How They Relate

SAPA and Gale-Shapley are complementary, not competing:

• SAPA exhaustively calculates ALL possible bilateral acquisitions—useful for exploring "what if Country X targeted Country Y?"
• Gale-Shapley identifies STABLE coalition matches—useful for understanding "who actually teams up with whom over time?"
• A high SAPA score (e.g., China → Taiwan 60.8%) might appear in Gale-Shapley as a coalition scenario (e.g., China + Russia → Taiwan in Phase 2)
• The Deal Explorer shows both: SAPA bilateral viability + related coalition matches from Gale-Shapley

Methodological Note: Interpreting Viability Scores

Viability % scores are composite indices, not calibrated probabilities. A score of 60% does not mean "60% chance of acquisition occurring." Rather, it represents a weighted combination of strategic motivation (resources, proximity, chokepoints), coercion feasibility (military capability ratio), and blocking constraints (alliances, distance, economic size).

How to interpret: Viability scores are relative rankings useful for comparing pairs. A 60% score means "strong strategic interest with feasible execution path" — not "6 in 10 chance this happens." Historical validation against actual acquisition attempts has not been conducted; treat these as exploratory indices, not predictive probabilities.

Formula

Base Industrial Value = Effective_GDP × Effective_Multiple × Adjustment_Multipliers

This follows corporate M&A precedent where industrial nations trade at 5-15× EBITDA. The base multiplier of 10× is adjusted based on governance quality, sanctions, and currency stability:

• Governance Multiplier: 0.70× (fragile states) to 1.30× (strong institutions) - based on World Bank WGI
• Sanctions Discount: -15% (VEN) to -40% (PRK) for 18 sanctioned entities
• Currency Stability: 0.40× (hyperinflation) to 1.05× (reserve currency)
• Shadow Economy: Adds informal economy value (-50% to +55% of GDP)

Examples:

• USA: GDP $27.3T × 10.0 × 1.25 (strong governance) × 1.05 (reserve currency) = ~$358T industrial value
• Russia: GDP $2.07T × 10.0 × 0.85 (governance) × 0.70 (sanctions/currency) = ~$12.3T industrial value
• Nigeria: GDP $0.44T × 10.0 × 0.80 × 1.35 (shadow economy) = ~$4.8T industrial value

Methodology: HCI-Productivity Approach

We use the World Bank Human Capital Index (HCI) combined with actual productivity data to calculate lifetime labor value:

Where:

• Working_Pop = Total population × working-age percentage (15-64)
• GDP_per_Worker = Country-specific actual productivity ($650 to $185,000)
• Labor_Share = Portion of GDP going to labor (0.25 to 0.60)
• Working_Years = 40-year career duration for NPV
• HCI² = Human Capital Index squared (quality multiplier)

Why HCI-Productivity (not Jorgenson-Fraumeni):

• J-F requires microdata (earnings by age/sex/education) only available for ~20 OECD countries
• A global valuation model needs 95%+ coverage, not 15%
• HCI is World Bank peer-reviewed data covering 174 countries
• Uses actual GDP per worker instead of theoretical estimates

Additional Adjustments:

• Digital Productivity Multiplier: Based on internet penetration
  • Formula: If penetration ≥ 50%: 1.0 + (penetration - 0.50); If < 50%: 1.0 - (0.50 - penetration) × 0.5
  • Range: 0.75x (0% internet) to 1.5x (100% internet)
• Brain Drain Coefficient: -5% (voluntary) to -60% (hostile conquest)
• Population-Climate Burden: Reduces HC when climate costs exceed productivity

Tiered Reserve Valuation

Replaces simplistic "reserve × commodity price" with realistic extraction economics:

Real Example: Bolivia Lithium

• Reserve: 3M tons (proven reserves; 21M tons total resources)
• Market price: $15,000/ton
• Naive calculation: 3M × $15K × 0.40 = $18B
• Rigorous: 3M × $15K × 0.25 margin × 0.60 probability × 0.30 political discount = $810M
• Impact: -95% adjustment for political constraints and execution risk

Quantified Strategic Value

• Chokepoints: Annual toll revenue × 15-year NPV
  • Suez Canal: $6.5B annual toll × 15 = $97.5B strategic value
  • Panama Canal: $4.0B annual × 15 = $60B
  • Malacca Strait: ~$200B transit value controlled by Singapore
• Forward Bases: Force projection value - operational costs
• Contested Territory: (Resource value + leverage) × (1 - dispute probability)
• AI Infrastructure Premium: Strategic multiplier for AI leadership
  • Application: Adds 3-25% strategic premium to Enterprise Value for nations with dominant AI positioning
  • Factors: AI investment concentration, digital infrastructure control (data centers, semiconductors), tech sector strength
  • Premium Multipliers:
    • USA: +20% (dominant ecosystem: NVIDIA, Google, OpenAI)
    • China: +15% (major state investment, rapid infrastructure scaling)
    • UK, Taiwan: +8% (DeepMind/AI talent, TSMC semiconductor dominance)
    • Canada, Israel: +6-7% (Toronto/Montreal AI hubs, startup concentration)
    • Singapore, Korea: +5% (regional AI hubs, Samsung chips)
    • Japan: +4% (semiconductors, robotics)
    • France, Germany, Australia: +3% (European AI leadership)
    • Netherlands, India: +2% (ASML chips, IT services)
    • Others: No premium
  • Rationale: AI infrastructure is now as strategically critical as military capability or natural resources. Control of semiconductor supply chains, data center networks, and talent concentration provides geopolitical leverage

The Creditor Concentration Problem

$56B in Chinese-held debt carries different leverage risk than $56B in diversified private debt.

We calculate nominal debt as a percentage of GDP, then use creditor concentration (HHI index) to adjust the effective debt burden on a buyer:

Debt Adjustment Process

How it works:

• Calculate HHI (Herfindahl-Hirschman Index) of creditor concentration from 0 (perfectly diversified) to 1.0 (single creditor)
• Higher HHI means fewer creditors hold more debt = higher leverage on buyer
• The leverage multiplier typically ranges from 0.95× to 1.25× nominal debt
• China-dominated debt (>40%) and IMF conditionality create strategic constraints

Example: USA vs China

• USA: HHI = 0.345 (diversified), leverage factor ≈ 1.0×, Risk score 20.7/100 (low)
• China: HHI = 0.738 (concentrated), leverage factor ≈ 1.2×, Risk score 44.3/100 (moderate)

Hidden Costs of Acquisition

Critically, environmental cleanup costs cannot be repudiated by a new owner and represent real liabilities:

• Carbon obligation: Countries with high emissions face future carbon taxes/reparations
• Polluted sites: Brownfields, mining sites, nuclear facilities require cleanup
• Water debt: Aquifer depletion, river pollution obligations
• Deforestation: Reforestation commitments under international agreements

Impact: Environmental liabilities reduce net valuation by 2-15% depending on industrialization level and climate commitments.

Dynamic Climate Liability Calculation

We replaced static climate vulnerability tables with dynamic ND-GAIN data to calculate climate adaptation costs:

ND-GAIN Indicators:

• Vulnerability (0-1): Exposure to climate hazards, sensitivity to impacts, and reliance on climate-sensitive sectors
• Readiness (0-1): Economic, governance, and social readiness to adapt to climate change

Freshwater as Strategic Asset

Water is a 21st-century strategic resource. Nations with abundant freshwater receive a valuation premium:

Climate Winners & Losers

Climate change creates winners and losers in agricultural potential. We adjust land value based on IPCC projections:

BATNA Reduction for Climate-Vulnerable Targets

Countries facing climate collapse have reduced negotiating leverage (weaker BATNA - Best Alternative to Negotiated Agreement):

Desperation Thresholds:

• < 0.3 (STABLE): No discount - target retains full negotiating leverage
• 0.3-0.5 (STRESSED): Minimal discount - some climate pressure
• 0.5-0.8 (VULNERABLE): Partial discount (up to 25%) - significant pressure
• > 0.8 (DESPERATE): Full 40% discount - existential threat, willing to accept unfavorable terms

World Bank Groundswell 2050 Projections

Climate change drives human capital redistribution independent of acquisition scenarios. The World Bank projects 216 million internal climate migrants by 2050. The model adjusts HC values based on these projected migration patterns:

Regional Projections

Emigration Pressure Factors

• ND-GAIN Vulnerability: 40% weight - climate exposure and sensitivity
• Low Readiness: 30% weight - inability to adapt to climate change
• Climate Desperation: 30% weight - existential threat level
• Regional Factor: 1.5× multiplier for World Bank Groundswell source regions

Immigration Attractiveness Factors

• Destination Score: 70% weight - immigration infrastructure, policy openness, historical ties
• Climate Stability: 30% weight - low vulnerability + high readiness

Three Integration Scenarios

Cost Drivers:

• Military occupation: $10-100B (depending on resistance)
• Institutional overhaul: $5-30B
• Infrastructure repair from conflict: $20-80B
• Political stabilization (2-5 years): $2-10B annual

Willing Cooperation

Value = Assets - Low Integration Costs - Liabilities

Assumes negotiated transfer with minimal resistance. Integration follows democratic legitimacy. Highest valuation due to lowest acquisition friction.

Contested Transfer

Value = Assets × Institutional_Multiplier - Medium Integration Costs - Creditor-Adjusted Liabilities

Most realistic scenario. Some domestic/international resistance, but transfer negotiated over 5-10 years. Moderate valuation.

Hostile Acquisition

Value = Assets × (1 - Brain_Drain) - High Integration Costs - Liabilities × (1 + Enforcement_Cost)

Coercive acquisition with significant capital flight. Buyer must maintain occupation indefinitely. Lowest valuation due to maximum acquisition costs.

Risk Score Formula (Granular)

Risk Score Interpretation:

• 0-35: High autonomy - diversified creditors, buyer controls integration
• 36-55: Medium autonomy - moderate concentration, creditor negotiations required
• 56-70: Low autonomy - high concentration, creditor cartel controls terms
• 71-100: Very low autonomy - extreme concentration, buyer severely constrained

Brain Drain Coefficient

In hostile acquisitions, skilled workers flee. We quantify this as a percentage reduction in human capital value:

Alliance Membership Premium & Transfer Risk

Alliance memberships create quantifiable GDP premiums through trade access, security guarantees, and coordination benefits. These premiums are conditional—they transfer to friendly buyers but evaporate in hostile acquisitions.

Critical insight: On hostile acquisition, alliance membership is typically revoked, so a buyer does NOT inherit these benefits. Sanctions and expulsion typically follow, creating "stranded alliance value."

Western Alliances

Eurasian & Non-Western Security Blocs

Regional Economic Communities

African Regional Blocs

The Lift Index

Measuring the gap between territorial production and resident income

Conceptual framework informed by theories of phantom capital and capital sovereignty. Citation: Poliks, M., & Trillo, R. A. (2025). Exocapitalism: Economies with absolutely no limits. Becoming Press.

Formula: ((GDP - GNI) / GDP) × 100

The Lift Index measures the proportion of a nation's GDP that represents capital flows through the territory without accruing income to residents. It reveals:

How the Lift Index Adjusts Our Pricing Model

The Lift Index directly modifies the Industrial Value (GDP) component of sovereign valuation through a GDP Quality Adjustment:

• Phantasmic GDP discount: High positive Lift Index (>10%) applies 15% discount to GDP value—recognizing that phantom capital can evaporate in hostile scenarios or policy changes. Moderate positive Lift applies scaled 5-10% discount.
• Creditor premium boost: Negative Lift Index applies boost of up to 15%—recognizing that residents earning abroad provides alternative income streams and geopolitical resilience during regime change.
• Integration challenge: Acquiring a country with high Phantasmic GDP (positive Lift) requires maintaining investor confidence or replacing lost capital flows with domestic investment. Valuation discount reflects this risk.
• Regime change advantage: In hostile scenarios, Phantasmic GDP typically evaporates entirely; Creditor Premium provides baseline income independent of geopolitical status.

Examples in Pricing

• Ireland (+23.35%): GDP reduced by ~9% due to moderate phantom GDP burden. Reflects that ~¼ of Irish GDP is foreign capital flow-through.
• Luxembourg (+30.32%): GDP reduced by 15% (maximum discount). Extreme phantom GDP concentration in global finance.
• Japan (-21.68%): GDP boosted by ~15% for creditor strength. Japanese residents' overseas income provides $400B+ annual buffer independent of domestic conditions.
• United States (-1.08%): Slight creditor boost (~1%) from overseas income. Reflects balanced economy with modest foreign earnings advantage.

Source: Brand Finance Global Soft Power Index

What it measures: The economic value of a nation's reputation, cultural influence, diplomatic network, and institutional prestige. Soft power affects ability to attract investment, talent, trade relationships, and alliance credibility.

Example Values (2024):

• USA: $26.0T nation brand value (score: 74.8)
• China: $10.2T (score: 63.2)
• UK: $5.4T (score: 66.5)
• Germany: $4.9T (score: 62.7)
• Japan: $4.2T (score: 62.5)

Brand Finance applies ISO 10668 valuation methodology to nation brands, treating countries as "brands" with quantifiable reputation value.

Source: World Bank KNOMAD

What it measures: The economic connection to citizens abroad. Remittances represent ongoing diaspora economic engagement—an asset, not just income. We capitalize annual flows to estimate the "stock value" of diaspora networks.

Top Diaspora Asset Values:

• India: $1.38T diaspora asset
• Mexico: $676B diaspora asset
• Philippines: $403B diaspora asset
• China: $314B diaspora asset
• Egypt: $296B diaspora asset

Note: Brain drain is modeled separately as a liability (human capital flight during hostile acquisition). Diaspora value captures the positive economic connection to citizens abroad.

Source: Fund for Peace Fragile States Index (FSI)

What it measures: State stability and resilience. Fragile states have discounted asset values due to: governance risk, conflict potential, institutional decay, and capital flight risk. The FSI captures 12 indicators across cohesion, economic, political, and social dimensions.

Extremes:

• Most Fragile:
  • Somalia: 111.3 (×0.54)
  • Sudan: 109.3 (×0.55)
  • South Sudan: 109.0 (×0.55)
• Most Stable:
  • Finland: 14.3 (×0.94)
  • Iceland: 15.2 (×0.94)
  • Norway: 22.8 (×0.91)

Source: Eurostat Table 29 / OECD Pensions at a Glance

What it measures: Accrued pension entitlements as % of GDP. These represent promises governments have made to current and future retirees. An acquirer would inherit (or must repudiate) these obligations.

Highest Pension Burdens (% GDP):

• Spain: 507% of GDP (partially funded)
• Austria: 442% of GDP
• Italy: 420% of GDP
• France: 390% of GDP
• Japan: ~320% of GDP (severe aging)

Caveat from Eurostat: "Accrued-to-date pension entitlements are NOT suitable as a measure of sustainability and should not be considered government debt." We apply a conservative 25% factor to reflect this nuance.

Updated Asset/Liability Formula

Impact Summary:

• Soft Power: USA gains $26T, significantly boosting total value
• Diaspora: India gains $1.4T, Philippines $400B+
• Fragility: Somalia loses ~46% of base assets; Finland retains 94%
• Pensions: Spain adds ~$1.7T liability; USA adds ~$900B

Primary Data Sources

Confidence Levels:

• High confidence (±5%): OECD countries, transparent financial systems (US, EU, Japan)
• Medium confidence (±15%): Emerging markets with decent reporting (Brazil, India, Mexico)
• Low confidence (±30%): Fragile states with limited data (Somalia, Syria, Venezuela)
• Very low confidence (±50%+): Conflict zones or non-reporting countries (DPRK, parts of Sub-Saharan Africa)

Known Data Gaps

• China debt percentages showing 0%: AidData tracks Chinese lending to development countries but may undercount due to opaque financing structures. Major economies don't formally report Chinese holdings
• IMF percentages low across board: IMF involvement appears during crisis lending. Current dataset reflects pre-crisis periods for most countries
• Developing economy data: Many Sub-Saharan and Pacific nations have incomplete creditor composition due to lack of transparency
• Private creditor undercount: Institutional investors tracked via World Bank CRS but aggregates may obscure true concentration
• Country profiles & blurbs: Population, economic, and political data sourced from CIA World Factbook, World Bank, IMF, and Trading Economics. Each country entry includes blurbSources array listing specific sources used for that profile
• Capital city photos: Sourced from free stock photography APIs (Unsplash, Pexels, Pixabay). Photos are real-world capital city images (not flag images). Current collection: 67% high-quality professional photos (≥150KB). 30% in refresh queue to replace lower-quality placeholders. Quarterly refresh cycle maintains quality standard of ≥150KB minimum file size
• Internet Penetration & Digital Productivity Multiplier: Derived from ITU Facts & Figures 2025 and World Bank WDI. Formula: ≥50% penetration: 1.0 + (penetration - 0.50); <50% penetration: 1.0 - (0.50 - penetration) × 0.5. Multiplies human capital value by 0.75x to 1.5x range based on internet access. Empirical basis: correlation r=0.87 between penetration and GDP growth; each 10% increase yields +1.4-1.6% GDP growth in developing economies
• AI Investment & Digital Infrastructure: Coverage limited to 50+ countries with significant AI investment. Data from Crunchbase (venture funding), World Bank (capex), government budgets (public investment). Key metrics: total AI investment (2023-2025), data center capacity, GPU/semiconductor manufacturing capability, AI talent concentration. Note: emerging economies (India, Vietnam) rapidly expanding AI capabilities; data may lag 6-12 months for smaller nations
• Lift Index: Measures the gap between territorial production (GDP) and resident income (GNI). Formula: ((GDP - GNI) / GDP) × 100. Positive Lift Index = "Phantasmic GDP" (capital passes through without accruing to residents, typical of tax havens and financial centers); Negative Lift Index = "Creditor Premium" (residents earn more abroad than foreigners earn domestically, typical of creditor nations). Global mean: 1.47%; range: -21.68% (Japan) to +30.32% (Luxembourg). Updated annually; data quality: ±2-3% for OECD, ±5-10% for developing markets

51 Cases (1776–2023)

The database includes every major modern case of territorial detachment, categorized by outcome:

What Each Case Records

• Entity name & host nation at time of detachment
• Year of detachment or attempt
• Outcome: successful, failed, ongoing, absorbed
• Mechanism: negotiated (Singapore), unilateral declaration (Kosovo), war (Bangladesh), referendum (South Sudan), collapse (Soviet states)
• Host nuclear status: Whether host nation was nuclear-armed at time of detachment. Key finding: no city has ever been detached from a nuclear power against its will
• GDP at detachment (where available): Validates economic leverage scoring
• Structural profile: Population, host nation GDP share, dominant Bratton layer — enables pattern matching to current cities

How Much Strategic Value Does a City Hold Relative to Its GDP Share?

Lambda measures whether a city "punches above its weight" — holding more strategic value than its share of national GDP would suggest.

Where adjusted_city_valuation_share is a weighted composite of five dimensions:

Interpretation:

• λ < 1.0: City contributes less strategic value than its GDP share suggests ("Passenger")
• λ = 1.0: City's strategic value is proportional to its GDP ("Baseline")
• λ 1.0–1.5: City punches above its weight ("Leveraged")
• λ 1.5–2.0: City holds disproportionate strategic value ("Detachment Candidate")
• λ > 2.0: City is the primary strategic asset of the host nation ("Core Asset")

Cap: λ capped at 3.0. City-states (Singapore, Luxembourg, Hong Kong) are calibration cases that hit the cap.

Earth / Flow / Cloud Value Distribution

Following Benjamin Bratton's vertical sovereignty model (The Stack, 2016), each city's strategic value is decomposed into three layers. This informs severance feasibility: cities with high upper-layer value are easier to detach because the host nation's physical control mechanisms don't capture the value.

How Realistic Is Detachment?

Composite score from 0.0 to 1.0 measuring the realistic probability that a city could be detached from its host nation. Uses 6 sub-scores weighted by importance, multiplied by a nuclear modifier.

The Headline Score

Each city's final ranking combines Lambda (how valuable it is) with severance feasibility (how detachable it is):

Where:

• lambda_normalized = λ / 3.0 (scales to 0.0–1.0 range)
• severance_feasibility = composite from Section 12.3
• strategic_value_factor = 1.0 + (Bratton Cloud layer bonus) + (GFCI financial center bonus)

Viability tiers:

Which Cities Are Scored?

A city enters the detachment analysis if it passes all three gates:

• GaWC rank ≥ Gamma+ (score ≥ 0.4) — minimum global connectivity threshold
• Metro GDP ≥ $50 billion — minimum economic mass for viability as a standalone entity
• Climate data available for host country (ND-GAIN or Swiss Re city-level)

Currently 74 cities pass all gates. All 49 originally curated cities plus 25 expansion cities meet eligibility. The gate is designed for forward-looking expansion — as coverage grows toward 150+ cities, the filter removes candidates that lack the structural prerequisites for meaningful detachment analysis.

24 Platform Entities Across 6 Sovereign Functions

Each platform entity is categorized by which sovereign function(s) it exercises:

Each platform also has acquirer preferences — weighted scores for energy, water, fiber connectivity, human capital, regulatory environment, market size, tax regime, land cost, and geological stability — used in platform-city matching.

Per-Country and Per-City Measurement

For each of the 50 countries hosting scored cities, the PSI measures how deeply platform companies have penetrated sovereign functions:

PSI Range: 0.0 (full state sovereignty over digital functions) to 1.0 (platforms have effectively replaced state functions). Top PSI countries: China 0.76, USA 0.75, Ireland 0.72, UK 0.69, Canada 0.68.

City-level PSI: National score adjusted by the city's Cloud layer percentage from Bratton decomposition. San Francisco (Cloud = 0.70) has the highest city PSI at 0.90; cities with high Earth layers have lower platform sovereignty exposure.

24 Platforms × 74 Cities = 1,702 Preference Scores

Each platform has unique infrastructure requirements. The preference model matches platform needs to city capabilities:

Platform-city preferences feed into city-level Gale-Shapley matching (Phase 5), where platforms act as acquirers alongside traditional state actors. A city matched with Amazon faces different sovereignty implications than one matched with Apple.

Physical Resource Demand Projections

Platform companies are not purely digital — they require massive physical infrastructure. The demand model projects platform resource consumption across three horizons:

By 2030, platform renewable energy farms will occupy an estimated 933,000 acres — overwhelmingly sited in low-cost agricultural areas (Texas Panhandle, Midwest, desert Southwest, southern Spain). Each GW of solar requires approximately 5,000–10,000 acres. Amazon alone is the world's largest corporate renewable energy purchaser at 20+ GW contracted.

13 Documented Platform Earth-Layer Acquisitions

Platform companies are already acquiring physical territory for infrastructure. The model tracks these acquisitions with an extraction ratio — the estimated ratio of value extracted from a territory versus value returned through jobs, taxes, and investment:

Pattern: Platforms preferentially acquire territory in small, economically limited communities with cheap energy and permissive regulation. Extraction ratios are highest where the community has fewest economic alternatives. Swedish tax system (Meta Luleå: 15:1) captures more value than U.S. localities (Google The Dalles: 40:1).

Data Coverage Disclosure

Platform sovereignty scores use a three-tier data model reflecting the availability of direct measurement data across countries:

Countries below the line (Tiers 2 and 3) lack direct platform market share measurement. Their PSI scores are structural estimates — useful for comparative ranking but not calibrated to observed platform activity. The Detachable Assets explorer marks each platform entry with a confidence dot: measured, partial, modeled.

State Acquirer Preference for City

Each state acquirer ranks all 74 cities using a 5-factor synergy score (Spec Section 3A). Capacity: 3 cities per state acquirer.

City Preference for State Acquirer

Each city ranks state acquirers by how well the acquirer serves the city's governance and economic interests:

Platform Acquirer Preference for City

Platform companies rank cities on digital infrastructure suitability (Spec Section 3D.4). Capacity: 5 cities per platform.

City Preference for Platform Acquirer

Cities rank platforms by local economic benefit potential:

Matching Algorithm

Combined synergy for each (acquirer, city) pair is the geometric mean of the acquirer's preference and the city's preference: synergy = sqrt(acquirer_score × city_pref). Two independent Gale-Shapley rounds produce stable matches above a minimum synergy threshold of 0.25. Hard constraints block matches where the acquirer is hostile to the city's host alliance, the platform is the dominant employer (>15% workforce), or antitrust actions exist in the host jurisdiction.

Output: city_acquirer_matches.json — stable matches across 74 cities, 228 state acquirers, and 83 platform acquirers. Explore on Detachable Assets

State Acquirer Host Friction

State acquirers face four friction components from the host nation:

Platform Acquirer Host Friction

Platform acquirers face regulatory rather than military friction:

Alliance divergence between acquirer and host scales deal viability: same bloc = 0.0 penalty, opposing blocs (NATO vs BRICS) = 1.0 penalty. Deals where a China-aligned acquirer targets a NATO-hosted city face maximum divergence friction. Only deals above 10% viability are displayed in the Deal Explorer.

Output: city_sapa_deals.json — 528 deals evaluated, survival rate ~29%. Explore on Deal Explorer

Coalition Viability Score (10 Sub-Scores + Governance Multiplier)

Spec Section 3C.2. Each coalition is scored across 10 weighted dimensions, then scaled by the coalition's mean governance quality:

Greedy Optimization with Restarts

Spec Section 3C.4. The algorithm uses 50 anchors (top-scoring entities) plus 30 regional anchors to seed coalition construction:

• Seed: Start from each anchor entity as the core
• Grow: Add highest-synergy compatible member from entity pool. Constraints: no duplicate host nations, at least one financial center, at least one sovereign entity per coalition, max 3 platforms per coalition, max 5 appearances per entity across all coalitions
• Prune: Remove members that reduce overall viability
• Deduplicate: Reject coalitions with >40% membership overlap with existing selections
• Select top 40: Rank by viability score and take the top 40 coalitions. Each receives a Claude Opus-authored narrative describing governance structure, economic logic, and strategic risks

Output: network_state_coalitions.json — 33 coalitions generated from multi-entity pool (cities, states, territories, city-states, platforms). Explore on Detachable Assets → Network Constructor

Fiscal Impact Model

Tax revenue concentrates in cities disproportionate to population share. A tax concentration multiplier inflates revenue loss beyond raw GDP share:

Brain Drain Cascade

Skilled workers follow economic opportunity into the detached city. Emigration accelerates over four horizons, calibrated against post-Soviet emigration (10–25%), Greek crisis (12%), and Venezuelan collapse (15%):

7-Component Sovereign Viability Score

Combines institutional capacity with economic shock severity:

States are classified as Viable/Diminished (≥60%), Struggling/Severe Crisis (35–60%), Failing/Terminal Decline (20–35%), or Collapse Trajectory/Non-Viable (<20%). City-states (Singapore, Luxembourg, Hong Kong) score 0% by definition — detachment eliminates the state entirely.

Additional Impact Layers

• Infrastructure stranding: 5 categories (port, airport, data center, energy grid, telecom) — the detached city takes the infrastructure; the residual state bears maintenance costs for underutilized connecting networks
• Inequality shift: Gini coefficient computed for 48 countries from World Bank data. Removing high-income city populations compresses the distribution downward, but poverty rate increases as fiscal transfer payments evaporate
• Platform acquisition risk overlay: Fiscal desperation makes residual states vulnerable to ceding sovereignty for platform-mediated economic stabilization. Higher fiscal gap = higher extraction ratio tolerance
• Temporal projections: GDP drag compounds at each horizon (Y1/Y5/Y10/Y25) with annual_drag = brain_drain × 0.35 + 0.005 × fiscal_gap_pct
• 8-section structured narratives: Each host nation receives a Claude Opus-authored impact assessment drawing on theoretical frameworks from Milanovic (global inequality), Povinelli (abandonment/disposability), Mbembe (necropolitics), and Sassen (expulsion)

Output: residual_state_impacts.json — 50 host nations, 4 temporal horizons, 33 coalition compound scenarios. Explore on Detachable Assets → Residual States

Dignity Floor Index (DFI): 7-Dimension Conjunctive Measure

Each country receives a composite DFI score and per-dimension breakdown. The DFI is conjunctive — a country fails the floor if any single dimension falls below its threshold, regardless of how well other dimensions perform.

Dimension Sub-Indicators

Indicator values cascade through a 4-tier resolution: (1) measured from dfi_indicators.json, (2) measured from economic_baselines.json, (3) proxied from world_bank_indicators.json, (4) development-scaled global median default (poor countries receive worse defaults via a GDP-per-capita sigmoid). Active conflict countries (UKR, SDN, SOM, YEM, SYR, MMR, AFG, LBY, ETH, COD, SSD, IRQ) receive a hard conflict penalty multiplier (0.35–0.65) on service delivery dimensions.

Calibration Against World Bank Baselines

The DFI measures a broader concept than income alone. The World Bank's Poverty, Prosperity, and Planet report (2024) provides the canonical income-based baselines against which DFI results can be compared. Our dignity floor captures populations that income measures miss — people above the income line who nonetheless lack adequate health systems, political voice, housing, or environmental safety.

Key comparison: The World Bank counts 3.53B people below $6.85/day. Our national-level DFI structural floor identifies 3.69B in countries where at least one dimension fails — a similar magnitude because national averages mask within-country inequality (India “passes” nationally despite 82% of its population being below $6.85/day per WB PIP 2022). When we inject World Bank poverty headcount ratios as an individual-level floor — recognizing that the income-poor within DFI-green countries still lack dignity in practice — the inequality-adjusted figure rises to 5.76B (71.6%). The 2.2B gap between the WB’s income-only count and our adjusted count represents populations affected by non-income structural failures: authoritarian governance (China 1.4B, Russia 144M), weak health systems, environmental vulnerability, and inadequate housing that income measures alone do not capture.

Scalar assumption: We use WB poverty headcount ratios as a baseline floor because conditions for the income-poor are scalar with structural capacity — if a country’s governance, health system, or environment degrades, the poorest suffer disproportionately. WB poverty data thus serves as a minimum individual-level dignity violation rate, with structural DFI failures adding additional affected populations on top.

Projected trajectories: The World Bank projects 622 million will remain below the extreme income line by 2030, and estimates it would take more than a century at current rates to bring all populations above $6.85/day. Climate change could push an additional 100+ million below income thresholds by 2030 (World Bank Climate and Development Reports). Our archetype configurations model how structural reforms — ranging from fiscal federalism to full sovereign restructuring — could accelerate or retard these trajectories across all seven dimensions simultaneously.

Source: World Bank, Poverty, Prosperity, and Planet (2024); OPHI/UNDP, Global Multidimensional Poverty Index (2024). DFI income adequacy sub-indicator uses the $6.85/day threshold directly: income_adequacy = min(1.0, median_income / (2.0 × $6.85/day)).

Sub-National DFI Disaggregation

For 20 countries with available sub-national data, the engine multiplies national dimension scores by regional adjustment factors to produce region-level DFI estimates. This captures within-country inequality that national averages obscure (e.g., rural Bihar vs. urban Maharashtra in India, or the U.S. Deep South vs. Pacific Northwest). Data loaded from subnational_adjustments.json, producing 75 regional DFI breakdowns.

Residual Human Capital Layer

Bridges Phase 8 into Phase 9 by computing post-detachment DFI for each of the 49 host nations. Models the population that cannot leave:

5-Stage Search Algorithm

Each archetype configuration is generated through a formal 5-stage pipeline:

Redistribution Pool (Stage 3)

5 Objective Functions

16 Archetype Configurations

Transition Feasibility (5 Components)

The fundamental tradeoff: dignity compliance and transition feasibility are inversely correlated. Fiscal Federalism achieves 55% compliance with 0.81 feasibility; Full Optimization achieves 97% compliance with 0.13 feasibility. Climate Resilience Tax shows the fiscal ceiling: even with unlimited funding, compliance maxes at 62% because political participation cannot be purchased. Benevolent Global Monarch removes that ceiling by mandating institutional reform, achieving 100% at $115T — but requires authority that has never existed. There is no configuration that simultaneously maximizes both compliance and feasibility.

Conceptual Territory: Archetypes I–O

Archetypes A–H model governance arrangements that build on or reform existing state structures. Archetypes I–O expand the analysis into geopolitical territories from climate politics, collapse studies, and political theory — scenarios that dominate real-world policy debate but fall outside conventional institutional reform.

State Command Economy (I) occupies the territory where a vanguard party or authoritarian state seizes control of the economy for rapid decarbonization. Historical precedents include Soviet-era industrialization, Cuban healthcare universalization, and Chinese state-directed development. The model shows high material delivery but zero political freedom. The environmental score (0.55) is lower than expected because command economies historically produce severe non-carbon pollution even when they reduce CO&sub2;.

Eco-Fascism (J) models the territory where environmental crisis is instrumentalized to justify biopolitical exclusion — border militarization, carrying-capacity ideology, and the construction of protected vs. expendable populations. This archetype exists in the analysis specifically to demonstrate that exclusionary approaches produce worse aggregate outcomes than any constructive alternative.

Ecomodernism (K) represents the territory where technological deployment bypasses political reform entirely — nuclear energy, atmospheric geoengineering, AI-optimized resource allocation, and precision agriculture. Environmental safety is the star dimension (0.85 effectiveness). But the model reveals that technology cannot bypass governance absorption: countries with weak institutions still cannot deploy or maintain complex technical systems.

Degrowth / Managed Retreat (L) occupies the territory of intentional economic contraction within planetary boundaries. Environmental safety scores highest (0.90 effectiveness), but 40% of the material_security score is GDP per capita, which degrowth deliberately reduces. This archetype works within electoral systems, yielding the highest political participation effectiveness (0.65) among the new set.

Non-Consensual Algorithmic Governance (M) models governance BY algorithm rather than governance WITH algorithms, imposed without democratic mandate. Transfer effectiveness is highest (0.85) because algorithmic systems eliminate bureaucratic friction. But V-Dem electoral democracy equals zero, civil liberties equal zero, and surveillance infrastructure destroys civil society.

Consensual AI Governance (P) models the same algorithmic efficiency as M, but with a critical difference: the population willingly accepts AI as legitimate authority. Political participation is non-zero because citizens democratically choose algorithmic decision-making — a world where people believe AI is better than human governance. Social connection is preserved rather than destroyed, because there is no need for surveillance when compliance is voluntary. The comparison between M and P isolates the question: does AI governance fail because algorithms cannot govern, or because people will not accept being governed by algorithms?

Full Collapse / Warlordism (N) represents the true floor — complete institutional failure across all regions including wealthy ones. Rich countries degrade at 40% of the full rate due to infrastructure inertia. The analytical purpose is establishing the absolute bottom of the possibility space.

Utopian Anarchism / Mutual Aid (O) occupies the territory of bottom-up commons governance without state institutions. Our DFI model shows anarchism scoring moderate (~60% compliance), but this reflects the model’s institutional bias more than anarchist wellbeing. GDP per capita does not capture cooperative production. V-Dem indices cannot distinguish direct democracy from no democracy. Anarchism provides something our model cannot fully measure.

Structural Feasibility (7 Components)

Computed per country to assess whether each nation can sustain its sovereign functions under a given archetype:

Output: optimal_configurations.json — 220 countries, 16 archetype configurations, sub-national DFI for 20 countries (75 regions), residual human capital for 49 host nations, SPI cross-validation. Explore on Optimizations

Methodological Note: City Analysis as Extension, Not Replacement

The city-level detachment framework (Sections 11–13) extends the country-level valuation (Sections 1–10); it does not replace it. Country valuations remain the foundation — a city's Lambda and severance scores are derived from its relationship to the host nation's valuation. Platform sovereignty analysis adds a new analytical dimension that applies to both countries and cities. The viability scores in this framework are composite indices, not calibrated probabilities — they measure structural feasibility, not likelihood.

Integration Model Uncertainty

The framework values sovereign entities and now models post-detachment impacts through Phase 8 (Residual State Impact Assessment), including fiscal erosion, brain drain, skill distribution shift, and immobility effects on host nations. However, full post-integration modeling — what happens if a nation is actually absorbed by an acquirer — remains unmodeled. Key uncertainties:

• Extraction-only scenarios: Population ignored, local elites maintained—human capital valuations may overstate realizable value
• Debt-trap dynamics: When economic control already exists, formal acquisition adds marginal value that is difficult to quantify
• Military occupation costs: Historical precedents (Afghanistan: $2.3T over 20 years) suggest occupation costs can dwarf asset value
• Full integration: No modern precedent exists for voluntary full integration of a sovereign nation

The gap between economic capture and formal acquisition remains the framework's largest conceptual uncertainty.

What This Framework Does NOT Capture

• Cultural identity & nationalism: No formula quantifies resistance to foreign rule
• Diaspora networks: Historical populations abroad may support or oppose acquisitions. Note: network coalitions (Section 14.3) partially model diaspora-like cross-border connections
• Regional power dynamics: Partially addressed by alliance network analysis (Section 7) and Gale-Shapley acquirer matching, but neighbor-specific second-order effects (e.g., Iran-Saudi Arabia) remain unmodeled
• Pandemic/supply chain shocks: Black swan events create discontinuous valuations
• Religious/ethnic conflict: Sectarian tensions reduce valuation but resist quantification
• Regime change probability: Internal political instability affects acquisition timing but is not modeled dynamically; the fragility index provides a static proxy only

Confidence Degradation Over Time

• 1-2 years: Highest accuracy—current assets, debts, and relationships are well-documented
• 5-10 years: Medium accuracy—institutional changes, debt accumulation, and alliance shifts introduce uncertainty
• 20+ years: Low accuracy—geopolitical realignment, demographic transitions, and technological disruption make projections speculative

This valuation framework is intentionally simplified for clarity. Real geopolitical analysis requires scenario modeling, game theory, and regional expertise. The 16 archetype configurations (Fiscal Federalism, Devolution, Network of City-States, Platform Commons, Full Optimization, Climate Resilience Tax, Benevolent Global Monarch, Sovereign Realpolitik, State Command Economy, Eco-Fascism, Ecomodernism, Degrowth, Non-Consensual Algorithmic Governance, Full Collapse, Utopian Anarchism, Consensual AI Governance) each operate on different time horizons, and confidence degrades at different rates depending on the archetype assumed. Use this as a starting point, not an oracle.

Platform Sovereignty Data Limitations

Approximately 170 of 220 countries in the platform sovereignty analysis rely on modeled platform data (Tiers 2–3) rather than directly observed metrics. Tier 1 coverage (direct measurement) is limited to ~50 countries with robust digital infrastructure reporting. The inequality-adjusted dignity floor count (5.76B people) depends on World Bank poverty headcount data quality, which covers 182 countries with varying recency (2014–2024). Countries with older survey data may have dignity floor estimates that lag actual conditions by several years.

National-Level Aggregation

The Dignity Floor Index treats each country as a homogeneous unit, computing a single feasibility score per nation. Within-country inequality is partially addressed by the inequality adjustment (World Bank poverty headcount injection into the fiscal gap calculation) and sub-national disaggregation (20 countries, 75 regions). However, the majority of countries lack sub-national data entirely, meaning that significant within-country variation in governance capacity, economic base, and infrastructure quality is averaged away. Federations, conflict-affected states, and countries with large urban-rural divides are most likely to have their sub-national variation obscured by national-level aggregation.