Add three new sections estimating cumulative (since 1900) costs that the chapter currently lacks:
- Cumulative property/environmental destruction
- Professionals killed (scientists, doctors, engineers, teachers, nurses, farmers, skilled trades)
- QALY-based value of all lost life-years
All estimates use simple back-of-envelope math with cited inputs. New parameters go in parameters.py.
WAR_DEATHS_SINCE_1900_TOTAL= 200,000,000 (Leitenberg/CISSM central estimate ~200M including democide, famine; source: necrometrics.com, Leitenberg 2006)
WORKFORCE_PCT_PHYSICIANS= 0.3% (~0.3% of population were doctors/physicians)WORKFORCE_PCT_NURSES_MIDWIVES= 0.4%WORKFORCE_PCT_ENGINEERS= 0.2%WORKFORCE_PCT_SCIENTISTS_RESEARCHERS= 0.1%WORKFORCE_PCT_TEACHERS= 1.0%WORKFORCE_PCT_FARMERS_AGRICULTURAL= 40% (but deaths among farmers proportional to general pop, so use general %)WORKFORCE_PCT_SKILLED_TRADES= 3.0%
Actually, simpler approach: just use a single combined "professionals" percentage and multiply. The 20th century average across all countries: doctors (~0.3%), nurses (~0.4%), engineers (~0.2%), scientists (~0.1%), teachers (~1%), skilled trades (~3%) = ~5% combined professional class. But farming was 30-60% of population early 1900s. We'll note farmers separately since their % is so different.
WAR_PROFESSIONALS_KILLED_SINCE_1900= 200M × 5% = 10,000,000 (conservative; doesn't account for targeting of educated classes)WAR_DOCTORS_KILLED_SINCE_1900= 200M × 0.3% = 600,000WAR_ENGINEERS_KILLED_SINCE_1900= 200M × 0.2% = 400,000WAR_SCIENTISTS_KILLED_SINCE_1900= 200M × 0.1% = 200,000WAR_TEACHERS_KILLED_SINCE_1900= 200M × 1.0% = 2,000,000WAR_NURSES_KILLED_SINCE_1900= 200M × 0.4% = 800,000
- Average age of war death: ~28 years (weighted: soldiers ~23, civilians older)
- Average life expectancy mid-20th century: ~55 years (global weighted avg)
- Years of life lost per death: 55 - 28 = 27 years
WAR_LIFE_YEARS_LOST_SINCE_1900= 200M × 27 = 5.4 billion life-yearsWAR_QALY_VALUE_SINCE_1900= 5.4B × $150,000/QALY = $810 trillion- (Use existing
STANDARD_ECONOMIC_QALY_VALUE_USD= $150,000)
Sum of known estimates:
- WWI: ~$5T (all belligerents, property + economic disruption)
- WWII: ~$23T (Harrison, all belligerents property destruction + reconstruction)
- Korea: ~$0.5T
- Vietnam: ~$1T
- Post-9/11 wars: ~$8T (Brown Costs of War project)
- Other 20th-21st century conflicts (China civil war, Iran-Iraq, Congo, Syria, Ukraine, etc.): ~$5-10T
WAR_CUMULATIVE_PROPERTY_DESTRUCTION_SINCE_1900= ~$45T (central estimate)
- Current annual: $100B/year
- Historical average (lower in early 1900s, higher recently): ~$30B/year average over 125 years
- Plus one-time catastrophic events: nuclear testing ($500B remediation), Agent Orange ($100B+), Gulf War oil fires, DU contamination, Zone Rouge, etc.
WAR_CUMULATIVE_ENVIRONMENTAL_DAMAGE_SINCE_1900= ~$5T (very rough)
- Leitenberg 2006 (CISSM deaths in wars 20th century) - already may exist
- Necrometrics.com (Matthew White's 20th century death estimates)
- Harrison 2000 (Economics of WWII)
- Brown University Costs of War project
- HHS 2024 QALY valuation ($150K standard)
- NSF Science & Engineering workforce data
Insert after "The Running Tab" section (line ~329) and before "Hidden Costs of War" (line ~332).
- Cumulative property destruction table by major conflict (~$45T total)
- Cumulative environmental destruction estimate (~$5T)
- Brief Wishonia-voice commentary
- Simple methodology: workforce % × 200M total deaths
- Table: profession | workforce % | estimated killed
- Note that this is conservative (educated classes were disproportionately targeted in Holocaust, Khmer Rouge, Cultural Revolution, Soviet purges)
- Punchline: 200,000 scientists and 600,000 doctors. How many cures died with them?
- 200M deaths × 27 avg years lost = 5.4 billion life-years
- At $150,000/QALY = $810 trillion
- Compare to $180T cumulative military spending: the lives destroyed were worth 4.5× what was spent destroying them
- This is the ultimate ROI calculation: spend $180T, destroy $810T in human life value
Regenerate _variables.yml with new parameters.
dih_models/parameters.py- new parametersreferences.bib- new citations (if needed)knowledge/problem/cost-of-war.qmd- three new sections