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 (China → Taiwan, 48.5%) 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 and are not calibrated probabilities. A score of 60% represents a weighted combination of strategic motivation (resources, proximity, chokepoints), coercion feasibility (military capability ratio), and blocking constraints (alliances, distance, economic size); it does not mean "60% chance of acquisition occurring."

How to interpret: Viability scores are relative rankings useful for comparing pairs. A 60% score means "strong strategic interest with feasible execution path" and does not mean "6 in 10 chance this happens." Historical validation against actual acquisition attempts has not been conducted; treat these as exploratory indices with no predictive probability attached.

The industrial, human-capital, resource and land bases below are computed for every entity on one scale. For the 150 countries in the World Bank Changing Wealth of Nations 2024 accounts (2020 values), each base is blended with the matching CWON stock (produced capital, human capital, nonrenewable and renewable natural capital), after that stock is moved onto the computed scale by k, the median ratio of computed base to CWON value across covered countries:

• Disagreement fallback: where k × CWON_Value is more than 5× above or below the computed base, or CWON's oil component is null for a country with oil reserves (Saudi Arabia, Kuwait), the computed base is used alone and the country's data-quality record names the asset. 54 countries blend all four bases, 96 blend some of them, and 70 entities outside CWON (Taiwan, the oceans, most small territories) keep the computed bases.
• Industrial base: the blend is taken before the governance multiplier, which then applies to covered and uncovered countries alike.
• Net foreign assets and sovereign wealth funds are left out of the asset total. The CWON net foreign assets series we fetch is unsigned (the United States shows +$31.5T), so Saudi Arabia's $1.69T and Kuwait's $1.06T are recorded as context only.
• Pre-ship gate: each monthly refresh is compared with the prices it replaces before any page regenerates, and is rolled back if more than 10 entities move 50% or more, a top-50 entity moves more than 12 ranks, or a fixed ordering breaks (Saudi resources above the UAE's, Israel and the UAE above Algeria, no ocean above #40).

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 for governance quality, sanctions, currency stability, investment climate and AI infrastructure:

• Governance Multiplier: 0.33× (fragile states) to 2.00× (strong institutions) - blended World Bank WGI (40%), Transparency International CPI (30%), and Heritage institutional pillars (30%)
• Sanctions Discount: 5% to 50% across 18 sanctioned entities
• Currency Stability: 0% discount at the reserve-currency tier (a 1.00× multiplier, not a premium) to 60% under hyperinflation
• Shadow Economy: Adds informal economy value, 7.8% to 85% of GDP across 84 countries with data; countries without data are assumed to sit at 15%
• Investment Climate: 0.90× to 1.10×, from the five Heritage business-freedom pillars
• AI Infrastructure Premium: 1.02× to 1.20×, applied to industrial value only, for the 14 countries on the AI-power list

Examples (industrial value as the engine currently computes it, shadow economy, governance, sanctions, currency and AI premium included):

• USA: $1,266.2T industrial value: strong governance and the largest AI premium
• Russia: $21.8T industrial value: sanctions and currency discounts dominate
• Nigeria: $5.8T industrial value: a large shadow-economy uplift against a weak governance multiplier

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 176 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)
• Human Capital Resilience: 95% retained (voluntary) to 40% retained (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

• Reserves priced: lithium 3,000,000 tonnes at $15,000/tonne, plus natural gas, zinc and gold
• Tiered value before country adjustments: $42.7B
• Country adjustment: a combined 0.63 multiplier (30% political constraint, 10% climate risk)
• Resource value carried into the valuation: $48.4B

There is no separate extraction-probability term. Execution risk enters only through the tier discount factor above (0.20 to 1.00) and the country-level political and climate adjustment; the engine does not multiply reserves by a probability of extraction.

Quantified Strategic Value

• Chokepoints and forward bases: a flat enterprise-value multiplier per country; no toll-revenue NPV is computed. The strategic premium is (Industrial + Resources + Land + Human Capital) × (multiplier − 1), applied to the 12 countries that carry one.
  • Djibouti 1.5, Taiwan 1.4, Egypt 1.25, Panama 1.2, South Korea 1.2
  • Singapore 1.15, Poland 1.15, Türkiye 1.15, Qatar 1.1, Yemen 1.1, Gibraltar 1.1, Germany 1.05
  • The annual-toll × 15-year NPV model described in earlier versions of this page is not computed anywhere in the pipeline. Suez, Panama and Malacca toll revenues are not inputs.
• Contested Territory: not separately valued. Territorial disputes enter the model through the SAPA historical-claim table (Section 1b); the asset side carries none of it.
• AI Infrastructure Premium: Strategic multiplier for AI leadership
  • Application: multiplies industrial value only (enterprise value and the strategic premium above are excluded) by 1.02× to 1.20× for the 14 nations on the AI-power list
  • 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 +8%, Taiwan +8% (DeepMind/AI talent, TSMC semiconductor dominance)
    • Canada +7%, Israel +6% (Toronto/Montreal AI hubs, startup concentration)
    • Singapore +5%, Korea +5% (regional AI hubs, Samsung chips)
    • Japan +4% (semiconductors, robotics)
    • France +3%, Germany +3%, Australia +3% (European AI leadership)
    • Netherlands +2%, 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 Bands (discount at each band edge):

• < 0.3 (STABLE): 0% - target retains full negotiating leverage
• 0.3 to 0.5 (STRESSED): rises to 4% (some climate pressure)
• 0.5 to 0.8 (VULNERABLE): steps to 16% at 0.5 and rises to 40% at 0.8
• ≥ 0.8 (DESPERATE): the full 40% - existential threat, willing to accept unfavourable terms

The discount is not linear in desperation. The slope quadruples at 0.5, so a target at 0.49 gets 4% and one at 0.50 gets 16%.

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: min(1.5, 1 + projected_migrants_millions / 100), so a Groundswell source region reaches the 1.5× ceiling only above 50 million projected migrants

Immigration Attractiveness Factors

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

Four Integration Scenarios

Every country is classified into one of four scenarios from its fragility, institutional capacity and occupation-resistance history. The classification sets a total integration cost as a share of GDP, spread over a scenario- and size-dependent duration.

What actually varies the cost:

• Scenario classification: a resistance score blending fragility, institutional capacity and historical occupation resistance picks one of the four rows above
• Duration: 3 to 35 years, set by the scenario and by whether the country is small, medium developing, or developed; the total is spread across it
• Climate desperation: Section 3.6 then cuts the resulting cost by up to 40%
• Not modelled separately: occupation, institutional overhaul, infrastructure repair and stabilisation are the narrative behind the calibration; the code does not sum them as line items

Willing Cooperation (best case)

The buyer pays about half the base integration cost, so the price rises by that saving. Nothing else changes: alliance value and human capital are assumed to survive the transfer intact.

Contested Transfer (base case)

The base price itself, with the full scenario-classified integration cost already deducted. This is the headline enterprise value shown throughout the site.

Hostile Acquisition (worst case)

The buyer pays 1.50× the base integration cost on top of the base, loses 0.80 of the target's alliance value to expulsion, and loses the human capital that flees. Against that, it avoids the debt it declines to assume. There is no separate enforcement-cost multiplier on liabilities.

Risk Score Formula (Granular)

Strategic-collateral component. When a creditor physically holds a strategic asset of the borrower (a port, a base, a corridor), its leverage exceeds what the debt share alone implies, so a fourth component adds up to 15 points. Leverage multipliers are asset-specific (capped at 3.0×): e.g. China-Hambantota Port (Sri Lanka) 2.0× → +10; China-Gwadar + CPEC (Pakistan) 2.5× → +15; US-Al Udeid Base (Qatar) 2.0× → +10. Entities with no creditor data on file (e.g. uninhabited or speculative territories) take a neutral default Risk Score of 50.

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)

Six Dimensions, Weighted

Why security is capped low (0.13): hosting a US base or buying US fighters is alliance, not acquisition. A weight in line with the other dimensions would push every NATO member into “captured.” The cap, combined with the gate below, prevents that.

Active-Vectors Gate

The page text has long defined capture as “decisive leverage on two or more of [the dimensions above].” The composite score alone doesn't enforce that: a single dimension scoring near 1.0 can drag the composite up without any second lever. The gate fixes this:

Capture requires more than one vector. Single-vector security alliance (Israel, Japan, South Korea: 100% US arms supplier and hosted bases) maxes a security contribution of 0.13 with one active vector and stays in LOW_CAPTURE. Switzerland with no active vectors and noise-floor scores stays in SOVEREIGN.

SIPRI Cutoffs: Soviet-Era Arms Are Not Current Capture

Arms transfers carry political weight only while the supplier still has a working relationship with the current regime. We apply a default 2014-present delivery window, plus per-country effective-from cutoffs at sovereignty / regime transitions:

• Post-Soviet successor states (1991): Estonia, Latvia, Lithuania, Ukraine, Moldova, Georgia, Armenia, Azerbaijan, Kazakhstan, etc.; pre-1991 USSR transfers do not count toward Russian capture.
• NATO accession states (1991 to 2008): Poland, Czechia, Hungary, Romania, Bulgaria, the Baltics, Slovenia, Croatia, Albania, Bosnia, Serbia, Montenegro, North Macedonia, Kosovo; alliance flip severs the prior supply relationship.
• Iraq (2003), Libya (2011), Afghanistan (2021), Syria (2025): regime change resets the security-supply baseline.
• Iran (1979), Egypt (1979): revolution / Camp David flip respectively reset US/USSR transfers.
• Sahel juntas (Mali 2020, Burkina Faso 2022, Niger 2023): coup-driven pivots from France toward Russia / Wagner.
• Cuba, Vietnam, North Korea (1991): USSR→Russia continuity, no political flip; relationships preserved.

Calibration

Human Capital Resilience

In hostile acquisitions, skilled workers flee. We quantify this as the percentage of human capital value retained:

Why a Single English-Proficiency Modifier Was Insufficient

Earlier versions of the brain-drain coefficient gated emigration intent by a single English-proficiency modifier (±6 to 12%). That signal conflated language proficiency with the actual apparatus of mobility (passports, visas, currency power, destination corridors, and exit feasibility) and applied identical treatment to populations who could execute flight and populations who could not. Under hostile conquest, a Tajik subsistence farmer and a Swedish engineer with the same English proficiency received the same brain-drain coefficient, despite radically different real-world ability to leave.

The current model decomposes the coefficient into two orthogonal terms:

Intent is unchanged from prior versions: it captures the scenario type (voluntary merger → military conquest → autocratic regime change), modulated by diaspora network strength and the regime delta between origin and acquirer. Mobility is a new five-signal scalar that gates intent by the population's actual capacity to execute flight. The geometric mean ensures that any single near-zero gate dominates, matching the real-world pattern in which populations are trapped by any one of passport, poverty, currency, network, or war.

Five Mobility Signals

Calibration & Validation

The mobility factor was calibrated against UN DESA emigrant-stock data at the bilateral level (origin×destination pairs) using the predecessor bilateral model: pair-level R² = 0.19, Spearman rank correlation +0.42, consistent with cross-sectional bilateral migration models in the literature, where idiosyncratic destination policy and cohort effects bound explanatory power. The current unilateral five-signal formula was not formally re-calibrated; it was verified against approximately twelve historical reference cases (post-Soviet emigration 1991 to 2000, Syrian conflict 2011 to 2024, Venezuelan collapse 2015 to 2024, North Korean defection 1998 to 2024, Greek crisis 2010 to 2018, Cuban migration cycles, others) and produced expected ordinal rankings.

Calibration Result Summary

Known Limitations

1. Source-side exit bans not modeled beyond hard overrides. Regimes that restrict departure but are not on the closed-regime list (PRK, ERI, TKM) will have their mobility overstated. The fragility-index proxy catches some of this but does not distinguish active emigration prohibition from generalized state breakdown.
2. The "hostile" scenario is acquirer-agnostic at the sovereign level. The same target produces the same hostile-acquisition brain-drain regardless of who the acquirer is. City-level pairwise computation in city_sapa_engine.compute_brain_drain does carry acquirer context, but the sovereign-level coefficient does not.
3. EU freedom-of-movement may slightly under-credit. EU/EEA/CH origins receive a destination-score floor of 0.85, but real EU-internal mobility for skilled professionals is closer to functionally complete. The 0.85 floor is conservative.
4. FX depreciation captured only via 3-year window. Currencies that have weakened gradually over longer horizons (e.g., 5 to 10 year structural depreciation) without showing in the 3-year monthly close series are under-penalized. Adding a longer FX window is a refinement.
5. Bilateral-pair acquirer corridor not modeled. The unilateral mobility factor scores an origin's aggregate capacity to flee, not its capacity to flee toward a specific acquirer. For acquirer-specific predictions the bilateral model in bilateral_mobility.py remains the appropriate tool.

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."

How alliance value is computed: each membership contributes its conservative GDP premium plus an FDI boost of (FDI_multiplier − 1) × GDP × 0.50. For every alliance the model actually assigns, the FDI boost is larger than the GDP premium, so it is the bigger half of alliance value.

Bilateral security ties: Blocs are clubs, so bilateral protection is counted separately, from data/bilateral_defense_ties.json: defence treaties from ATOP v5.1 (in force through 2018), US treaties confirmed in the State Department's Treaties in Force 2025, the 22 US Major Non-NATO Allies, and dated post-2018 pacts (Russia-North Korea 2024, Saudi Arabia-Pakistan 2025). Only the protected party is credited: a US defence treaty adds 8% of GDP (NATO members excluded, since NATO already carries it), Major Non-NATO Ally status 3%, and any other bilateral defence pact 2%, each with an FDI boost as above. These three premiums are uncalibrated priors. A direct defence treaty between buyer and target blocks coercion in SAPA, as a shared bloc does.

What the per-alliance transfer columns are not: the "Transfer to Hostile" percentages below describe the alliance definitions, but the valuation engine does not read them. In the hostile scenario it applies one flat loss of 0.80 to a country's total alliance value, whichever alliances that value came from.

Western Alliances

Eurasian & Non-Western Security Blocs

Regional Economic Communities

African Regional Blocs

Integration Friction Formula

Ideology modulates the base alliance-derived friction (0.0 / 0.10 / 0.30) with a continuous multiplier:

The modifier range (0.70 to 1.15) is intentionally asymmetric: high alignment can meaningfully reduce friction, but ideological opposition alone cannot create more friction than geographic and military barriers already impose.

Composite Alignment Score (-1.0 to +1.0)

Seven weighted signals combine into a single alignment score for each buyer-target pair:

38 Ideology Types, Pairwise Scoring (0.0 to 1.0)

The compatibility matrix measures observed historical interaction patterns between ideology families and makes no normative judgments about political systems. Scores reflect documented cooperation, conflict, and institutional compatibility:

• High compatibility (0.7 to 1.0): Liberal democratic pairs (US-UK), Sunni solidarity blocs (Saudi-Pakistan), authoritarian alignment (Russia-China)
• Medium compatibility (0.3 to 0.7): Mixed-regime pairs with pragmatic cooperation despite ideological distance
• Low compatibility (0.0 to 0.3): Ideological adversaries (US-Iran), theocratic-secular mismatches

The matrix expanded from 18×18 to 38×38 to capture sub-denominational distinctions (Sunni vs. Shia, Protestant vs. Catholic) where geopolitically significant.

25 Alliances, 166 Countries

Distinct from traditional security alliances (NATO, CSTO), ideology-driven alliances reflect shared civilizational, religious, or governance affinity. These create latent integration pathways that reduce friction even absent formal military treaties:

• Religious blocs: OIC (57 members), Arab League (22), Buddhist cooperation networks
• Governance blocs: Five Eyes (5), EU (27), BRICS+ (expanded membership), GCC (6)
• Regional identity: African Union (55), ASEAN (10), Pacific Islands Forum (18)

Alliance overlap is log-weighted to avoid over-counting pairs that share many blocs; the marginal integration benefit of the 5th shared alliance is smaller than the 1st.

102 Geopolitical Events (1792 to Present)

The historical events dataset validates ideology alignment scores against real-world outcomes. Each event is tagged with the ideology alignment of the participating states, whether the interaction was cooperative or conflictual, and the observed outcome:

• Cooperative events: Treaty signings, alliance formations, peaceful reunifications, economic unions
• Conflictual events: Invasions, proxy wars, sanctions campaigns, ideological containment

These events serve as ground truth for calibrating the alignment formula: pairs with high cooperative history receive positive modifiers, pairs with conflictual history receive negative modifiers.

How Ideology Changes Acquisition Viability

Ideology acts as a modulator: it adjusts the integration friction term within SAPA, but does not override strategic impulse or coercion feasibility:

Methodological Note: Observable Patterns, Not Normative Judgments

All ideology scoring reflects documented historical patterns (how states with given ideology profiles have actually interacted) and makes no value judgments about political systems or religions. High compatibility between authoritarian states (e.g., Russia-China) reflects observed coordination and implies no endorsement. Low compatibility between theocratic and secular states reflects documented friction and implies no cultural hierarchy.

Religious demographic data uses standard academic categories (Pew Research Center taxonomy). Sub-denominational splits (Sunni/Shia, Protestant/Catholic) are included only where geopolitically significant and supported by quantitative evidence.

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:

Display only; not applied to the price

The Lift Index is a descriptive layer. It does not enter the published sovereign prices. The engine that writes them applies no GDP-quality adjustment for lift; a lift-based industrial-value adjustment exists in an older valuation module that is not on the path producing the prices shown on this site. Earlier versions of this page described that adjustment as though it were live.

What the index is useful for stands unchanged: it separates territorial production from resident income, and flags where an acquirer would be buying mostly flow-through capital.

Current readings

• Ireland +31.4%: a large share of recorded Irish output is foreign capital flowing through.
• Luxembourg +31.9%: extreme phantom-GDP concentration in global finance.
• Japan -6.3%: a creditor position: residents earn more abroad than foreigners earn in Japan.
• United States +0.6%: close to balanced, as a large economy that is both a destination and a source of investment income.

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 as well as 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: Human capital flight is modeled separately as a liability (via the Human Capital Resilience metric). 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.

The two sources are blended because they cover different sets of countries. The Fragile States Index reaches 84 of the 217 countries in the economic baseline; V-Dem and Freedom House together reach 197 and 197. Where only one source is present, that source stands alone; where neither is, the multiplier is 1.0.

Extremes (the 84 entities scored by both sources, where the multiplier is fully determined):

• Most Fragile:
  • SSD: FSI 109 (×0.584)
  • SDN: FSI 109.3 (×0.585)
  • SYR: FSI 107.5 (×0.586)
• Most Stable:
  • FIN: FSI 14.3 (×0.958)
  • ISL: FSI 15.2 (×0.957)
  • LUX: FSI 20.2 (×0.942)

Across the whole priced set the multiplier runs from 0.58 to 1.00, but the ceiling is reached by entities with neither fragility nor political-freedom data. A 1.00 multiplier means "not scored" and does not indicate "perfectly stable".

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)

What the engine books: the United States carries a pension liability of $14.6T and Spain $2.7T, both after the 0.25 factor. Countries outside the source's coverage carry zero, which understates their obligation.

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 0.25 factor to reflect this nuance.

Updated Asset/Liability Formula

Impact Summary:

• Soft Power: USA gains $26.0T, significantly boosting total value
• Diaspora: India gains $1.4T, Philippines $400B+
• Fragility: the lowest stability multiplier in the priced set is 0.58 (SSD), i.e. a 42% write-down of base assets
• Pensions: Spain adds $2.7T of liability; the USA adds $14.6T

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 to 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). Fetched live from the World Bank API: 200 countries with both figures in the same year, global mean +1.23%, running from -62.8% (Tuvalu) to +32.3% (Puerto Rico (US)). Display only; it is not applied to the price (Section 9)

51 Cases (1776 to 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 to 1.5: City punches above its weight ("Leveraged")
• λ 1.5 to 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 is clamped at 3.00. The cap is common and does not mark city-states: 27 of the 74 scored cities reach it, sovereign city-states among them but also large metros whose sub-scores saturate. Hong Kong, previously listed here as a cap case, scores 0.78: it is not flagged as already-sovereign in the scores file, so its λ comes from the ordinary sub-score blend; Singapore and Luxembourg receive the fixed ceiling.

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 to 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.

84 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: GBR 0.8650, USA 0.8601, DNK 0.8333, AUS 0.8299, SGP 0.8293.

City-level PSI: National score adjusted by the city's Cloud layer percentage from Bratton decomposition. The highest city PSI is Boston at 0.8171 (Cloud 0.45); San Francisco follows at 0.7827 (Cloud 0.41). Cities with high Earth layers have lower platform sovereignty exposure.

84 Platforms × 74 Cities = 6,216 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 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 to 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, 217 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 friction, with no military term:

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: 524 deals priced, of which 227 clear the 10% viability threshold and are displayed. 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 the best: Rank by viability score and take the top coalitions, up to a cap of 40. 22 survive the deduplication and constraint filters in the current run. Each receives a Claude Opus-authored narrative describing governance structure, economic logic, and strategic risks

Output: network_state_coalitions.json: 22 coalitions from the 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:

Human Capital Flight Cascade

Skilled workers follow economic opportunity into the detached city. Emigration accelerates over four horizons, calibrated against post-Soviet emigration (75-90% retained), Greek crisis (88% retained), and Venezuelan collapse (85% retained):

7-Component Sovereign Viability Score

Combines institutional capacity with economic shock severity:

States are classified as Viable/Diminished (≥60%), Struggling/Severe Crisis (≥35%), Failing/Terminal Decline (≥15%), or Collapse Trajectory/Non-Viable below that. City-states (Singapore, Luxembourg, Hong Kong) score 0% by definition, since 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, 30 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 to 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 3.56B 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 5.85B (71.5%). 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, of which 30 fall below the floor.

Residual Human Capital Layer

Bridges Phase 8 into Phase 9 by computing post-detachment DFI for each of the 49 host nations that carry a residual human-capital scenario. 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

18 Archetype Configurations

Budget fraction and transfer effectiveness read "not recorded" for the archetypes whose generator does not write them into optimal_configurations.json. Those archetypes do use both internally; the values simply are not published yet, and this page will not restate a number the output does not carry.

Transition Feasibility (5 Components)

The fundamental tradeoff: dignity compliance and transition feasibility are inversely correlated. Fiscal Federalism achieves 56.9% compliance with 0.81 feasibility; Full Optimization achieves 97.0% compliance with 0.13 feasibility. Climate Resilience Tax shows the fiscal ceiling: even with unlimited funding, compliance stops at 62.3% because political participation cannot be purchased. Benevolent Global Monarch removes that ceiling by mandating institutional reform, reaching 100.0%, but requires authority that has never existed, and scores 0.02 on feasibility. There is no configuration that simultaneously maximizes both compliance and feasibility.

Conceptual Territory: Archetypes I to O

Archetypes A to H model governance arrangements that build on or reform existing state structures. Archetypes I to 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. Q and R ask whether delivery has to run through the state at all, and whether one floor can be defined for the whole world.

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 (algorithms hold authority directly), 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, in a world where people believe AI is better than human governance. Social connection is preserved (M destroys it), 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.

Sovereign Bypass / Disintermediation (Q) routes the floor around the treasury: direct transfers, index- and satellite-triggered parametric payouts, platform-delivered services, subnational revenue capture. It is the archetype that treats state fiscal absorption as the binding constraint, with the amount of money available a secondary factor. Compliance lands close to Fiscal Federalism, on a noticeably lower feasibility score, because the delivery infrastructure has to exist before the state consents to being bypassed.

Cultural & Civilizational Sovereignty (R) lets civilizational blocs define and fund their own floors, with no single standard applied to every country. It trades comparability for consent. The compliance figure it earns here is therefore a weaker claim than the others in this table: it is scored against our seven dimensions, which is precisely the premise the archetype rejects.

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), a figure driven by the model’s institutional bias. GDP per capita does not capture cooperative production. V-Dem indices cannot distinguish direct democracy from no democracy.

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: 228 sovereign entities, 18 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 to 13) extends the country-level valuation (Sections 1 to 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 that measure structural feasibility; they are not calibrated probabilities of 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, human capital flight, 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

Reproducibility

Re-running the pipeline on unchanged inputs reproduces every engine output byte for byte, apart from the generation timestamps each file carries. Matching, coalition formation and deal pricing are deterministic and do not depend on set iteration order or the Python hash seed.

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 18 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, Sovereign Bypass, Cultural & Civilizational Sovereignty) each operate on different time horizons, and confidence degrades at different rates depending on the archetype assumed. Use this as a starting point.

Platform Sovereignty Data Limitations

The platform sovereignty analysis scores 50 countries and 74 cities directly; every other sovereign entity relies on modelled platform data (Tiers 2 to 3) in the absence of observed metrics. The inequality-adjusted dignity floor count (5.85B people) depends on World Bank poverty headcount data quality, which covers 182 countries with varying recency (2014 to 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.