard Deviation | 0.00345% |
| 90% Confidence Interval | [0.0473%, 0.059%] |
The histogram shows the distribution of Medical Research Spending as Percentage of Total Disease Burden across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.54.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Medical Research Spending as Percentage of Total Disease Burden will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.55 Ratio of Military to Government Clinical Trials Spending
Value: 604 ratio
Ratio of global military spending to government clinical trials spending
Inputs:
- Global Military Spending in 2024 📊: $2.72T (95% CI: $2.45T - $2.99T)
- Annual Global Government Spending on Clinical Trials 📊: $4.50B (95% CI: $3B - $6B)
\[ \text{Ratio} = \frac{\$2.7T}{\$4.5B} \approx 600\times \]
✓ High confidence
2.55.1 Sensitivity Analysis
Sensitivity Indices for Ratio of Military to Government Clinical Trials Spending
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Government Clinical Trials Spending Annual | -1.4345 | Strong driver |
| Global Military Spending Annual 2024 | 0.4717 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.55.2 Monte Carlo Distribution
Simulation Results Summary: Ratio of Military to Government Clinical Trials Spending
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 604 |
| Mean (expected value) | 624 |
| Median (50th percentile) | 617 |
| Standard Deviation | 81 |
| 90% Confidence Interval | [498, 804] |
The histogram shows the distribution of Ratio of Military to Government Clinical Trials Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.55.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Military to Government Clinical Trials Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.56 Ratio of Military Spending to Medical Research Spending
Value: 40.3 ratio
Ratio of military spending to medical research spending
Inputs:
- Global Government Medical Research Spending 📊: $67.5B (95% CI: $54B - $81B)
- Global Military Spending in 2024 📊: $2.72T (95% CI: $2.45T - $2.99T)
\[ Ratio = \frac{\$2,718B}{\$67.5B} \approx 40.3:1 \]
Methodology: ../problem/cost-of-war#misallocation
✓ High confidence
2.56.1 Sensitivity Analysis
Sensitivity Indices for Ratio of Military Spending to Medical Research Spending
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Med Research Spending | -2.8723 | Strong driver |
| Global Military Spending Annual 2024 | 2.1117 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.56.2 Monte Carlo Distribution
Simulation Results Summary: Ratio of Military Spending to Medical Research Spending
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 40.3 |
| Mean (expected value) | 40.3 |
| Median (50th percentile) | 40.3 |
| Standard Deviation | 1.38 |
| 90% Confidence Interval | [37.7, 43.2] |
The histogram shows the distribution of Ratio of Military Spending to Medical Research Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.56.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Military Spending to Medical Research Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.57 Misallocation Factor: Cost to Kill vs Cost to Save
Value: 2.89k ratio
Misallocation factor: cost to kill vs cost to save
Inputs:
- Total Annual Conflict Deaths Globally 🔢: 245k deaths/year
- Total Annual Cost of War Worldwide 🔢: $11.4T
- Cost per Life Saved by Medical Research 🔢: $16.1K
\[ Misallocation = \frac{\$46.4M}{\$16,071} \approx 2,889x \]
Methodology: ../problem/cost-of-war#grotesque-mathematics
✓ High confidence
2.57.1 Sensitivity Analysis
Sensitivity Indices for Misallocation Factor: Cost to Kill vs Cost to Save
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual War Total Cost | 1.4500 | Strong driver |
| Global Annual Conflict Deaths Total | -0.8878 | Strong driver |
| Global Cost Per Life Saved Med Research Annual | -0.3718 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.57.2 Monte Carlo Distribution
Simulation Results Summary: Misallocation Factor: Cost to Kill vs Cost to Save
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 2.89k |
| Mean (expected value) | 2.86k |
| Median (50th percentile) | 2.84k |
| Standard Deviation | 292 |
| 90% Confidence Interval | [2.42k, 3.38k] |
The histogram shows the distribution of Misallocation Factor: Cost to Kill vs Cost to Save across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.57.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Misallocation Factor: Cost to Kill vs Cost to Save will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.58 Annual Peace Dividend from 1% Reduction in Total War Costs
Value: $114B
Annual peace dividend from 1% reduction in total war costs
Inputs:
- Total Annual Cost of War Worldwide 🔢: $11.4T
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Cost_{soc,ann} = Cost_{war,total} \times Reduction_{treaty} = \$11.36T \times 1.0\% = \$113.55B \]
Methodology: ../appendix/peace-dividend-calculations
✓ High confidence
2.58.1 Sensitivity Analysis
Sensitivity Indices for Annual Peace Dividend from 1% Reduction in Total War Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual War Total Cost | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.58.2 Monte Carlo Distribution
Simulation Results Summary: Annual Peace Dividend from 1% Reduction in Total War Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $114B |
| Mean (expected value) | $113B |
| Median (50th percentile) | $112B |
| Standard Deviation | $16.8B |
| 90% Confidence Interval | [$87.4B, $143B] |
The histogram shows the distribution of Annual Peace Dividend from 1% Reduction in Total War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.58.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Peace Dividend from 1% Reduction in Total War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.59 Conflict Reduction Benefits from 1% Less Military Spending
Value: $86.4B
Conflict reduction benefits from 1% less military spending (lower confidence - assumes proportional relationship)
Inputs:
- Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $114B
- Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2B
\[ PeaceDividend_{conflict} = \$113.55B - \$27.18B = \$86.37B \]
Methodology: Direct Calculation
? Low confidence
2.59.1 Sensitivity Analysis
Sensitivity Indices for Conflict Reduction Benefits from 1% Less Military Spending
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Peace Dividend Annual Societal Benefit | 1.1124 | Strong driver |
| Treaty Annual Funding | -0.1283 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.59.2 Monte Carlo Distribution
Simulation Results Summary: Conflict Reduction Benefits from 1% Less Military Spending
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $86.4B |
| Mean (expected value) | $85.9B |
| Median (50th percentile) | $84.6B |
| Standard Deviation | $15.1B |
| 90% Confidence Interval | [$62.9B, $113B] |
The histogram shows the distribution of Conflict Reduction Benefits from 1% Less Military Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.59.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Conflict Reduction Benefits from 1% Less Military Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.60 Annual Savings from 1% Reduction in Direct War Costs
Value: $76.5B
Annual savings from 1% reduction in direct war costs
Inputs:
\[ Cost_{direct,peace} = Cost_{direct,total} \times Reduction_{treaty} = \$7.66T \times 1.0\% = \$76.55B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.60.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Direct War Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual War Direct Costs Total | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.60.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Direct War Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $76.5B |
| Mean (expected value) | $76.1B |
| Median (50th percentile) | $75.1B |
| Standard Deviation | $11.4B |
| 90% Confidence Interval | [$58.9B, $96.4B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Direct War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.60.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Direct War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.61 Annual Savings from 1% Reduction in Environmental Damage
Value: $1B
Annual savings from 1% reduction in environmental damage
Inputs:
- Annual Environmental Damage and Restoration Costs from Conflict 📊: $100B (95% CI: $70B - $140B)
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Savings_{env,peace} = Cost_{env,ann} \times Reduction_{treaty} = \$100.00B \times 1.0\% = \$1.00B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.61.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Environmental Damage
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Environmental Damage Conflict | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.61.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Environmental Damage
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $1B |
| Mean (expected value) | $997M |
| Median (50th percentile) | $982M |
| Standard Deviation | $170M |
| 90% Confidence Interval | [$732M, $1.32B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Environmental Damage across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.61.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Environmental Damage will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.62 Annual Savings from 1% Reduction in Human Casualties
Value: $24.5B
Annual savings from 1% reduction in human casualties
Inputs:
- Total Annual Human Life Losses from Conflict 🔢: $2.45T
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Savings_{human,peace} = Loss_{human,ann} \times Reduction_{treaty} = \$2.45T \times 1.0\% = \$24.46B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.62.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Human Casualties
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Human Life Losses Conflict | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.62.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Human Casualties
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $24.5B |
| Mean (expected value) | $24.2B |
| Median (50th percentile) | $23.5B |
| Standard Deviation | $7.40B |
| 90% Confidence Interval | [$13.1B, $37.5B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Human Casualties across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.62.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Human Casualties will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.63 Annual Savings from 1% Reduction in Indirect War Costs
Value: $37B
Annual savings from 1% reduction in indirect war costs
Inputs:
- Total Annual Indirect War Costs 🔢: $3.70T
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Cost_{indirect,peace} = Cost_{indirect,total} \times Reduction_{treaty} = \$3.70T \times 1.0\% = \$37.00B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.63.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Indirect War Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual War Indirect Costs Total | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.63.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Indirect War Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $37B |
| Mean (expected value) | $36.9B |
| Median (50th percentile) | $36.3B |
| Standard Deviation | $6.28B |
| 90% Confidence Interval | [$27.1B, $48.7B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Indirect War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.63.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Indirect War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.64 Annual Savings from 1% Reduction in Infrastructure Destruction
Value: $18.8B
Annual savings from 1% reduction in infrastructure destruction
Inputs:
- Total Annual Infrastructure Destruction 🔢: $1.88T
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Savings_{infra,peace} = Infrastructure_{global} \times Reduction_{treaty} = \$1.88T \times 1.0\% = \$18.75B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.64.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Infrastructure Destruction
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Infrastructure Destruction Conflict | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.64.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Infrastructure Destruction
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $18.8B |
| Mean (expected value) | $18.7B |
| Median (50th percentile) | $18.4B |
| Standard Deviation | $3.19B |
| 90% Confidence Interval | [$13.7B, $24.7B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Infrastructure Destruction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.64.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Infrastructure Destruction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.65 Annual Savings from 1% Reduction in Lost Economic Growth
Value: $27.2B
Annual savings from 1% reduction in lost economic growth
Inputs:
- Annual Lost Economic Growth from Military Spending Opportunity Cost 📊: $2.72T (95% CI: $1.90T - $3.80T)
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Savings_{lost_econ,peace} = Cost_{mil,ann} \times Reduction_{treaty} = \$2.72T \times 1.0\% = \$27.18B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.65.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Lost Economic Growth
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Lost Economic Growth Military Spending | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.65.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Lost Economic Growth
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $27.2B |
| Mean (expected value) | $27.1B |
| Median (50th percentile) | $26.7B |
| Standard Deviation | $4.61B |
| 90% Confidence Interval | [$19.9B, $35.8B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Lost Economic Growth across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.65.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Lost Economic Growth will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.66 Annual Savings from 1% Reduction in Lost Human Capital
Value: $3B
Annual savings from 1% reduction in lost human capital
Inputs:
- Annual Lost Productivity from Conflict Casualties 📊: $300B (95% CI: $210B - $420B)
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Savings_{human,peace} = Lost_{global} \times Reduction_{treaty} = \$300.00B \times 1.0\% = \$3.00B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.66.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Lost Human Capital
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Lost Human Capital Conflict | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.66.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Lost Human Capital
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $3B |
| Mean (expected value) | $2.99B |
| Median (50th percentile) | $2.95B |
| Standard Deviation | $510M |
| 90% Confidence Interval | [$2.20B, $3.95B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Lost Human Capital across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.66.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Lost Human Capital will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.67 Annual Savings from 1% Reduction in PTSD and Mental Health Costs
Value: $2.32B
Annual savings from 1% reduction in PTSD and mental health costs
Inputs:
- Annual PTSD and Mental Health Costs from Conflict 📊: $232B (95% CI: $162B - $325B)
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Cost_{PTSD,peace} = Cost_{ann} \times Reduction_{treaty} = \$232.00B \times 1.0\% = \$2.32B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.67.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in PTSD and Mental Health Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Psychological Impact Costs Conflict | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.67.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in PTSD and Mental Health Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $2.32B |
| Mean (expected value) | $2.31B |
| Median (50th percentile) | $2.28B |
| Standard Deviation | $396M |
| 90% Confidence Interval | [$1.70B, $3.06B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in PTSD and Mental Health Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.67.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in PTSD and Mental Health Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.68 Annual Savings from 1% Reduction in Refugee Support Costs
Value: $1.50B
Annual savings from 1% reduction in refugee support costs
Inputs:
- Annual Refugee Support Costs 📊: $150B (95% CI: $105B - $210B)
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Cost_{ref,peace} = Cost_{ref,ann} \times Reduction_{treaty} = \$150.00B \times 1.0\% = \$1.50B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.68.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Refugee Support Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Refugee Support Costs | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.68.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Refugee Support Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $1.50B |
| Mean (expected value) | $1.50B |
| Median (50th percentile) | $1.47B |
| Standard Deviation | $255M |
| 90% Confidence Interval | [$1.10B, $1.98B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Refugee Support Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.68.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Refugee Support Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.69 Annual Savings from 1% Reduction in Trade Disruption
Value: $6.16B
Annual savings from 1% reduction in trade disruption
Inputs:
\[ Savings_{trade,peace} = Disruption_{trade,ann} \times Reduction_{treaty} = \$616.00B \times 1.0\% = \$6.16B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.69.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Trade Disruption
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Trade Disruption Conflict | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.69.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Trade Disruption
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $6.16B |
| Mean (expected value) | $6.14B |
| Median (50th percentile) | $6.05B |
| Standard Deviation | $1.05B |
| 90% Confidence Interval | [$4.50B, $8.12B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Trade Disruption across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.69.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Trade Disruption will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.70 Annual Savings from 1% Reduction in Veteran Healthcare Costs
Value: $2B
Annual savings from 1% reduction in veteran healthcare costs
Inputs:
- Annual Veteran Healthcare Costs 📊: $200B (95% CI: $140B - $280B)
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Cost_{vet,peace} = Cost_{vet,ann} \times Reduction_{treaty} = \$200.10B \times 1.0\% = \$2.00B \]
Methodology: ../economics/peace-dividend
✓ High confidence
2.70.1 Sensitivity Analysis
Sensitivity Indices for Annual Savings from 1% Reduction in Veteran Healthcare Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Veteran Healthcare Costs | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.70.2 Monte Carlo Distribution
Simulation Results Summary: Annual Savings from 1% Reduction in Veteran Healthcare Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $2B |
| Mean (expected value) | $2B |
| Median (50th percentile) | $1.97B |
| Standard Deviation | $340M |
| 90% Confidence Interval | [$1.46B, $2.63B] |
The histogram shows the distribution of Annual Savings from 1% Reduction in Veteran Healthcare Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.70.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Savings from 1% Reduction in Veteran Healthcare Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.71 Personal Lifetime Wealth (Age 30, 1% Treaty)
Value: $508K
Personal lifetime wealth benefit for a 30-year-old with $50K income under 1% treaty. Life extension uncertainty (5-50 years) propagates through Monte Carlo to show full range of outcomes from conservative antibiotic precedent to optimistic aging reversal scenarios.
Inputs:
- Life Extension from Treaty Research Acceleration 📊: 20 years (95% CI: 5 years - 100 years)
- Trial Capacity Multiplier 🔢: 22.8 ratio
\[ \text{PLW} = \sum_{t=0}^{T + \Delta L} \frac{B_t}{(1+r)^t} \]
Methodology: ../appendix/disease-eradication-personal-lifetime-wealth-calculations
~ Medium confidence
2.71.1 Sensitivity Analysis
Sensitivity Indices for Personal Lifetime Wealth (Age 30, 1% Treaty)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Trial Capacity Multiplier | -0.5427 | Strong driver |
| Life Extension Years | 0.4976 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.71.2 Monte Carlo Distribution
Simulation Results Summary: Personal Lifetime Wealth (Age 30, 1% Treaty)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $508K |
| Mean (expected value) | $437K |
| Median (50th percentile) | $392K |
| Standard Deviation | $187K |
| 90% Confidence Interval | [$205K, $815K] |
The histogram shows the distribution of Personal Lifetime Wealth (Age 30, 1% Treaty) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.71.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Personal Lifetime Wealth (Age 30, 1% Treaty) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.72 US Per Capita Chronic Disease Cost
Value: $12.2K
US per capita chronic disease cost
Inputs:
- US Annual Chronic Disease Spending 📊: $4.10T (95% CI: $3.30T - $5T)
- US Population in 2024 📊: 335M people (95% CI: 330M people - 340M people)
\[ Cost_{percap,dis} = \frac{Spending_{chronic,ann}}{Population} = \frac{\$4.10T}{335M} = \$12.2K \]
✓ High confidence
2.72.1 Sensitivity Analysis
Sensitivity Indices for US Per Capita Chronic Disease Cost
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| US Chronic Disease Spending Annual | 0.9139 | Strong driver |
| US Population 2024 | 0.0862 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.72.2 Monte Carlo Distribution
Simulation Results Summary: US Per Capita Chronic Disease Cost
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $12.2K |
| Mean (expected value) | $12.2K |
| Median (50th percentile) | $12.2K |
| Standard Deviation | $1.15K |
| 90% Confidence Interval | [$10.3K, $14.3K] |
The histogram shows the distribution of US Per Capita Chronic Disease Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.72.3 Exceedance Probability
This exceedance probability chart shows the likelihood that US Per Capita Chronic Disease Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.73 US Per Capita Mental Health Cost
Value: $1.04K
US per capita mental health cost
Inputs:
- US Mental Health Costs 📊: $350B (95% CI: $260B - $450B)
- US Population in 2024 📊: 335M people (95% CI: 330M people - 340M people)
\[ Cost_{percap,health} = \frac{Cost_{mental,ann}}{Population} = \frac{\$350.00B}{335M} = \$1.0K \]
✓ High confidence
2.73.1 Sensitivity Analysis
Sensitivity Indices for US Per Capita Mental Health Cost
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| US Mental Health Cost Annual | 0.9281 | Strong driver |
| US Population 2024 | 0.0720 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.73.2 Monte Carlo Distribution
Simulation Results Summary: US Per Capita Mental Health Cost
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $1.04K |
| Mean (expected value) | $1.04K |
| Median (50th percentile) | $1.03K |
| Standard Deviation | $130 |
| 90% Confidence Interval | [$832, $1.28K] |
The histogram shows the distribution of US Per Capita Mental Health Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.73.3 Exceedance Probability
This exceedance probability chart shows the likelihood that US Per Capita Mental Health Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.74 Total Suffering Hours Eliminated
Value: 7.65T hours
Total hours of human suffering eliminated by 8.2-year disease eradication timeline shift (one-time benefit from YLD component, not annual recurring)
Inputs:
\[ Hours = 868M \text{ (YLD)} \times 8{,}760 \text{ (hrs/yr)} = 7.60T \]
Methodology: ../appendix/regulatory-mortality-analysis#daly-calculation
~ Medium confidence
2.74.1 Sensitivity Analysis
Sensitivity Indices for Total Suffering Hours Eliminated
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Disease Eradication Delay Yld | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.74.2 Monte Carlo Distribution
Simulation Results Summary: Total Suffering Hours Eliminated
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 7.65T |
| Mean (expected value) | 8.52T |
| Median (50th percentile) | 7.43T |
| Standard Deviation | 4.97T |
| 90% Confidence Interval | [2.55T, 18.2T] |
The histogram shows the distribution of Total Suffering Hours Eliminated across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.74.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Suffering Hours Eliminated will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.75 Thalidomide DALYs Per Event
Value: 41.8k DALYs
Total DALYs per US-scale thalidomide event (YLL + YLD)
Inputs:
- Thalidomide YLD Per Event 🔢: 13.0k years
- Thalidomide YLL Per Event 🔢: 28.8k years
\[ 28{,}800 + 12{,}960 = 41{,}760 \text{ DALYs} \]
~ Medium confidence
2.75.1 Sensitivity Analysis
Sensitivity Indices for Thalidomide DALYs Per Event
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Thalidomide Yll Per Event | 0.6302 | Strong driver |
| Thalidomide Yld Per Event | 0.3700 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.75.2 Monte Carlo Distribution
Simulation Results Summary: Thalidomide DALYs Per Event
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 41.8k |
| Mean (expected value) | 42.4k |
| Median (50th percentile) | 40.7k |
| Standard Deviation | 12.2k |
| 90% Confidence Interval | [24.8k, 67.0k] |
The histogram shows the distribution of Thalidomide DALYs Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.75.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide DALYs Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.76 Thalidomide Deaths Per Event
Value: 360 deaths
Deaths per US-scale thalidomide event
Inputs:
- Thalidomide Mortality Rate 📊: 40% (95% CI: 35% - 45%)
- Thalidomide US Cases Prevented 🔢: 900 cases
\[ 900 \text{ (cases)} \times 40\% \text{ (mortality)} = 360 \text{ deaths} \]
~ Medium confidence
2.76.1 Sensitivity Analysis
Sensitivity Indices for Thalidomide Deaths Per Event
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Thalidomide US Cases Prevented | 1.5048 | Strong driver |
| Thalidomide Mortality Rate | -0.5069 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.76.2 Monte Carlo Distribution
Simulation Results Summary: Thalidomide Deaths Per Event
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 360 |
| Mean (expected value) | 364 |
| Median (50th percentile) | 352 |
| Standard Deviation | 95.8 |
| 90% Confidence Interval | [223, 555] |
The histogram shows the distribution of Thalidomide Deaths Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.76.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide Deaths Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.77 Thalidomide Survivors Per Event
Value: 540 cases
Survivors per US-scale thalidomide event
Inputs:
- Thalidomide Mortality Rate 📊: 40% (95% CI: 35% - 45%)
- Thalidomide US Cases Prevented 🔢: 900 cases
\[ 900 \text{ (cases)} \times 60\% \text{ (survival)} = 540 \text{ survivors} \]
~ Medium confidence
2.77.1 Sensitivity Analysis
Sensitivity Indices for Thalidomide Survivors Per Event
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Thalidomide Mortality Rate | 0.5550 | Strong driver |
| Thalidomide US Cases Prevented | 0.4456 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.77.2 Monte Carlo Distribution
Simulation Results Summary: Thalidomide Survivors Per Event
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 540 |
| Mean (expected value) | 536 |
| Median (50th percentile) | 530 |
| Standard Deviation | 86.3 |
| 90% Confidence Interval | [398, 698] |
The histogram shows the distribution of Thalidomide Survivors Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.77.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide Survivors Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.78 Thalidomide US Cases Prevented
Value: 900 cases
Estimated US thalidomide cases prevented by FDA rejection
Inputs:
- Thalidomide Cases Worldwide 📊: 15.0k cases (95% CI: 10.0k cases - 20.0k cases)
- US Population Share 1960 📊: 6% (95% CI: 5.5% - 6.5%)
\[ 15{,}000 \times 6\% = 900 \text{ cases} \]
~ Medium confidence
2.78.1 Sensitivity Analysis
Sensitivity Indices for Thalidomide US Cases Prevented
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Thalidomide Cases Worldwide | 1.3752 | Strong driver |
| Thalidomide US Population Share 1960 | -0.3763 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.78.2 Monte Carlo Distribution
Simulation Results Summary: Thalidomide US Cases Prevented
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 900 |
| Mean (expected value) | 901 |
| Median (50th percentile) | 884 |
| Standard Deviation | 182 |
| 90% Confidence Interval | [622, 1.25k] |
The histogram shows the distribution of Thalidomide US Cases Prevented across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.78.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide US Cases Prevented will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.79 Thalidomide YLD Per Event
Value: 13.0k years
Years Lived with Disability per thalidomide event
Inputs:
- Thalidomide Disability Weight 📊: 0.4 ratio (95% CI: 0.32 ratio - 0.48 ratio)
- Thalidomide Survivors Per Event 🔢: 540 cases
- Thalidomide Survivor Lifespan 📊: 60 years (95% CI: 50 years - 70 years)
\[ 540 \text{ (surv)} \times 60 \text{ (yrs)} \times 0.4 \text{ (weight)} = 12{,}960 \text{ YLD} \]
~ Medium confidence
2.79.1 Sensitivity Analysis
Sensitivity Indices for Thalidomide YLD Per Event
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Thalidomide Disability Weight | 24.5185 | Strong driver |
| Thalidomide Survivor Lifespan | -20.7343 | Strong driver |
| Thalidomide Survivors Per Event | -2.7939 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.79.2 Monte Carlo Distribution
Simulation Results Summary: Thalidomide YLD Per Event
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 13.0k |
| Mean (expected value) | 13.3k |
| Median (50th percentile) | 12.6k |
| Standard Deviation | 4.50k |
| 90% Confidence Interval | [6.93k, 22.6k] |
The histogram shows the distribution of Thalidomide YLD Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.79.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide YLD Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.80 Thalidomide YLL Per Event
Value: 28.8k years
Years of Life Lost per thalidomide event (infant deaths)
Inputs:
- Thalidomide Deaths Per Event 🔢: 360 deaths
\[ 360 \text{ (deaths)} \times 80 \text{ (years)} = 28{,}800 \text{ YLL} \]
~ Medium confidence
2.80.1 Sensitivity Analysis
Sensitivity Indices for Thalidomide YLL Per Event
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Thalidomide Deaths Per Event | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.80.2 Monte Carlo Distribution
Simulation Results Summary: Thalidomide YLL Per Event
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 28.8k |
| Mean (expected value) | 29.1k |
| Median (50th percentile) | 28.2k |
| Standard Deviation | 7.67k |
| 90% Confidence Interval | [17.8k, 44.4k] |
The histogram shows the distribution of Thalidomide YLL Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.80.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Thalidomide YLL Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.81 Total Global Research Funding (Baseline + 1% treaty Funding)
Value: $94.7B
Total global research funding (baseline + 1% treaty funding)
Inputs:
- Global Government Medical Research Spending 📊: $67.5B (95% CI: $54B - $81B)
- Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2B
\[ Funding_{total} = Spending_{global} + Funding_{ann} = \$67.50B + \$27.18B = \$94.68B \]
Methodology: ../economics/economics
✓ High confidence
2.81.1 Sensitivity Analysis
Sensitivity Indices for Total Global Research Funding (Baseline + 1% treaty Funding)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Med Research Spending | 0.7761 | Strong driver |
| Treaty Annual Funding | 0.2297 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.81.2 Monte Carlo Distribution
Simulation Results Summary: Total Global Research Funding (Baseline + 1% treaty Funding)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $94.7B |
| Mean (expected value) | $94.5B |
| Median (50th percentile) | $94.1B |
| Standard Deviation | $8.46B |
| 90% Confidence Interval | [$81.1B, $109B] |
The histogram shows the distribution of Total Global Research Funding (Baseline + 1% treaty Funding) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.81.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Global Research Funding (Baseline + 1% treaty Funding) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.82 Treaty System Benefit Multiplier vs Childhood Vaccination Programs
Value: 10.3 ratio
Treaty system benefit multiplier vs childhood vaccination programs
Inputs:
- Estimated Annual Global Economic Benefit from Childhood Vaccination Programs 📊: $15B (SE: ±$4.50B)
- Combined Peace and Health Dividends for ROI Calculation 🔢: $155B
\[ Multiplier_{treaty} = \frac{Dividend_{ann}}{Benefit_{ann}} = \frac{\$155.05B}{\$15.00B} = 10.34 \]
Methodology: ../economics/economics#better-than-the-best-charities
✓ High confidence
2.82.1 Sensitivity Analysis
Sensitivity Indices for Treaty System Benefit Multiplier vs Childhood Vaccination Programs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Childhood Vaccination Annual Benefit | -1.3476 | Strong driver |
| Combined Peace Health Dividends Annual For ROI Calc | 0.5130 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.82.2 Monte Carlo Distribution
Simulation Results Summary: Treaty System Benefit Multiplier vs Childhood Vaccination Programs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 10.3 |
| Mean (expected value) | 10.8 |
| Median (50th percentile) | 10.6 |
| Standard Deviation | 1.85 |
| 90% Confidence Interval | [8.16, 14] |
The histogram shows the distribution of Treaty System Benefit Multiplier vs Childhood Vaccination Programs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.82.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty System Benefit Multiplier vs Childhood Vaccination Programs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.83 Amortized Annual Treaty Campaign Cost
Value: $250M
Amortized annual campaign cost (total cost ÷ campaign duration)
Inputs:
- Treaty Campaign Duration: 4 years (95% CI: 3 years - 5 years)
- Total 1% Treaty Campaign Cost 🔢: $1B
\[ AnnualCost = \$1B / 4 = \$0.25B \]
Methodology: ../strategy/roadmap#campaign-budget
✓ High confidence
2.83.1 Sensitivity Analysis
Sensitivity Indices for Amortized Annual Treaty Campaign Cost
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Campaign Total Cost | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.83.2 Monte Carlo Distribution
Simulation Results Summary: Amortized Annual Treaty Campaign Cost
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $250M |
| Mean (expected value) | $249M |
| Median (50th percentile) | $235M |
| Standard Deviation | $87.9M |
| 90% Confidence Interval | [$134M, $413M] |
The histogram shows the distribution of Amortized Annual Treaty Campaign Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.83.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Amortized Annual Treaty Campaign Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.84 Total 1% Treaty Campaign Cost
Value: $1B
Total treaty campaign cost (100% VICTORY Incentive Alignment Bonds)
Inputs:
- Global Referendum Campaign: Ads, Media, Partnerships, Staff, Legal/Compliance: $300M (95% CI: $180M - $500M)
- Political Lobbying Campaign: Direct Lobbying, Super Pacs, Opposition Research, Staff, Legal/Compliance: $650M (95% CI: $325M - $1.30B)
- Reserve Fund / Contingency Buffer: $50M (95% CI: $20M - $100M)
\[ CampaignCost = \$300M \text{ (ref)} + \$650M \text{ (lob)} + \$50M \text{ (res)} = \$1.0B \]
Methodology: ../appendix/fundraising-strategy#capital-structure-campaign-vs-implementation
✓ High confidence
2.84.1 Sensitivity Analysis
Sensitivity Indices for Total 1% Treaty Campaign Cost
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Campaign Budget Lobbying | 0.7092 | Strong driver |
| Treaty Campaign Budget Referendum | 0.2333 | Weak driver |
| Treaty Campaign Budget Reserve | 0.0579 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.84.2 Monte Carlo Distribution
Simulation Results Summary: Total 1% Treaty Campaign Cost
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $1B |
| Mean (expected value) | $997M |
| Median (50th percentile) | $939M |
| Standard Deviation | $351M |
| 90% Confidence Interval | [$536M, $1.65B] |
The histogram shows the distribution of Total 1% Treaty Campaign Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.84.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total 1% Treaty Campaign Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.85 Target Voting Bloc Size for Campaign
Value: 280M of people
Target voting bloc size for campaign (3.5% of global population - critical mass for social change)
Inputs:
- Global Population in 2024 📊: 8.00B of people (95% CI: 7.80B of people - 8.20B of people)
- Critical Mass Threshold for Social Change 📊: 3.5% (95% CI: 2.5% - 4.5%)
\[ Campaign_{camp,treaty} = Population_{global} \times Threshold_{global} = 8.00B \times 3.5\% = 280M \]
Methodology: ../strategy/roadmap#voting-bloc
✓ High confidence
2.85.1 Sensitivity Analysis
Sensitivity Indices for Target Voting Bloc Size for Campaign
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Population Activism Threshold % | 1.1097 | Strong driver |
| Global Population 2024 | -0.1099 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.85.2 Monte Carlo Distribution
Simulation Results Summary: Target Voting Bloc Size for Campaign
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 280M |
| Mean (expected value) | 279M |
| Median (50th percentile) | 276M |
| Standard Deviation | 42.1M |
| 90% Confidence Interval | [213M, 359M] |
The histogram shows the distribution of Target Voting Bloc Size for Campaign across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.85.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Target Voting Bloc Size for Campaign will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.86 Treaty ROI - Lag Elimination (PRIMARY)
Value: 1.19M ratio
Treaty ROI based on eliminating the 8.2-year post-safety efficacy lag (PRIMARY METHODOLOGY). Total one-time benefit from disease eradication delay elimination divided by $1B campaign cost. This is the primary ROI estimate for total health benefits.
Inputs:
- Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
- Standard Economic Value per QALY 📊: $150K (SE: ±$30K)
- Total 1% Treaty Campaign Cost 🔢: $1B
\[ ROI_{lag\_elimination} = \frac{\$1{,}286T}{\$1.00B} = 1{,}286{,}242:1 \]
Methodology: ../figures/dfda-investment-returns-bar-chart
~ Medium confidence
2.86.1 Sensitivity Analysis
Sensitivity Indices for Treaty ROI - Lag Elimination (PRIMARY)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Standard Economic QALY Value Usd | 4.2068 | Strong driver |
| Disease Eradication Delay DALYs | -3.0982 | Strong driver |
| Treaty Campaign Total Cost | -0.8212 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.86.2 Monte Carlo Distribution
Simulation Results Summary: Treaty ROI - Lag Elimination (PRIMARY)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 1.19M |
| Mean (expected value) | 1.24M |
| Median (50th percentile) | 1.26M |
| Standard Deviation | 50.2k |
| 90% Confidence Interval | [1.14M, 1.27M] |
The histogram shows the distribution of Treaty ROI - Lag Elimination (PRIMARY) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.86.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty ROI - Lag Elimination (PRIMARY) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.87 Cost per DALY Averted (Timeline Shift)
Value: $0.126
Cost per DALY averted from one-time timeline shift (8.2 years). This is a conservative estimate that only counts campaign cost ($1B) and ignores all economic benefits ($27B/year funding unlocked + $50B/year R&D savings). For comparison: bed nets cost $89.0/DALY, deworming costs $4-10/DALY. This intervention is 700x more cost-effective than bed nets while also being self-funding.
Inputs:
- Total 1% Treaty Campaign Cost 🔢: $1B
- Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
\[ \text{Cost/DALY} = \frac{\$1.0B}{7.90B} = \$0.127 \]
Methodology: ../appendix/dfda-cost-benefit-analysis
✓ High confidence
2.87.1 Sensitivity Analysis
Sensitivity Indices for Cost per DALY Averted (Timeline Shift)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Disease Eradication Delay DALYs | 0.5676 | Strong driver |
| Treaty Campaign Total Cost | 0.4332 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.87.2 Monte Carlo Distribution
Simulation Results Summary: Cost per DALY Averted (Timeline Shift)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $0.126 |
| Mean (expected value) | $0.121 |
| Median (50th percentile) | $0.119 |
| Standard Deviation | $0.022 |
| 90% Confidence Interval | [$0.090, $0.159] |
The histogram shows the distribution of Cost per DALY Averted (Timeline Shift) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.87.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Cost per DALY Averted (Timeline Shift) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.88 Expected Cost per DALY (Risk-Adjusted)
Value: $13
Expected cost per DALY accounting for political success probability uncertainty. Monte Carlo samples from beta(0.1%, 10%) distribution. At the ultra-conservative 1% estimate, this is still more cost-effective than bed nets ($89.0/DALY).
Inputs:
- Cost per DALY Averted (Timeline Shift) 🔢: $0.126
- Political Success Probability 📊: 1% (95% CI: 0.1% - 10%)
\[ E[\text{Cost/DALY}] = \frac{\text{Cost}_{conditional}}{P_{success}} \]
Methodology: ../appendix/dfda-cost-benefit-analysis
? Low confidence
2.88.1 Sensitivity Analysis
Sensitivity Indices for Expected Cost per DALY (Risk-Adjusted)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Political Success Probability | -0.5921 | Strong driver |
| Treaty dFDA Cost Per DALY Timeline Shift | 0.2380 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.88.2 Monte Carlo Distribution
Simulation Results Summary: Expected Cost per DALY (Risk-Adjusted)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $13 |
| Mean (expected value) | $69 |
| Median (50th percentile) | $71 |
| Standard Deviation | $54 |
| 90% Confidence Interval | [$2.34, $148] |
The histogram shows the distribution of Expected Cost per DALY (Risk-Adjusted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.88.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Expected Cost per DALY (Risk-Adjusted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.89 Expected Cost-Effectiveness vs Bed Nets Multiplier
Value: 7.07 ratio
Expected value multiplier vs bed nets (accounts for political uncertainty)
Inputs:
- Bed Nets Cost per DALY 📊: $89 (95% CI: $78 - $100)
- Expected Cost per DALY (Risk-Adjusted) 🔢: $13
\[ E[\text{Multiplier}] = \frac{\$89}{\$0.51} = 175\times \]
? Low confidence
2.89.1 Sensitivity Analysis
Sensitivity Indices for Expected Cost-Effectiveness vs Bed Nets Multiplier
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Expected Cost Per DALY | -0.6072 | Strong driver |
| Bed Nets Cost Per DALY | 0.0814 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.89.2 Monte Carlo Distribution
Simulation Results Summary: Expected Cost-Effectiveness vs Bed Nets Multiplier
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 7.07 |
| Mean (expected value) | 7.61 |
| Median (50th percentile) | 1.23 |
| Standard Deviation | 13.6 |
| 90% Confidence Interval | [0.647, 37.4] |
The histogram shows the distribution of Expected Cost-Effectiveness vs Bed Nets Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.89.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Expected Cost-Effectiveness vs Bed Nets Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.90 Annual Lives Saved from 1% Reduction in Conflict Deaths
Value: 2.45k lives/year
Annual lives saved from 1% reduction in conflict deaths
Inputs:
- Total Annual Conflict Deaths Globally 🔢: 245k deaths/year
- 1% Reduction in Military Spending/War Costs from Treaty: 1%
\[ Deaths_{ann} = Deaths_{total} \times Reduction_{treaty} = 244{,}600 \times 1.0\% = 2{,}446 \]
Methodology: ../appendix/parameters-and-calculations#sec-treaty_lives_saved_annual_global
✓ High confidence
2.90.1 Sensitivity Analysis
Sensitivity Indices for Annual Lives Saved from 1% Reduction in Conflict Deaths
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Global Annual Conflict Deaths Total | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.90.2 Monte Carlo Distribution
Simulation Results Summary: Annual Lives Saved from 1% Reduction in Conflict Deaths
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 2.45k |
| Mean (expected value) | 2.44k |
| Median (50th percentile) | 2.42k |
| Standard Deviation | 315 |
| 90% Confidence Interval | [1.94k, 3.02k] |
The histogram shows the distribution of Annual Lives Saved from 1% Reduction in Conflict Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.90.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual Lives Saved from 1% Reduction in Conflict Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.91 1% treaty Basic Annual Benefits (Peace + R&D Savings)
Value: $155B
Basic annual benefits: peace dividend + Decentralized Framework for Drug Assessment R&D savings only (2 of 8 benefit categories, excludes regulatory delay value)
Inputs:
- Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $114B
- Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $41.5B
\[ Benefit_{ann} = Cost_{soc,ann} + Benefit_{gross,ann} = \$113.55B + \$41.50B = \$155.05B \]
Methodology: ../appendix/parameters-and-calculations#sec-treaty_peace_plus_rd_annual_benefits
✓ High confidence
2.91.1 Sensitivity Analysis
Sensitivity Indices for 1% treaty Basic Annual Benefits (Peace + R&D Savings)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Peace Dividend Annual Societal Benefit | 0.7305 | Strong driver |
| dFDA R&D Gross Savings Annual | 0.3480 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.91.2 Monte Carlo Distribution
Simulation Results Summary: 1% treaty Basic Annual Benefits (Peace + R&D Savings)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $155B |
| Mean (expected value) | $154B |
| Median (50th percentile) | $152B |
| Standard Deviation | $23.1B |
| 90% Confidence Interval | [$119B, $195B] |
The histogram shows the distribution of 1% treaty Basic Annual Benefits (Peace + R&D Savings) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.91.3 Exceedance Probability
This exceedance probability chart shows the likelihood that 1% treaty Basic Annual Benefits (Peace + R&D Savings) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.92 Annual QALYs Gained from Peace Dividend
Value: 85.6k QALYs/year
Annual QALYs gained from peace dividend (lives saved × QALYs/life)
Inputs:
- Standard QALYs per Life Saved 📊: 35 QALYs/life (SE: ±7 QALYs/life)
- Annual Lives Saved from 1% Reduction in Conflict Deaths 🔢: 2.45k lives/year
\[ Dividend_{ann} = QALYs_{RD} \times Deaths_{ann} = 35 \times 2{,}446 = 85{,}610 \]
Methodology: ../appendix/parameters-and-calculations#sec-treaty_qalys_gained_annual_global
✓ High confidence
2.92.1 Sensitivity Analysis
Sensitivity Indices for Annual QALYs Gained from Peace Dividend
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Lives Saved Annual Global | 0.7923 | Strong driver |
| Standard QALYs Per Life Saved | 0.2060 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.92.2 Monte Carlo Distribution
Simulation Results Summary: Annual QALYs Gained from Peace Dividend
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 85.6k |
| Mean (expected value) | 87.5k |
| Median (50th percentile) | 84.4k |
| Standard Deviation | 28.6k |
| 90% Confidence Interval | [45.1k, 141k] |
The histogram shows the distribution of Annual QALYs Gained from Peace Dividend across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.92.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Annual QALYs Gained from Peace Dividend will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.93 1% treaty Recurring Annual Benefits
Value: $155B
Truly recurring annual benefits from 1% treaty: peace dividend ($113.6B/year) + R&D savings ($41.5B/year). Note: Health benefits are one-time timeline shifts, NOT included here.
Inputs:
- Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $41.5B
- Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $114B
\[ Benefit_{ann} = Benefit_{DFDA,ann} + Cost_{soc,ann} = \$41.50B + \$113.55B = \$155.05B \]
Methodology: ../economics/economics
✓ High confidence
2.93.1 Sensitivity Analysis
Sensitivity Indices for 1% treaty Recurring Annual Benefits
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Peace Dividend Annual Societal Benefit | 0.7305 | Strong driver |
| dFDA Benefit R&D Only Annual | 0.3480 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.93.2 Monte Carlo Distribution
Simulation Results Summary: 1% treaty Recurring Annual Benefits
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $155B |
| Mean (expected value) | $154B |
| Median (50th percentile) | $152B |
| Standard Deviation | $23.1B |
| 90% Confidence Interval | [$119B, $195B] |
The histogram shows the distribution of 1% treaty Recurring Annual Benefits across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.93.3 Exceedance Probability
This exceedance probability chart shows the likelihood that 1% treaty Recurring Annual Benefits will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.94 Treaty ROI - Historical Rate (Conservative Floor)
Value: 251k ratio
Treaty ROI based on historical rate of drug development (existing drugs only, conservative floor). Total one-time benefit from avoiding regulatory delay for drugs already in development divided by $1B campaign cost.
Inputs:
\[ ROI_{treaty} = \frac{Delay_{total}}{Cost_{camp,total}} = \frac{\$250.92T}{\$1.00B} = 250{,}920 \]
Methodology: ../figures/dfda-investment-returns-bar-chart
✓ High confidence
2.94.1 Sensitivity Analysis
Sensitivity Indices for Treaty ROI - Historical Rate (Conservative Floor)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Campaign Total Cost | -1.3414 | Strong driver |
| Historical Progress Economic Loss Total | 0.4296 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.94.2 Monte Carlo Distribution
Simulation Results Summary: Treaty ROI - Historical Rate (Conservative Floor)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 251k |
| Mean (expected value) | 261k |
| Median (50th percentile) | 266k |
| Standard Deviation | 25.4k |
| 90% Confidence Interval | [219k, 278k] |
The histogram shows the distribution of Treaty ROI - Historical Rate (Conservative Floor) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.94.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty ROI - Historical Rate (Conservative Floor) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.95 Treaty ROI - Innovation Acceleration (Optimistic)
Value: 2.38M ratio
Treaty ROI based on lag elimination plus innovation acceleration effects (OPTIMISTIC UPPER BOUND). Includes cascading innovation effects from eliminating Phase 2-4 cost barriers. Research-backed 2× multiplier represents combined timeline and volume effects (Nature 2023, Woods et al. 2024).
Inputs:
- Total Economic Loss from Disease Eradication + Innovation Acceleration 🔢: $2.38 quadrillion
- Total 1% Treaty Campaign Cost 🔢: $1B
\[ ROI_{treaty} = \frac{Ratio_{total}}{Cost_{camp,total}} = \frac{\$2382.84T}{\$1.00B} = 2.4M \]
Methodology: ../figures/dfda-investment-returns-bar-chart
? Low confidence
2.95.1 Sensitivity Analysis
Sensitivity Indices for Treaty ROI - Innovation Acceleration (Optimistic)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Disease Eradication Plus Acceleration Economic Loss Total | 5.3101 | Strong driver |
| Treaty Campaign Total Cost | -5.1271 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.95.2 Monte Carlo Distribution
Simulation Results Summary: Treaty ROI - Innovation Acceleration (Optimistic)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 2.38M |
| Mean (expected value) | 2.47M |
| Median (50th percentile) | 2.51M |
| Standard Deviation | 100k |
| 90% Confidence Interval | [2.28M, 2.54M] |
The histogram shows the distribution of Treaty ROI - Innovation Acceleration (Optimistic) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.95.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty ROI - Innovation Acceleration (Optimistic) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.96 Treaty ROI - Lag Elimination (PRIMARY)
Value: 1.19M ratio
Treaty ROI based on eliminating the 8.2-year post-safety efficacy lag (PRIMARY METHODOLOGY). Total one-time benefit from disease eradication delay elimination divided by $1B campaign cost. This is the primary ROI estimate for total health benefits.
Inputs:
- Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
- Standard Economic Value per QALY 📊: $150K (SE: ±$30K)
- Total 1% Treaty Campaign Cost 🔢: $1B
\[ ROI_{lag\_elimination} = \frac{\$1{,}286T}{\$1.00B} = 1{,}286{,}242:1 \]
Methodology: ../figures/dfda-investment-returns-bar-chart
~ Medium confidence
2.96.1 Sensitivity Analysis
Sensitivity Indices for Treaty ROI - Lag Elimination (PRIMARY)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Standard Economic QALY Value Usd | 4.2068 | Strong driver |
| Disease Eradication Delay DALYs | -3.0982 | Strong driver |
| Treaty Campaign Total Cost | -0.8212 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.96.2 Monte Carlo Distribution
Simulation Results Summary: Treaty ROI - Lag Elimination (PRIMARY)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 1.19M |
| Mean (expected value) | 1.24M |
| Median (50th percentile) | 1.26M |
| Standard Deviation | 50.2k |
| 90% Confidence Interval | [1.14M, 1.27M] |
The histogram shows the distribution of Treaty ROI - Lag Elimination (PRIMARY) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.96.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Treaty ROI - Lag Elimination (PRIMARY) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.97 Total Annual Treaty System Costs
Value: $290M
Total annual system costs (campaign + Decentralized Framework for Drug Assessment operations)
Inputs:
- Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
- Amortized Annual Treaty Campaign Cost 🔢: $250M
\[ Cost_{total} = Cost_{DFDA,ann} + Cost_{camp,ann} = \$40.0M + \$250.0M = \$290.0M \]
Methodology: ../appendix/parameters-and-calculations#sec-treaty_total_annual_costs
✓ High confidence
2.97.1 Sensitivity Analysis
Sensitivity Indices for Total Annual Treaty System Costs
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty Campaign Annual Cost Amortized | 0.9158 | Strong driver |
| dFDA Annual OPEX | 0.0856 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.97.2 Monte Carlo Distribution
Simulation Results Summary: Total Annual Treaty System Costs
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $290M |
| Mean (expected value) | $289M |
| Median (50th percentile) | $274M |
| Standard Deviation | $95.9M |
| 90% Confidence Interval | [$161M, $468M] |
The histogram shows the distribution of Total Annual Treaty System Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.97.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Total Annual Treaty System Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.98 1% treaty Recurring Annual Benefits
Value: $155B
Truly recurring annual benefits from 1% treaty: peace dividend ($113.6B/year) + R&D savings ($41.5B/year). Note: Health benefits are one-time timeline shifts, NOT included here.
Inputs:
- Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $41.5B
- Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $114B
\[ Benefit_{total} = Benefit_{DFDA,ann} + Cost_{soc,ann} = \$41.50B + \$113.55B = \$155.05B \]
Methodology: ../economics/economics
✓ High confidence
2.98.1 Sensitivity Analysis
Sensitivity Indices for 1% treaty Recurring Annual Benefits
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Peace Dividend Annual Societal Benefit | 0.7305 | Strong driver |
| dFDA Benefit R&D Only Annual | 0.3480 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.98.2 Monte Carlo Distribution
Simulation Results Summary: 1% treaty Recurring Annual Benefits
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $155B |
| Mean (expected value) | $154B |
| Median (50th percentile) | $152B |
| Standard Deviation | $23.1B |
| 90% Confidence Interval | [$119B, $195B] |
The histogram shows the distribution of 1% treaty Recurring Annual Benefits across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.98.3 Exceedance Probability
This exceedance probability chart shows the likelihood that 1% treaty Recurring Annual Benefits will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.99 Cost-Effectiveness vs Bed Nets Multiplier
Value: 707 ratio
How many times more cost-effective than bed nets (using $89/DALY midpoint estimate)
Inputs:
- Bed Nets Cost per DALY 📊: $89 (95% CI: $78 - $100)
- Cost per DALY Averted (Timeline Shift) 🔢: $0.126
\[ \text{Multiplier} = \frac{\$89}{\$0.127} = 701\times \]
✓ High confidence
2.99.1 Sensitivity Analysis
Sensitivity Indices for Cost-Effectiveness vs Bed Nets Multiplier
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Treaty dFDA Cost Per DALY Timeline Shift | -0.5699 | Strong driver |
| Bed Nets Cost Per DALY | -0.4111 | Moderate driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.99.2 Monte Carlo Distribution
Simulation Results Summary: Cost-Effectiveness vs Bed Nets Multiplier
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 707 |
| Mean (expected value) | 751 |
| Median (50th percentile) | 749 |
| Standard Deviation | 87.7 |
| 90% Confidence Interval | [617, 884] |
The histogram shows the distribution of Cost-Effectiveness vs Bed Nets Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.99.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Cost-Effectiveness vs Bed Nets Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.100 Cumulative Trial Capacity Years Over 20 Years
Value: 456 years
Cumulative trial-capacity-equivalent years over 20-year period
Inputs:
- Trial Capacity Multiplier 🔢: 22.8 ratio
\[ Capacity_{20yr} = 25.7 \times 20 = 514 \text{ years} \]
✓ High confidence
2.100.1 Sensitivity Analysis
Sensitivity Indices for Cumulative Trial Capacity Years Over 20 Years
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Trial Capacity Multiplier | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.100.2 Monte Carlo Distribution
Simulation Results Summary: Cumulative Trial Capacity Years Over 20 Years
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 456 |
| Mean (expected value) | 475 |
| Median (50th percentile) | 465 |
| Standard Deviation | 98.3 |
| 90% Confidence Interval | [321, 676] |
The histogram shows the distribution of Cumulative Trial Capacity Years Over 20 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.100.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Cumulative Trial Capacity Years Over 20 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.101 Trial Capacity Multiplier
Value: 22.8 ratio
Trial capacity multiplier from DIH funding capacity vs. current global trial participation
Inputs:
- Annual Global Clinical Trial Participants 📊: 1.90M patients/year (95% CI: 1.50M patients/year - 2.30M patients/year)
- Patients Fundable Annually 🔢: 43.4M patients/year
\[ Multiplier = \frac{Fundable_{ann}}{Trials_{curr}} = \frac{43.4M}{1.9M} = 22.85 \]
✓ High confidence
2.101.1 Sensitivity Analysis
Sensitivity Indices for Trial Capacity Multiplier
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| DIH Patients Fundable Annually | 0.8710 | Strong driver |
| Current Trial Slots Available | -0.1260 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.101.2 Monte Carlo Distribution
Simulation Results Summary: Trial Capacity Multiplier
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 22.8 |
| Mean (expected value) | 23.8 |
| Median (50th percentile) | 23.3 |
| Standard Deviation | 4.92 |
| 90% Confidence Interval | [16.1, 33.8] |
The histogram shows the distribution of Trial Capacity Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.101.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Trial Capacity Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.102 Ratio of Type Ii Error Cost to Type I Error Benefit
Value: 3.07k ratio
Ratio of Type II error cost to Type I error benefit (harm from delay vs. harm prevented)
Inputs:
- Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
- Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) 🔢: 2.59M DALYs
\[ Cost = \frac{DALYs_{dis}}{DALYs} = \frac{7.94B}{2.6M} = 3{,}068 \]
Methodology: ../appendix/regulatory-mortality-analysis#risk-analysis
~ Medium confidence
2.102.1 Sensitivity Analysis
Sensitivity Indices for Ratio of Type Ii Error Cost to Type I Error Benefit
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Type I Error Benefit DALYs | -0.7283 | Strong driver |
| Disease Eradication Delay DALYs | -0.2363 | Weak driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.102.2 Monte Carlo Distribution
Simulation Results Summary: Ratio of Type Ii Error Cost to Type I Error Benefit
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 3.07k |
| Mean (expected value) | 3.16k |
| Median (50th percentile) | 3.13k |
| Standard Deviation | 396 |
| 90% Confidence Interval | [2.55k, 3.88k] |
The histogram shows the distribution of Ratio of Type Ii Error Cost to Type I Error Benefit across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.102.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Ratio of Type Ii Error Cost to Type I Error Benefit will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.103 Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)
Value: 2.59M DALYs
Maximum DALYs saved by FDA preventing unsafe drugs over 62-year period 1962-2024 (extreme overestimate: one Thalidomide-scale event per year)
Inputs:
- Thalidomide DALYs Per Event 🔢: 41.8k DALYs
\[ 41{,}760 \times 62 = 2.59M \text{ DALYs} \]
Methodology: ../appendix/regulatory-mortality-analysis#risk-analysis
? Low confidence
2.103.1 Sensitivity Analysis
Sensitivity Indices for Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Thalidomide DALYs Per Event | 1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.103.2 Monte Carlo Distribution
Simulation Results Summary: Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 2.59M |
| Mean (expected value) | 2.63M |
| Median (50th percentile) | 2.53M |
| Standard Deviation | 754k |
| 90% Confidence Interval | [1.54M, 4.15M] |
The histogram shows the distribution of Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.103.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.104 Unexplored Therapeutic Frontier
Value: 99.7%
Fraction of possible drug-disease space that remains unexplored (>99%)
Inputs:
- Tested Drug-Disease Relationships: 32.5k relationships (95% CI: 15.0k relationships - 50.0k relationships)
- Possible Drug-Disease Combinations 🔢: 9.50M combinations
\[ \text{Unexplored} = 1 - \text{Exploration Ratio} = 1 - 0.00342 = 99.66\% \]
Methodology: ../problem/untapped-therapeutic-frontier
✓ High confidence
2.104.1 Sensitivity Analysis
Sensitivity Indices for Unexplored Therapeutic Frontier
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| Tested Relationships Estimate | -1.0000 | Strong driver |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.104.2 Monte Carlo Distribution
Simulation Results Summary: Unexplored Therapeutic Frontier
| Statistic | Value |
|---|---|
| Baseline (deterministic) | 99.7% |
| Mean (expected value) | 99.7% |
| Median (50th percentile) | 99.7% |
| Standard Deviation | 0.0868% |
| 90% Confidence Interval | [99.5%, 99.8%] |
The histogram shows the distribution of Unexplored Therapeutic Frontier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.104.3 Exceedance Probability
This exceedance probability chart shows the likelihood that Unexplored Therapeutic Frontier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
2.105 US Major Diseases Total Annual Cost
Value: $1.25T
Total annual US cost of major diseases (diabetes, Alzheimer’s, heart disease, cancer)
Inputs:
- US Alzheimer’s Annual Cost 📊: $355B (95% CI: $302B - $408B)
- US Cancer Annual Cost 📊: $208B (95% CI: $177B - $239B)
- US Diabetes Annual Cost 📊: $327B (95% CI: $278B - $376B)
- US Heart Disease Annual Cost 📊: $363B (95% CI: $309B - $417B)
\[ Cost_{total} = Cost_{alz,ann} + Cost_{cancer,ann} + Cost_{diab,ann} + Cost_{heart,ann} = \$355.00B + \$208.00B + \$327.00B + \$363.00B = \$1.25T \]
Methodology: ../solution/aligning-incentives#insurance-companies
✓ High confidence
2.105.1 Sensitivity Analysis
Sensitivity Indices for US Major Diseases Total Annual Cost
Regression-based sensitivity showing which inputs explain the most variance in the output.
| Input Parameter | Sensitivity Coefficient | Interpretation |
|---|---|---|
| US Alzheimers Annual Cost | 1.5728 | Strong driver |
| US Diabetes Annual Cost | -0.3421 | Moderate driver |
| US Heart Disease Annual Cost | -0.2747 | Weak driver |
| US Cancer Annual Cost | 0.0440 | Minimal effect |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.
2.105.2 Monte Carlo Distribution
Simulation Results Summary: US Major Diseases Total Annual Cost
| Statistic | Value |
|---|---|
| Baseline (deterministic) | $1.25T |
| Mean (expected value) | $1.25T |
| Median (50th percentile) | $1.25T |
| Standard Deviation | $91.1B |
| 90% Confidence Interval | [$1.10T, $1.42T] |
The histogram shows the distribution of US Major Diseases Total Annual Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.
2.105.3 Exceedance Probability
This exceedance probability chart shows the likelihood that US Major Diseases Total Annual Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.
3 External Data Sources
Parameters sourced from peer-reviewed publications, institutional databases, and authoritative reports.
3.1 Antidepressant Trial Exclusion Rate
Value: 86.1%
Mean exclusion rate in antidepressant trials (86.1% of real-world patients excluded)
Source: NIH (2015) - Antidepressant clinical trial exclusion rates
✓ High confidence
3.2 Average Annual Stock Market Return
Value: 10%
Average annual stock market return (10%)
Source: CNBC (2025) - Warren Buffett’s career average investment return
✓ High confidence
3.3 Average US Hourly Wage
Value: $30
Average US hourly wage
Source: BLS (2024) - Average US hourly wage
✓ High confidence
3.4 Baseline Annual Lives Saved by Pharmaceuticals
Value: 12 deaths/year
Baseline annual lives saved by pharmaceuticals (conservative aggregate)
Source: World Health Organization (2024) - WHO Global Health Estimates 2024
~ Medium confidence • 📊 Peer-reviewed • Updated 2024
3.5 Bed Nets Cost per DALY
Value: $89
GiveWell cost per DALY for insecticide-treated bed nets (midpoint estimate, range $78-100). DALYs (Disability-Adjusted Life Years) measure disease burden by combining years of life lost and years lived with disability. Bed nets prevent malaria deaths and are considered a gold standard benchmark for cost-effective global health interventions - if an intervention costs less per DALY than bed nets, it’s exceptionally cost-effective. GiveWell synthesizes peer-reviewed academic research with transparent, rigorous methodology and extensive external expert review.
Source: GiveWell - GiveWell Cost per Life Saved for Top Charities (2024)
3.5.1 Uncertainty Range
Technical: 95% CI: [$78, $100] • Distribution: Normal
What this means: This estimate has moderate uncertainty. The true value likely falls between $78 and $100 (±12%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The normal distribution means values cluster around the center with equal chances of being higher or lower.
3.5.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed
3.6 Average Reading Speed
Value: 200 words/minute
Average reading speed (conservative for non-fiction)
Source: Educational psychology literature - Average reading speed
✓ High confidence
3.7 Total Annual Value of Unpaid Caregiving in US
Value: $600B
Total annual value of unpaid caregiving in US
Source: AARP (2023) - Unpaid caregiver hours and economic value
✓ High confidence
3.8 Number of Unpaid Caregivers in US
Value: 38.0M people
Number of unpaid caregivers in US
Source: AARP (2023) - Unpaid caregiver hours and economic value
✓ High confidence
3.9 Average Monthly Hours of Unpaid Family Caregiving in US
Value: 20 hours/month
Average monthly hours of unpaid family caregiving in US
Source: AARP (2023) - Unpaid caregiver hours and economic value
✓ High confidence
3.10 Estimated Replacement Cost per Hour of Caregiving
Value: $25
Estimated replacement cost per hour of caregiving
Source: AARP (2023) - Unpaid caregiver hours and economic value
✓ High confidence
3.11 Estimated Annual Global Economic Benefit from Childhood Vaccination Programs
Value: $15B
Estimated annual global economic benefit from childhood vaccination programs (measles, polio, etc.)
Source: CDC MMWR (1994) - Childhood vaccination economic benefits
3.11.1 Uncertainty Range
Technical: Distribution: Lognormal (SE: $4.50B)
3.11.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.12 Return on Investment from Childhood Vaccination Programs
Value: 13 ratio
Return on investment from childhood vaccination programs
Source: CDC (2017) - Childhood Vaccination (US) ROI
✓ High confidence
3.13 Disability Weight for Untreated Chronic Conditions
Value: 0.35 weight
Disability weight for untreated chronic conditions (WHO Global Burden of Disease)
Source: World Health Organization (2024) - WHO Global Health Estimates 2024
3.13.1 Uncertainty Range
Technical: Distribution: Normal (SE: 0.07 weight)
3.13.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
~ Medium confidence • 📊 Peer-reviewed
3.14 Current Active Trials at Any Given Time
Value: 10.0k trials
Current active trials at any given time (3-5 year duration)
Source: Direct analysis via - ClinicalTrials.gov cumulative enrollment data (2025)
✓ High confidence
3.15 Current Clinical Trial Participation Rate
Value: 0.06%
Current clinical trial participation rate (0.06% of population)
Source: ACS CAN - Clinical trial patient participation rate
✓ High confidence
3.16 Global Population with Chronic Diseases
Value: 2.40B people
Global population with chronic diseases
Source: ScienceDaily (2015) - Global prevalence of chronic disease
3.16.1 Uncertainty Range
Technical: 95% CI: [2.00B people, 2.80B people] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between 2.00B people and 2.80B people (±17%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.16.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.17 Average Annual New Drug Approvals Globally
Value: 50 drugs/year
Average annual new drug approvals globally
Source: C&EN (2025) - Annual number of new drugs approved globally: ~50
✓ High confidence
3.18 Current Global Clinical Trials per Year
Value: 3.30k trials/year
Current global clinical trials per year
Source: Research and Markets (2024) - Global clinical trials market 2024
3.18.1 Uncertainty Range
Technical: 95% CI: [2.64k trials/year, 3.96k trials/year] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between 2.64k trials/year and 3.96k trials/year (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.18.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.19 Current Trial Abandonment Rate
Value: 40%
Current trial abandonment rate (40% never complete)
Source: Industry estimates - Clinical trial abandonment
✓ High confidence
3.20 Annual Global Clinical Trial Participants
Value: 1.90M patients/year
Annual global clinical trial participants (IQVIA 2022: 1.9M post-COVID normalization)
Source: IQVIA Report - Global trial capacity
3.20.1 Uncertainty Range
Technical: 95% CI: [1.50M patients/year, 2.30M patients/year] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between 1.50M patients/year and 2.30M patients/year (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.20.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.21 Annual Defense Industry Lobbying Spending
Value: $127M
Annual defense industry lobbying spending
Source: OpenSecrets (2024) - Lobbying Spend (Defense)
✓ High confidence • 📊 Peer-reviewed • Updated 2024
3.22 Deworming Cost per DALY
Value: $55
Cost per DALY for deworming programs (range $28-82, midpoint estimate). GiveWell notes this 2011 estimate is outdated and their current methodology focuses on long-term income effects rather than short-term health DALYs.
Source: GiveWell - Cost per DALY for Deworming Programs
? Low confidence
3.23 Drug Development Cost (1980s)
Value: $194M
Drug development cost in 1980s (compounded to approval, 1990 dollars)
Source: Think by Numbers (1962) - Pre-1962 drug development costs and timeline
3.23.1 Uncertainty Range
Technical: Distribution: Fixed
3.23.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.24 Drug Repurposing Success Rate
Value: 30%
Percentage of drugs that gain at least one new indication after initial approval
Source: Nature Medicine (2024) - Drug Repurposing Rate (~30%)
✓ High confidence
3.25 Economic Multiplier for Education Investment
Value: 2.1 ratio
Economic multiplier for education investment (2.1x ROI)
Source: EPI - Education investment economic multiplier (2.1)
✓ High confidence
3.26 Economic Multiplier for Healthcare Investment
Value: 4.3 ratio
Economic multiplier for healthcare investment (4.3x ROI)
Source: PMC (2022) - Healthcare investment economic multiplier (1.8)
✓ High confidence
3.27 Economic Multiplier for Infrastructure Investment
Value: 1.6 ratio
Economic multiplier for infrastructure investment (1.6x ROI)
Source: World Bank (2022) - Infrastructure investment economic multiplier (1.6)
✓ High confidence
3.28 Economic Multiplier for Military Spending
Value: 0.6 ratio
Economic multiplier for military spending (0.6x ROI)
Source: Mercatus - Military spending economic multiplier (0.6)
✓ High confidence
3.29 Regulatory Delay for Efficacy Testing Post-Safety Verification
Value: 8.2 years
Regulatory delay for efficacy testing (Phase II/III) post-safety verification
3.29.1 Uncertainty Range
Technical: Distribution: Normal (SE: 1 years)
3.29.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed • Updated 2021
3.30 FDA-Approved Drug Products
Value: 20.0k products
Total FDA-approved drug products in the U.S.
Source: FDA - FDA-approved prescription drug products (20,000+)
✓ High confidence
3.31 FDA-Approved Unique Active Ingredients
Value: 1.65k compounds
Unique active pharmaceutical ingredients in FDA-approved products (midpoint of 1,300-2,000 range)
Source: FDA - FDA-approved prescription drug products (20,000+)
3.31.1 Uncertainty Range
Technical: 95% CI: [1.30k compounds, 2.00k compounds] • Distribution: Uniform
What this means: This estimate has moderate uncertainty. The true value likely falls between 1.30k compounds and 2.00k compounds (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The uniform distribution means any value in the range is equally likely.
3.31.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.32 FDA GRAS Substances
Value: 635 substances
FDA Generally Recognized as Safe (GRAS) substances (midpoint of 570-700 range)
Source: FDA - FDA GRAS List Count (~570-700)
3.32.1 Uncertainty Range
Technical: 95% CI: [570 substances, 700 substances] • Distribution: Uniform
What this means: This estimate has moderate uncertainty. The true value likely falls between 570 substances and 700 substances (±10%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The uniform distribution means any value in the range is equally likely.
3.32.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.33 FDA Phase 1 to Approval Timeline
Value: 9.1 years
FDA timeline from Phase 1 start to approval (Phase 1-3 + NDA review)
Source: Drugs.com - FDA drug approval timeline
3.33.1 Uncertainty Range
Technical: 95% CI: [6 years, 12 years] • Distribution: Gamma (SE: 2 years)
What this means: There’s significant uncertainty here. The true value likely falls between 6 years and 12 years (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The gamma distribution means values follow a specific statistical pattern.
3.33.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.34 Givewell Average Cost per Life Saved Across Top Charities
Value: $4.50K
GiveWell average cost per life saved across top charities
Source: GiveWell - GiveWell Cost per Life Saved for Top Charities (2024)
✓ High confidence
3.35 Givewell Cost per Life Saved (Maximum)
Value: $5.50K
GiveWell cost per life saved (Against Malaria Foundation)
Source: GiveWell - GiveWell Cost per Life Saved for Top Charities (2024)
✓ High confidence
3.36 Givewell Cost per Life Saved (Minimum)
Value: $3.50K
GiveWell cost per life saved (Helen Keller International)
Source: GiveWell - GiveWell Cost per Life Saved for Top Charities (2024)
✓ High confidence
3.37 Annual Deaths from Active Combat Worldwide
Value: 234k deaths/year
Annual deaths from active combat worldwide
Source: ACLED (2024) - Active combat deaths annually
3.37.1 Uncertainty Range
Technical: 95% CI: [180k deaths/year, 300k deaths/year] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between 180k deaths/year and 300k deaths/year (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.37.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.38 Annual Deaths from State Violence
Value: 2.70k deaths/year
Annual deaths from state violence
Source: UCDP - State violence deaths annually
3.38.1 Uncertainty Range
Technical: 95% CI: [1.50k deaths/year, 5.00k deaths/year] • Distribution: Lognormal
What this means: This estimate is highly uncertain. The true value likely falls between 1.50k deaths/year and 5.00k deaths/year (±65%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.38.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.39 Annual Deaths from Terror Attacks Globally
Value: 8.30k deaths/year
Annual deaths from terror attacks globally
Source: Our World in Data (2024) - Terror attack deaths (8,300 annually)
3.39.1 Uncertainty Range
Technical: 95% CI: [6.00k deaths/year, 12.0k deaths/year] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between 6.00k deaths/year and 12.0k deaths/year (±36%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.39.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.40 Annual Deaths from Curable Diseases Globally
Value: 55.0M deaths/year
Annual deaths from all diseases and aging globally
Source: World Health Organization (2024) - WHO Global Health Estimates 2024
✓ High confidence
3.41 Annual Environmental Damage and Restoration Costs from Conflict
Value: $100B
Annual environmental damage and restoration costs from conflict
Source: Brown Watson Costs of War - Environmental cost of war ($100B annually)
3.41.1 Uncertainty Range
Technical: 95% CI: [$70B, $140B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $70B and $140B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.41.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.42 Annual Infrastructure Damage to Communications from Conflict
Value: $298B
Annual infrastructure damage to communications from conflict
Source: Brown Watson Costs of War - Environmental cost of war ($100B annually)
3.42.1 Uncertainty Range
Technical: 95% CI: [$209B, $418B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $209B and $418B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.42.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.43 Annual Infrastructure Damage to Education Facilities from Conflict
Value: $234B
Annual infrastructure damage to education facilities from conflict
Source: Brown Watson Costs of War - Environmental cost of war ($100B annually)
3.43.1 Uncertainty Range
Technical: 95% CI: [$164B, $328B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $164B and $328B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.43.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.44 Annual Infrastructure Damage to Energy Systems from Conflict
Value: $422B
Annual infrastructure damage to energy systems from conflict
Source: Brown Watson Costs of War - Environmental cost of war ($100B annually)
3.44.1 Uncertainty Range
Technical: 95% CI: [$295B, $590B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $295B and $590B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.44.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.45 Annual Infrastructure Damage to Healthcare Facilities from Conflict
Value: $166B
Annual infrastructure damage to healthcare facilities from conflict
Source: Brown Watson Costs of War - Environmental cost of war ($100B annually)
3.45.1 Uncertainty Range
Technical: 95% CI: [$116B, $232B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $116B and $232B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.45.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.46 Annual Infrastructure Damage to Transportation from Conflict
Value: $487B
Annual infrastructure damage to transportation from conflict
Source: Brown Watson Costs of War - Environmental cost of war ($100B annually)
3.46.1 Uncertainty Range
Technical: 95% CI: [$340B, $680B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $340B and $680B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.46.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.47 Annual Infrastructure Damage to Water Systems from Conflict
Value: $268B
Annual infrastructure damage to water systems from conflict
Source: Brown Watson Costs of War - Environmental cost of war ($100B annually)
3.47.1 Uncertainty Range
Technical: 95% CI: [$187B, $375B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $187B and $375B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.47.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.48 Annual Lives Saved by Medical Research Globally
Value: 4.20M lives/year
Annual lives saved by medical research globally
Source: ScienceDaily (2020) - Medical research lives saved annually (4.2 million)
3.48.1 Uncertainty Range
Technical: 95% CI: [3.00M lives/year, 6.00M lives/year] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between 3.00M lives/year and 6.00M lives/year (±36%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.48.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.49 Annual Lost Economic Growth from Military Spending Opportunity Cost
Value: $2.72T
Annual lost economic growth from military spending opportunity cost
Source: SIPRI (2016) - 36:1 disparity ratio of spending on weapons over cures
3.49.1 Uncertainty Range
Technical: 95% CI: [$1.90T, $3.80T] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $1.90T and $3.80T (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.49.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.50 Annual Lost Productivity from Conflict Casualties
Value: $300B
Annual lost productivity from conflict casualties
Source: Think by Numbers (2021) - Lost human capital due to war ($270B annually)
3.50.1 Uncertainty Range
Technical: 95% CI: [$210B, $420B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $210B and $420B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.50.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.51 Annual PTSD and Mental Health Costs from Conflict
Value: $232B
Annual PTSD and mental health costs from conflict
Source: PubMed - Psychological impact of war cost ($100B annually)
3.51.1 Uncertainty Range
Technical: 95% CI: [$162B, $325B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $162B and $325B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.51.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.52 Annual Refugee Support Costs
Value: $150B
Annual refugee support costs (108.4M refugees × $1,384/year)
Source: CGDev (2024) - UNHCR average refugee support cost
3.52.1 Uncertainty Range
Technical: 95% CI: [$105B, $210B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $105B and $210B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.52.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.53 Annual Trade Disruption Costs from Currency Instability
Value: $57.4B
Annual trade disruption costs from currency instability
Source: World Bank - World Bank trade disruption cost from conflict
3.53.1 Uncertainty Range
Technical: 95% CI: [$40B, $80B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $40B and $80B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.53.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.54 Annual Trade Disruption Costs from Energy Price Volatility
Value: $125B
Annual trade disruption costs from energy price volatility
Source: World Bank - World Bank trade disruption cost from conflict
3.54.1 Uncertainty Range
Technical: 95% CI: [$87B, $175B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $87B and $175B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.54.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.55 Annual Trade Disruption Costs from Shipping Disruptions
Value: $247B
Annual trade disruption costs from shipping disruptions
Source: World Bank - World Bank trade disruption cost from conflict
3.55.1 Uncertainty Range
Technical: 95% CI: [$173B, $346B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $173B and $346B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.55.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.56 Annual Trade Disruption Costs from Supply Chain Disruptions
Value: $187B
Annual trade disruption costs from supply chain disruptions
Source: World Bank - World Bank trade disruption cost from conflict
3.56.1 Uncertainty Range
Technical: 95% CI: [$131B, $262B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $131B and $262B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.56.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.57 Annual Veteran Healthcare Costs
Value: $200B
Annual veteran healthcare costs (20-year projected)
Source: VA (2026) - Veteran healthcare cost projections
3.57.1 Uncertainty Range
Technical: 95% CI: [$140B, $280B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $140B and $280B (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.57.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.58 Annual Global Spending on Clinical Trials
Value: $83B
Annual global spending on clinical trials (Total: Government + Industry)
Source: Research and Markets (2024) - Global clinical trials market 2024
3.58.1 Uncertainty Range
Technical: 95% CI: [$60B, $110B] • Distribution: Lognormal (SE: $12.5B)
What this means: There’s significant uncertainty here. The true value likely falls between $60B and $110B (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.58.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.59 Daily Deaths from Curable Diseases Globally
Value: 150k deaths/day
Daily deaths from all diseases and aging globally
3.59.1 Uncertainty Range
Technical: 95% CI: [120k deaths/day, 180k deaths/day] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between 120k deaths/day and 180k deaths/day (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.59.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.60 Global Daily Deaths from Disease and Aging
Value: 150k deaths/day
Total global deaths per day from all disease and aging (WHO Global Burden of Disease 2024)
Source: World Health Organization (2024) - WHO Global Health Estimates 2024
3.60.1 Uncertainty Range
Technical: Distribution: Normal (SE: 7.50k deaths/day)
3.60.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed
3.61 Global Annual Direct Medical Costs of Disease
Value: $9.90T
Direct medical costs of disease globally (treatment, hospitalization, medication)
3.61.1 Uncertainty Range
Technical: 95% CI: [$7T, $14T] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $7T and $14T (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.61.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.62 Global Annual Economic Value of Human Life Lost to Disease
Value: $94.2T
Economic value of human life lost to disease annually (mortality valuation)
3.62.1 Uncertainty Range
Technical: 95% CI: [$66T, $132T] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $66T and $132T (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.62.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.63 Global Annual Productivity Loss from Disease
Value: $5T
Annual productivity loss from disease globally (absenteeism, reduced output)
3.63.1 Uncertainty Range
Technical: 95% CI: [$3.50T, $7T] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $3.50T and $7T (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.63.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.64 Annual Global Government Spending on Clinical Trials
Value: $4.50B
Annual global government spending on interventional clinical trials (~5-10% of total)
3.64.1 Uncertainty Range
Technical: 95% CI: [$3B, $6B] • Distribution: Lognormal (SE: $1B)
What this means: There’s significant uncertainty here. The true value likely falls between $3B and $6B (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.64.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.65 Global Household Wealth
Value: $454T
Total global household wealth (2022/2023 estimate)
Source: Credit Suisse/UBS (2023) - Credit Suisse Global Wealth Report 2023
✓ High confidence
3.66 Global Life Expectancy (2024)
Value: 79 years
Global life expectancy (2024)
Source: World Health Organization (2024) - WHO Global Health Estimates 2024
3.66.1 Uncertainty Range
Technical: Distribution: Normal (SE: 2 years)
3.66.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed • Updated 2024
3.67 Global Government Medical Research Spending
Value: $67.5B
Global government medical research spending
Source: See component country budgets: - Global government medical research spending ($67.5B, 2023–2024)
3.67.1 Uncertainty Range
Technical: 95% CI: [$54B, $81B] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between $54B and $81B (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.67.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.68 Global Military Spending in 2024
Value: $2.72T
Global military spending in 2024
Source: SIPRI (2025) - Global military spending ($2.72T, 2024)
3.68.1 Uncertainty Range
Technical: 95% CI: [$2.45T, $2.99T] • Distribution: Lognormal (SE: $272B)
What this means: This estimate has moderate uncertainty. The true value likely falls between $2.45T and $2.99T (±10%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.68.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.69 Global Population in 2024
Value: 8.00B of people
Global population in 2024
Source: UN (2022) - Global population reaches 8 billion
3.69.1 Uncertainty Range
Technical: 95% CI: [7.80B of people, 8.20B of people] • Distribution: Lognormal
What this means: We’re quite confident in this estimate. The true value likely falls between 7.80B of people and 8.20B of people (±2%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.69.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.70 Critical Mass Threshold for Social Change
Value: 3.5%
Critical mass threshold for social change (3.5% rule)
Source: Harvard Kennedy School (2020) - 3.5% participation tipping point
3.70.1 Uncertainty Range
Technical: 95% CI: [2.5%, 4.5%] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between 2.5% and 4.5% (±29%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.70.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.71 Annual Global Spending on Symptomatic Disease Treatment
Value: $8.20T
Annual global spending on symptomatic disease treatment
3.71.1 Uncertainty Range
Technical: 95% CI: [$6.50T, $10T] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between $6.50T and $10T (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.71.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.72 Estimated Total Economic Impact of Human Genome Project
Value: $1T
Estimated total economic impact of Human Genome Project
Source: NHGRI (2003) - Human Genome Project and CRISPR Discovery
✓ High confidence
3.73 Human Interactome Targeted by Drugs
Value: 12%
Percentage of human interactome (protein-protein interactions) targeted by drugs
Source: PMC (2023) - Only ~12% of human interactome targeted
✓ High confidence
3.74 ICD-10 Total Codes
Value: 14.0k codes
Total ICD-10 diagnostic codes for human diseases and conditions
Source: WHO (2019) - ICD-10 Code Count (~14,000)
✓ High confidence
3.75 Life Extension from Treaty Research Acceleration
Value: 20 years
Expected years of life extension from 1% treaty research acceleration (25x trial capacity). Bounds: 0 (complete failure) to ~150 (accident-limited lifespan minus current). Lognormal distribution allows for breakthrough scenarios.
Source: Wikipedia - Longevity Escape Velocity (LEV) - Maximum Human Life Extension Potential
3.75.1 Uncertainty Range
Technical: 95% CI: [5 years, 100 years] • Distribution: Lognormal
What this means: This estimate is highly uncertain. The true value likely falls between 5 years and 100 years (±238%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.75.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
? Low confidence
3.76 Maximum Annual Lobbyist Salary Range
Value: $2M
Maximum annual lobbyist salary range
Source: OpenSecrets - Lobbyist statistics for Washington D.C.
✓ High confidence
3.77 Minimum Annual Lobbyist Salary Range
Value: $500K
Minimum annual lobbyist salary range
Source: OpenSecrets - Lobbyist statistics for Washington D.C.
✓ High confidence
3.78 Return on Investment from Measles Vaccination Programs
Value: 14 ratio
Return on investment from measles (MMR) vaccination programs
Source: MDPI Vaccines (2024) - Measles Vaccination ROI
✓ High confidence
3.79 Annual Productivity Loss per Capita from Mental Health Issues
Value: $2K
Annual productivity loss per capita from mental health issues (beyond treatment costs)
Source: World Health Organization (2022) - Mental health global burden
✓ High confidence
3.80 NIH Clinical Trials Spending Percentage
Value: 3.3%
Percentage of NIH budget spent on clinical trials (3.3%)
Source: Bentley et al. (2023) - NIH spending on clinical trials: ~3.3%
3.80.1 Uncertainty Range
Technical: 95% CI: [2%, 5%] • Distribution: Beta
What this means: There’s significant uncertainty here. The true value likely falls between 2% and 5% (±45%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The beta distribution means values are bounded and can skew toward one end.
3.80.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.81 Oxford RECOVERY Trial Duration
Value: 3 months
Oxford RECOVERY trial duration (found life-saving treatment in 3 months)
Source: Manhattan Institute - RECOVERY trial 82× cost reduction
3.81.1 Uncertainty Range
Technical: Distribution: Fixed
3.81.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.82 Patient Willingness to Participate in Clinical Trials
Value: 44.8%
Patient willingness to participate in drug trials (44.8% in surveys, 88% when actually approached)
Source: Trials - Patient willingness to participate in clinical trials
~ Medium confidence
3.83 Pharma Drug Development Cost (Current System)
Value: $2.60B
Average cost to develop one drug in current system
Source: Tufts CSDD - Cost of drug development
3.83.1 Uncertainty Range
Technical: 95% CI: [$1.50B, $4B] • Distribution: Lognormal (SE: $500M)
What this means: There’s significant uncertainty here. The true value likely falls between $1.50B and $4B (±48%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.83.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed
3.84 Pharma Average Drug Revenue (Current System)
Value: $6.70B
Median lifetime revenue per successful drug (study of 361 FDA-approved drugs 1995-2014, median follow-up 13.2 years)
Source: Value in Health - Average lifetime revenue per successful drug
✓ High confidence • 📊 Peer-reviewed
3.85 Pharma ROI (Current System)
Value: 1.2%
ROI for pharma R&D (2022 historic low from Deloitte study of top 20 pharma companies, down from 6.8% in 2021, recovered to 5.9% in 2024)
Source: Deloitte (2025) - Pharmaceutical R&D return on investment (ROI)
✓ High confidence • 📊 Peer-reviewed
3.86 Pharma Drug Success Rate (Current System)
Value: 10%
Percentage of drugs that reach market in current system
Source: Nature Reviews Drug Discovery (2016) - Drug trial success rate from Phase I to approval
✓ High confidence • 📊 Peer-reviewed
3.87 Phase I-Passed Compounds Globally
Value: 7.50k compounds
Investigational compounds that have passed Phase I globally (midpoint of 5,000-10,000 range)
3.87.1 Uncertainty Range
Technical: 95% CI: [5.00k compounds, 10.0k compounds] • Distribution: Uniform
What this means: There’s significant uncertainty here. The true value likely falls between 5.00k compounds and 10.0k compounds (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The uniform distribution means any value in the range is equally likely.
3.87.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.88 Phase I Safety Trial Duration
Value: 2.3 years
Phase I safety trial duration
✓ High confidence • 📊 Peer-reviewed • Updated 2021
3.89 Phase 3 Trial Total Cost (Minimum)
Value: $20M
Phase 3 trial total cost (minimum)
Source: SofproMed - Phase 3 cost per trial range
✓ High confidence
3.90 Return on Investment from Sustaining Polio Vaccination Assets and Integrating into Expanded Immunization Programs
Value: 39 ratio
Return on investment from sustaining polio vaccination assets and integrating into expanded immunization programs
Source: WHO (2019) - Polio Vaccination ROI
✓ High confidence
3.91 Political Success Probability
Value: 1%
Estimated probability of treaty ratification and sustained implementation. Central estimate 1% is ultra-conservative. This assumes 99% chance of failure.
Source: ICRC (1997) - International Campaign to Ban Landmines (ICBL) - Ottawa Treaty (1997)
3.91.1 Uncertainty Range
Technical: 95% CI: [0.1%, 10%] • Distribution: Beta (SE: 2%)
What this means: This estimate is highly uncertain. The true value likely falls between 0.1% and 10% (±495%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The beta distribution means values are bounded and can skew toward one end.
3.91.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
? Low confidence
3.92 Post-1962 Drug Approval Reduction
Value: 70%
Reduction in new drug approvals after 1962 Kefauver-Harris Amendment (70% drop from 43→17 drugs/year)
Source: Think by Numbers - Post-1962 drop in new drug approvals
✓ High confidence • Updated 1962-1970
3.93 Percentage Military Spending Cut After WW2
Value: 30%
Percentage military spending cut after WW2 (historical precedent)
Source: Wikipedia (2020) - US military spending reduction after WWII
✓ High confidence
3.94 Pre-1962 Drug Development Cost
Value: $50M
Pre-1962 drug development cost (documented range: $10-50M in 1950s-1960s)
Source: Think by Numbers (1962) - Pre-1962 drug development costs and timeline
3.94.1 Uncertainty Range
Technical: 95% CI: [$10M, $50M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $10M and $50M (±40%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.94.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
~ Medium confidence
3.95 Pre-1962 Physician Count (Unverified)
Value: 144k physicians
Estimated physicians conducting real-world efficacy trials pre-1962 (unverified estimate)
Source: Think by Numbers (1966) - Pre-1962 physician-led clinical trials
? Low confidence
3.96 Total Number of Rare Diseases Globally
Value: 7.00k diseases
Total number of rare diseases globally
Source: GAO (2025) - 95% of diseases have no effective treatment
✓ High confidence
3.97 Recovery Trial Cost per Patient
Value: $500
RECOVERY trial cost per patient
Source: Oren Cass, Manhattan Institute (2023) - RECOVERY Trial Cost per Patient
3.97.1 Uncertainty Range
Technical: 95% CI: [$350, $700] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $350 and $700 (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.97.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.98 Mean Age of Preventable Death from Post-Safety Efficacy Delay
Value: 62 years
Mean age of preventable death from post-safety efficacy testing regulatory delay (Phase 2-4)
Source: World Health Organization (2024) - WHO Global Health Estimates 2024
3.98.1 Uncertainty Range
Technical: Distribution: Normal (SE: 3 years)
3.98.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
~ Medium confidence • 📊 Peer-reviewed
3.99 Pre-Death Suffering Period During Post-Safety Efficacy Delay
Value: 6 years
Pre-death suffering period during post-safety efficacy testing delay (average years lived with untreated condition while awaiting Phase 2-4 completion)
Source: World Health Organization (2024) - WHO Global Health Estimates 2024
3.99.1 Uncertainty Range
Technical: 95% CI: [4 years, 9 years] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between 4 years and 9 years (±42%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.99.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
~ Medium confidence • 📊 Peer-reviewed
3.100 Return on Investment from Smallpox Eradication Campaign
Value: 280 ratio
Return on investment from smallpox eradication campaign
Source: CSIS - Smallpox Eradication ROI
✓ High confidence
3.101 Total Economic Benefit from Smallpox Eradication Campaign
Value: $1.42B
Total economic benefit from smallpox eradication campaign
Source: CSIS - Smallpox Eradication ROI
✓ High confidence
3.102 Estimated Annual Global Economic Benefit from Smoking Cessation Programs
Value: $12B
Estimated annual global economic benefit from smoking cessation programs
Source: PMC (2012) - Contribution of smoking reduction to life expectancy gains
✓ High confidence
3.103 Standard Economic Value per QALY
Value: $150K
Standard economic value per QALY
Source: ICER (2024) - Value per QALY (standard economic value)
3.103.1 Uncertainty Range
Technical: Distribution: Normal (SE: $30K)
3.103.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.104 Standard QALYs per Life Saved
Value: 35 QALYs/life
Standard QALYs per life saved (WHO life tables)
Source: ICER (2024) - Value per QALY (standard economic value)
3.104.1 Uncertainty Range
Technical: Distribution: Normal (SE: 7 QALYs/life)
3.104.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.105 Annual Cost of Sugar Subsidies per Person
Value: $10
Annual cost of sugar subsidies per person
Source: GAO - Annual cost of U.S. sugar subsidies
✓ High confidence
3.106 Switzerland’s Defense Spending as Percentage of GDP
Value: 0.7%
Switzerland’s defense spending as percentage of GDP (0.7%)
Source: World Bank - Swiss military budget as percentage of GDP
✓ High confidence
3.107 Switzerland GDP per Capita
Value: $93K
Switzerland GDP per capita
Source: World Bank - Switzerland vs. US GDP per capita comparison
✓ High confidence
3.108 Deaths from 9/11 Terrorist Attacks
Value: 3.00k deaths
Deaths from 9/11 terrorist attacks
Source: Cato Institute - Chance of dying from terrorism statistic
✓ High confidence
3.109 Thalidomide Cases Worldwide
Value: 15.0k cases
Total thalidomide birth defect cases worldwide (1957-1962)
Source: Wikipedia - Thalidomide scandal: worldwide cases and mortality
3.109.1 Uncertainty Range
Technical: 95% CI: [10.0k cases, 20.0k cases] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between 10.0k cases and 20.0k cases (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.109.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
~ Medium confidence
3.110 Thalidomide Disability Weight
Value: 0.4 ratio
Disability weight for thalidomide survivors (limb deformities, organ damage)
Source: PLOS One (2019) - Health and quality of life of Thalidomide survivors as they age
3.110.1 Uncertainty Range
Technical: 95% CI: [0.32 ratio, 0.48 ratio] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between 0.32 ratio and 0.48 ratio (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.110.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
~ Medium confidence
3.111 Thalidomide Mortality Rate
Value: 40%
Mortality rate for thalidomide-affected infants (died within first year)
Source: Wikipedia - Thalidomide scandal: worldwide cases and mortality
3.111.1 Uncertainty Range
Technical: 95% CI: [35%, 45%] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between 35% and 45% (±13%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.111.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.112 Thalidomide Survivor Lifespan
Value: 60 years
Average lifespan for thalidomide survivors
Source: PLOS One (2019) - Health and quality of life of Thalidomide survivors as they age
3.112.1 Uncertainty Range
Technical: 95% CI: [50 years, 70 years] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between 50 years and 70 years (±17%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.112.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
~ Medium confidence
3.113 US Population Share 1960
Value: 6%
US share of world population in 1960
Source: US Census Bureau - Historical world population estimates
3.113.1 Uncertainty Range
Technical: 95% CI: [5.5%, 6.5%] • Distribution: Lognormal
What this means: We’re quite confident in this estimate. The true value likely falls between 5.5% and 6.5% (±8%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.113.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.114 Phase 3 Cost per Patient
Value: $80K
Phase 3 cost per patient (median)
Source: JAMA Internal Medicine - Phase 3 cost per patient
✓ High confidence
3.115 Example Phase 3 Trial Cost per Patient
Value: $48K
Example Phase 3 trial cost per patient ($48K)
Source: ProRelix Research - Clinical trial cost per patient (traditional Phase III)
✓ High confidence
3.116 FDA Cited Phase 3 Cost per Patient
Value: $41K
FDA cited Phase 3 cost per patient ($41K)
Source: FDA Study via NCBI - Trial Costs, FDA Study
✓ High confidence
3.117 Traditional FDA Drug Development Timeline
Value: 17 years
Traditional FDA drug development timeline
Source: Drugs.com - FDA drug approval timeline
✓ High confidence
3.118 Cost Reduction Factor Demonstrated by Recovery Trial
Value: 82 ratio
Cost reduction factor demonstrated by RECOVERY trial
Source: Manhattan Institute - RECOVERY trial 82× cost reduction
3.118.1 Uncertainty Range
Technical: 95% CI: [20 ratio, 150 ratio] • Distribution: Lognormal (SE: 20 ratio)
What this means: This estimate is highly uncertain. The true value likely falls between 20 ratio and 150 ratio (±79%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.118.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.119 Typical CEO Hourly Rate
Value: $10K
Typical CEO hourly rate
Source: EPI (2024) - CEO compensation
✓ High confidence
3.120 US Alzheimer’s Annual Cost
Value: $355B
Annual US cost of Alzheimer’s disease (direct and indirect)
Source: WHO (2019) - Annual global economic burden of Alzheimer’s and other dementias
3.120.1 Uncertainty Range
Technical: 95% CI: [$302B, $408B] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between $302B and $408B (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.120.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed
3.121 US Cancer Annual Cost
Value: $208B
Annual US cost of cancer (direct and indirect)
Source: JAMA Oncology (2020) - Annual global economic burden of cancer
3.121.1 Uncertainty Range
Technical: 95% CI: [$177B, $239B] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between $177B and $239B (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.121.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed
3.122 US Annual Chronic Disease Spending
Value: $4.10T
US annual chronic disease spending
Source: CDC - U.S. chronic disease healthcare spending
3.122.1 Uncertainty Range
Technical: 95% CI: [$3.30T, $5T] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between $3.30T and $5T (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.122.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.123 US Diabetes Annual Cost
Value: $327B
Annual US cost of diabetes (direct and indirect)
Source: Diabetes Care - Annual global economic burden of diabetes
3.123.1 Uncertainty Range
Technical: 95% CI: [$278B, $376B] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between $278B and $376B (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.123.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed
3.124 US Heart Disease Annual Cost
Value: $363B
Annual US cost of heart disease and stroke (direct and indirect)
Source: Int’l Journal of Cardiology (2050) - Annual global economic burden of heart disease
3.124.1 Uncertainty Range
Technical: 95% CI: [$309B, $417B] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between $309B and $417B (±15%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.124.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence • 📊 Peer-reviewed
3.125 US Mental Health Costs
Value: $350B
US mental health costs (treatment + productivity loss)
Source: World Health Organization (2022) - Mental health global burden
3.125.1 Uncertainty Range
Technical: 95% CI: [$260B, $450B] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $260B and $450B (±27%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.125.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.126 US Military Spending as Percentage of GDP
Value: 3.5%
US military spending as percentage of GDP (2024)
Source: Statista (2024) - US military budget as percentage of GDP
✓ High confidence
3.127 US Population in 2024
Value: 335M people
US population in 2024
Source: US Census Bureau (2024) - Number of registered or eligible voters in the U.S.
3.127.1 Uncertainty Range
Technical: 95% CI: [330M people, 340M people] • Distribution: Lognormal
What this means: We’re quite confident in this estimate. The true value likely falls between 330M people and 340M people (±1%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
3.127.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.128 Value of Statistical Life
Value: $10M
Value of Statistical Life (conservative estimate)
Source: DOT (2024) - DOT Value of Statistical Life ($13.6M)
3.128.1 Uncertainty Range
Technical: 95% CI: [$5M, $15M] • Distribution: Gamma (SE: $3M)
What this means: There’s significant uncertainty here. The true value likely falls between $5M and $15M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The gamma distribution means values follow a specific statistical pattern.
3.128.2 Input Distribution
This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.
✓ High confidence
3.129 Vitamin A Supplementation Cost per DALY
Value: $37
Cost per DALY for vitamin A supplementation programs (India: $23-50; Africa: $40-255; wide variation by region and baseline VAD prevalence). Using India midpoint as conservative estimate.
Source: PLOS ONE (2010) - Cost per DALY for Vitamin A Supplementation
~ Medium confidence
3.130 Estimated Annual Global Economic Benefit from Water Fluoridation Programs
Value: $800M
Estimated annual global economic benefit from water fluoridation programs
Source: UN News (2014) - Clean Water & Sanitation (LMICs) ROI
✓ High confidence
3.131 Return on Investment from Water Fluoridation Programs
Value: 23 ratio
Return on investment from water fluoridation programs
Source: UN News (2014) - Clean Water & Sanitation (LMICs) ROI
✓ High confidence
3.132 Cost-Effectiveness Threshold ($50,000/QALY)
Value: $50K
Cost-effectiveness threshold widely used in US health economics ($50,000/QALY, from 1980s dialysis costs)
Source: PMC - Cost-effectiveness threshold ($50,000/QALY)
✓ High confidence
3.133 Percentage of Workforce Experiencing Productivity Loss from Chronic Illness
Value: 28%
Percentage of workforce experiencing productivity loss from chronic illness (28%)
Source: Integrated Benefits Institute (2024) - Chronic illness workforce productivity loss
✓ High confidence
4 Core Definitions
Fundamental parameters and constants used throughout the analysis.
4.1 Approved Drug-Disease Pairings
Value: 1.75k pairings
Unique approved drug-disease pairings (FDA-approved uses, midpoint of 1,500-2,000 range)
4.1.1 Uncertainty Range
Technical: 95% CI: [1.50k pairings, 2.00k pairings] • Distribution: Uniform
What this means: This estimate has moderate uncertainty. The true value likely falls between 1.50k pairings and 2.00k pairings (±14%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The uniform distribution means any value in the range is equally likely.
Core definition
4.2 Celebrity and Influencer Endorsements
Value: $15M
Celebrity and influencer endorsements
Core definition
4.3 Community Organizing and Ambassador Program Budget
Value: $30M
Community organizing and ambassador program budget
Core definition
4.4 Contingency Fund for Unexpected Costs
Value: $50M
Contingency fund for unexpected costs
4.4.1 Uncertainty Range
Technical: 95% CI: [$30M, $80M] • Distribution: Uniform
What this means: There’s significant uncertainty here. The true value likely falls between $30M and $80M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The uniform distribution means any value in the range is equally likely.
Core definition
4.5 Defense Industry Conversion Program
Value: $50M
Defense industry conversion program
4.5.1 Uncertainty Range
Technical: 95% CI: [$40M, $70M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $40M and $70M (±30%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.6 Budget for Co-Opting Defense Industry Lobbyists
Value: $50M
Budget for co-opting defense industry lobbyists
Core definition
4.7 Healthcare Industry Alignment and Partnerships
Value: $35M
Healthcare industry alignment and partnerships
Core definition
4.8 Campaign Operational Infrastructure
Value: $20M
Campaign operational infrastructure
Core definition
4.9 AI-Assisted Legal Work Budget
Value: $50M
AI-assisted legal work budget
Core definition
4.10 Legal Defense Fund
Value: $20M
Legal defense fund
Core definition
4.11 Legal Drafting and Compliance Work
Value: $60M
Legal drafting and compliance work
4.11.1 Uncertainty Range
Technical: 95% CI: [$50M, $80M] • Distribution: Lognormal
What this means: This estimate has moderate uncertainty. The true value likely falls between $50M and $80M (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.12 EU Lobbying Campaign Budget
Value: $40M
EU lobbying campaign budget
Core definition
4.13 G20 Countries Lobbying Budget
Value: $35M
G20 countries lobbying budget
Core definition
4.14 US Lobbying Campaign Budget
Value: $50M
US lobbying campaign budget
Core definition
4.15 Maximum Mass Media Campaign Budget
Value: $1B
Maximum mass media campaign budget
Core definition
4.16 Minimum Mass Media Campaign Budget
Value: $500M
Minimum mass media campaign budget
Core definition
4.17 Opposition Research and Rapid Response
Value: $25M
Opposition research and rapid response
Core definition
4.18 Phase 1 Campaign Budget
Value: $200M
Phase 1 campaign budget (Foundation, Year 1)
Core definition
4.19 Phase 2 Campaign Budget
Value: $500M
Phase 2 campaign budget (Scale & Momentum, Years 2-3)
Core definition
4.20 Pilot Program Testing in Small Countries
Value: $30M
Pilot program testing in small countries
Core definition
4.21 Voting Platform and Technology Development
Value: $35M
Voting platform and technology development
4.21.1 Uncertainty Range
Technical: 95% CI: [$25M, $50M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $25M and $50M (±36%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.23 Scaling Preparation and Blueprints
Value: $30M
Scaling preparation and blueprints
Core definition
4.24 Campaign Core Team Staff Budget
Value: $40M
Campaign core team staff budget
Core definition
4.25 Super PAC Campaign Expenditures
Value: $30M
Super PAC campaign expenditures
Core definition
4.26 Tech Industry Partnerships and Infrastructure
Value: $25M
Tech industry partnerships and infrastructure
Core definition
4.27 Post-Victory Treaty Implementation Support
Value: $40M
Post-victory treaty implementation support
4.27.1 Uncertainty Range
Technical: 95% CI: [$30M, $55M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $30M and $55M (±31%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.29 Annual Cost of Unpaid Caregiving
Value: $6K
Annual cost of unpaid caregiving (replacement cost method)
Core definition
4.30 Childhood Vaccination Cost per DALY (Estimated)
Value: $30
Estimated cost per DALY for US childhood vaccination programs. Note: US cost-effectiveness studies primarily use cost per QALY (Quality-Adjusted Life Year) rather than cost per DALY. This estimate is derived from program costs and benefits for comparison purposes only.
Core definition
4.31 Concentrated Interest Sector Market Cap
Value: $5T
Estimated combined market capitalization of concentrated interest opposition (defense, fossil fuel, etc.)
Core definition
4.32 Current Patient Participation Rate in Clinical Trials
Value: 0.0792%
Current patient participation rate in clinical trials (0.08% = 1.9M participants / 2.4B disease patients)
Core definition
4.33 Days Per Year
Value: 365
Core definition
4.34 Mid-Range Funding for Commercial Dct Platform
Value: $500M
Mid-range funding for commercial DCT platform
Core definition
4.35 Percentage of Budget Defense Sector Keeps Under 1% treaty
Value: 99%
Percentage of budget defense sector keeps under 1% treaty
Core definition
4.36 Years to Reach Full Decentralized Framework for Drug Assessment Adoption
Value: 5 years
Years to reach full Decentralized Framework for Drug Assessment adoption
Core definition
4.37 Decentralized Framework for Drug Assessment Core Platform Annual OPEX
Value: $18.9M
Decentralized Framework for Drug Assessment core platform annual opex (midpoint of $11-26.5M)
4.37.1 Uncertainty Range
Technical: 95% CI: [$11M, $26.5M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $11M and $26.5M (±41%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.38 Decentralized Framework for Drug Assessment Core Platform Build Cost
Value: $40M
Decentralized Framework for Drug Assessment core platform build cost
4.38.1 Uncertainty Range
Technical: 95% CI: [$25M, $65M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $25M and $65M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.39 Decentralized Framework for Drug Assessment Community Support Costs
Value: $2M
Decentralized Framework for Drug Assessment community support costs
4.39.1 Uncertainty Range
Technical: 95% CI: [$1M, $3M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $1M and $3M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.40 Decentralized Framework for Drug Assessment Infrastructure Costs
Value: $8M
Decentralized Framework for Drug Assessment infrastructure costs (cloud, security)
4.40.1 Uncertainty Range
Technical: 95% CI: [$5M, $12M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $5M and $12M (±44%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.41 Decentralized Framework for Drug Assessment Overhead Percentage of Treaty Funding
Value: 0.147%
Percentage of treaty funding allocated to Decentralized Framework for Drug Assessment platform overhead
Core definition
4.42 Decentralized Framework for Drug Assessment Maintenance Costs
Value: $15M
Decentralized Framework for Drug Assessment maintenance costs
4.42.1 Uncertainty Range
Technical: 95% CI: [$10M, $22M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $10M and $22M (±40%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.43 Decentralized Framework for Drug Assessment Regulatory Coordination Costs
Value: $5M
Decentralized Framework for Drug Assessment regulatory coordination costs
4.43.1 Uncertainty Range
Technical: 95% CI: [$3M, $8M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $3M and $8M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.44 Decentralized Framework for Drug Assessment Staff Costs
Value: $10M
Decentralized Framework for Drug Assessment staff costs (minimal, AI-assisted)
4.44.1 Uncertainty Range
Technical: 95% CI: [$7M, $15M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $7M and $15M (±40%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.45 Decentralized Framework for Drug Assessment Target Cost per Patient in USD
Value: $1K
Target cost per patient in USD (same as DFDA_TARGET_COST_PER_PATIENT but in dollars)
Core definition
4.46 Decentralized Framework for Drug Assessment One-Time Build Cost
Value: $40M
Decentralized Framework for Drug Assessment one-time build cost (central estimate)
Core definition
4.47 Decentralized Framework for Drug Assessment One-Time Build Cost (Maximum)
Value: $46M
Decentralized Framework for Drug Assessment one-time build cost (high estimate)
Core definition
4.48 DIH Broader Initiatives Annual OPEX
Value: $21.1M
DIH broader initiatives annual opex (medium case)
4.48.1 Uncertainty Range
Technical: 95% CI: [$14M, $32M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $14M and $32M (±43%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.49 DIH Broader Initiatives Upfront Cost
Value: $230M
DIH broader initiatives upfront cost (medium case)
4.49.1 Uncertainty Range
Technical: 95% CI: [$150M, $350M] • Distribution: Lognormal
What this means: There’s significant uncertainty here. The true value likely falls between $150M and $350M (±44%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.50 Medical Research Percentage of Treaty Funding
Value: 80%
Percentage of treaty funding allocated to medical research (after bond payouts and IAB incentives)
Core definition
4.51 Patient Trial Subsidies Percentage of Treaty Funding
Value: 79.9%
Percentage of treaty funding going directly to patient trial subsidies
Core definition
4.53 Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths
Value: 18.4k ratio
Ratio of annual disease deaths to 9/11 terrorism deaths
Core definition
4.54 Ratio of Annual Disease Deaths to War Deaths
Value: 225 ratio
Ratio of annual disease deaths to war deaths
Core definition
4.55 Lifetime Benefit for Age 30 Baseline Scenario
Value: $4.30M
Lifetime benefit for age 30 baseline scenario ($4.3M)
Core definition
4.56 Eventually Avoidable Death Percentage
Value: 92.6%
Percentage of deaths that are eventually avoidable with sufficient biomedical research and technological advancement
Core definition
4.57 Minimum Investment for Family Offices
Value: $5M
Minimum investment for family offices
Core definition
4.59 Hours Per Day
Value: 24
Core definition
4.60 Hours Per Year
Value: 8.76k
Core definition
4.61 Bootstrap Campaign Cost (Base Case)
Value: $100M
Base case estimate for bootstrap campaign cost
Core definition
4.62 Bootstrap Campaign Cost (Conservative)
Value: $200M
Conservative estimate for bootstrap campaign cost
Core definition
4.63 Bootstrap Campaign Cost (Optimistic)
Value: $50M
Optimistic estimate for bootstrap campaign cost
Core definition
4.64 IAB Mechanism Annual Cost (High Estimate)
Value: $750M
Estimated annual cost of the IAB mechanism (high-end estimate including regulatory defense)
4.64.1 Uncertainty Range
Technical: 95% CI: [$160M, $750M]
What this means: There’s significant uncertainty here. The true value likely falls between $160M and $750M (±39%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
Core definition
4.65 IAB Political Incentive Funding Percentage
Value: 10%
Percentage of treaty funding allocated to Incentive Alignment Bond mechanism for political incentives (independent expenditures/PACs, post-office fellowships, Public Good Score infrastructure)
4.65.1 Uncertainty Range
Technical: Distribution: Fixed
Core definition
4.66 Minimum Investment for Institutional Investors
Value: $10M
Minimum investment for institutional investors
Core definition
4.67 Maximum Bond Investment for Lobbyist Incentives
Value: $20M
Maximum bond investment for lobbyist incentives
Core definition
4.68 Minutes Per Hour
Value: 60
Core definition
4.69 Months Per Year
Value: 12
Core definition
4.70 Standard Discount Rate for NPV Analysis
Value: 3%
Standard discount rate for NPV analysis (3% annual, social discount rate)
4.70.1 Uncertainty Range
Technical: Distribution: Fixed
Core definition
4.71 Standard Time Horizon for NPV Analysis
Value: 10 years
Standard time horizon for NPV analysis
4.71.1 Uncertainty Range
Technical: Distribution: Fixed
Core definition
4.72 Direct Fiscal Savings from 1% Military Spending Reduction
Value: $27.2B
Direct fiscal savings from 1% military spending reduction (high confidence)
Core definition
4.73 Pre-1962 Validation Years
Value: 77 years
Years of empirical validation for physician-led pragmatic trials (1883-1960)
Core definition
4.74 Safe Compounds Available for Testing
Value: 9.50k compounds
Total safe compounds available for repurposing (FDA-approved + GRAS substances, midpoint of 7,000-12,000 range)
4.74.1 Uncertainty Range
Technical: 95% CI: [7.00k compounds, 12.0k compounds] • Distribution: Uniform
What this means: There’s significant uncertainty here. The true value likely falls between 7.00k compounds and 12.0k compounds (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The uniform distribution means any value in the range is equally likely.
Core definition
4.75 Seconds Per Minute
Value: 60
Core definition
4.76 Seconds Per Year
Value: 31.5M
Core definition
4.77 Tested Drug-Disease Relationships
Value: 32.5k relationships
Estimated drug-disease relationships actually tested (approved uses + repurposed + failed trials, midpoint of 15,000-50,000 range)
4.77.1 Uncertainty Range
Technical: 95% CI: [15.0k relationships, 50.0k relationships] • Distribution: Lognormal
What this means: This estimate is highly uncertain. The true value likely falls between 15.0k relationships and 50.0k relationships (±54%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.78 Total Words in the Book
Value: 171k words
Total words in the book
Core definition
4.79 Annual Funding from 1% of Global Military Spending Redirected to DIH
Value: $27.2B
Annual funding from 1% of global military spending redirected to DIH
Core definition
4.80 Political Lobbying Campaign: Direct Lobbying, Super Pacs, Opposition Research, Staff, Legal/Compliance
Value: $650M
Political lobbying campaign: direct lobbying (US/EU/G20), Super PACs, opposition research, staff, legal/compliance. Budget exceeds combined pharma ($300M/year) and military-industrial complex ($150M/year) lobbying to ensure competitive positioning. Referendum relies on grassroots mobilization and earned media, while lobbying requires matching or exceeding opposition spending for political viability.
4.80.1 Uncertainty Range
Technical: 95% CI: [$325M, $1.30B] • Distribution: Lognormal
What this means: This estimate is highly uncertain. The true value likely falls between $325M and $1.30B (±75%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.81 Global Referendum Campaign: Ads, Media, Partnerships, Staff, Legal/Compliance
Value: $300M
Global referendum campaign (get 208M votes): ads, media, partnerships, staff, legal/compliance
4.81.1 Uncertainty Range
Technical: 95% CI: [$180M, $500M] • Distribution: Lognormal
What this means: This estimate is highly uncertain. The true value likely falls between $180M and $500M (±53%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.82 Reserve Fund / Contingency Buffer
Value: $50M
Reserve fund / contingency buffer (5% of total campaign cost). Conservative estimate uses 5% given transparent budget allocation and predictable referendum/lobbying costs, though industry standard is 10-20% for complex campaigns. Upper confidence bound of $100M (10%) reflects potential for unforeseen legal challenges, opposition response, or regulatory delays.
4.82.1 Uncertainty Range
Technical: 95% CI: [$20M, $100M] • Distribution: Lognormal
What this means: This estimate is highly uncertain. The true value likely falls between $20M and $100M (±80%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).
Core definition
4.83 Campaign Budget for Super Pacs and Political Lobbying
Value: $800M
Campaign budget for Super PACs and political lobbying
Core definition
4.84 Treaty Campaign Duration
Value: 4 years
Treaty campaign duration (3-5 year range, using midpoint)
4.84.1 Uncertainty Range
Technical: 95% CI: [3 years, 5 years] • Distribution: Triangular
What this means: This estimate has moderate uncertainty. The true value likely falls between 3 years and 5 years (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The triangular distribution means values cluster around a most-likely point but can range higher or lower.
Core definition
4.88 1% Reduction in Military Spending/War Costs from Treaty
Value: 1%
1% reduction in military spending/war costs from treaty
4.88.1 Uncertainty Range
Technical: Distribution: Fixed
Core definition
4.89 Decentralized Framework for Drug Assessment Trial Cost Reduction Percentage
Value: 50%
Trial cost reduction percentage (50% baseline, conservative)
4.89.1 Uncertainty Range
Technical: 95% CI: [40%, 65%] • Distribution: Beta
What this means: This estimate has moderate uncertainty. The true value likely falls between 40% and 65% (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The beta distribution means values are bounded and can skew toward one end.
Core definition
4.90 Trial-Relevant Diseases
Value: 1.00k diseases
Consolidated count of trial-relevant diseases worth targeting (after grouping ICD-10 codes)
4.90.1 Uncertainty Range
Technical: 95% CI: [800 diseases, 1.20k diseases] • Distribution: Uniform
What this means: This estimate has moderate uncertainty. The true value likely falls between 800 diseases and 1.20k diseases (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.
The uniform distribution means any value in the range is equally likely.
Core definition
4.91 Annual VICTORY Incentive Alignment Bond Payout
Value: $2.72B
Annual VICTORY Incentive Alignment Bond payout (treaty funding × bond percentage)
Core definition
4.92 Annual Return Percentage for VICTORY Incentive Alignment Bondholders
Value: 272%
Annual return percentage for VICTORY Incentive Alignment Bondholders
Core definition
4.93 Percentage of Captured Dividend Funding VICTORY Incentive Alignment Bonds
Value: 10%
Percentage of captured dividend funding VICTORY Incentive Alignment Bonds (10%)
4.93.1 Uncertainty Range
Technical: Distribution: Fixed
Core definition
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