Imagine a systemic conflict that breaks trade routes, constrains investment and raises the cost of security. Which region comes out ahead? The answer may be the one that loses the least.
An author-supplied scenario map, inspired by George Modelski’s work on cycles of global leadership, calls India an “all-weather” winner. Its conflict scenario casts South Asia as a relative beneficiary of diverted manufacturing. Yet the same map assigns South Asia negative scores in four of eight sectors, positive scores in three, and a neutral score in one. Its economy is scored −2, even while its construction industry is scored +2. That is a relative advantage inside a damaging future, not evidence of general prosperity.
A relative winner can still decline.
The source scores seven regions across eight sectors. The conflict case is shown below; the complete matrix follows it. With scripts enabled, choose a future and sector to test the label “winner”.
Conflict: South Asia has the least negative equal-weight mean (−0.125), but four of its eight sectors decline.
| Region | Score | Sector balance |
|---|---|---|
| North America | −0.75 | 5 down · 0 flat · 3 up |
| Europe | −3.25 | 8 down · 0 flat · 0 up |
| East Asia | −4.375 | 8 down · 0 flat · 0 up |
| South Asia | −0.125 | 4 down · 1 flat · 3 up |
| Africa | −1.875 | 7 down · 1 flat · 0 up |
| Latin America | −0.875 | 5 down · 3 flat · 0 up |
| Oceania | −0.625 | 4 down · 3 flat · 1 up |
Scores are ordinal author judgments versus a 2025 baseline, not measured impacts. Equal-weight means are only a diagnostic of the source matrix. “Down” counts a negative cell; it does not measure population affected.
Inspect all 168 source scores
Systemic conflict
| Sector | North America | Europe | East Asia | South Asia | Africa | Latin America | Oceania |
|---|---|---|---|---|---|---|---|
| Economy | −3 | −4 | −5 | −2 | −3 | −2 | −2 |
| Science & technology | +1 | −2 | −3 | +1 | −1 | 0 | +1 |
| Education | −1 | −3 | −4 | 0 | −2 | −1 | 0 |
| Birth rates | −2 | −3 | −4 | −1 | 0 | −1 | −1 |
| Housing | −2 | −4 | −5 | −1 | −2 | −1 | −2 |
| Infrastructure | +1 | −3 | −5 | +1 | −2 | 0 | 0 |
| Construction | +1 | −3 | −4 | +2 | −2 | 0 | 0 |
| Finance | −1 | −4 | −5 | −1 | −3 | −2 | −1 |
US-led renewal
| Sector | North America | Europe | East Asia | South Asia | Africa | Latin America | Oceania |
|---|---|---|---|---|---|---|---|
| Economy | +3 | +1 | −1 | +3 | +1 | +1 | +2 |
| Science & technology | +4 | +1 | −2 | +2 | 0 | 0 | +2 |
| Education | +3 | +1 | −1 | +2 | +1 | +1 | +2 |
| Birth rates | 0 | −1 | −2 | 0 | 0 | 0 | 0 |
| Housing | +2 | +1 | −1 | +2 | +1 | +1 | +1 |
| Infrastructure | +3 | +1 | 0 | +3 | +1 | +1 | +2 |
| Construction | +3 | +1 | −1 | +3 | +1 | +2 | +1 |
| Finance | +3 | +1 | −2 | +2 | 0 | 0 | +1 |
Cooperative breakthrough
| Sector | North America | Europe | East Asia | South Asia | Africa | Latin America | Oceania |
|---|---|---|---|---|---|---|---|
| Economy | +2 | +3 | +2 | +3 | +3 | +2 | +2 |
| Science & technology | +3 | +3 | +3 | +2 | +2 | +1 | +2 |
| Education | +2 | +3 | +2 | +3 | +4 | +2 | +2 |
| Birth rates | +1 | 0 | −1 | 0 | +1 | 0 | 0 |
| Housing | +1 | +2 | +1 | +3 | +2 | +1 | +1 |
| Infrastructure | +2 | +3 | +2 | +4 | +4 | +2 | +2 |
| Construction | +2 | +3 | +2 | +4 | +4 | +2 | +2 |
| Finance | +1 | +2 | +1 | +2 | +2 | +1 | +1 |
Relative winners and absolute outcomes
Among the seven regions in the source matrix, South Asia has the least negative simple average in the conflict case: −0.125 on the author’s −5 to +5 scale. East Asia is scored −4.375; Europe −3.25. The comparison is revealing, but the decimal places are not a measurement. These are ordinal judgments, and averaging them gives each sector and each region equal weight. An economy, a birth rate and a construction market do not share a common unit of welfare.
The instrument above lets you change the scenario and the sector. Start with conflict, then switch from “all sectors” to economy or construction. A label such as “winner” depends on the measure chosen. The complete 168-cell score matrix is available in the companion investigation, so the result can be challenged without trusting a graphic.
What the historical theory can and cannot do
Modelski’s Long Cycles in World Politics offers a way to think about changes in global leadership. It does not supply the three scenarios, the 40/35/25 probability split, or these sector scores. Those are the author’s speculative extensions. A historical pattern may suggest questions about coalitions and challengers; it cannot assign a 2060 impact score to African education or European housing.
One empirical anchor in the source is real and dated: SIPRI estimated US military spending at $997 billion and Chinese spending at $314 billion in 2024. That comparison describes expenditure, not usable capability, war outcome or the probability of US renewal. The original file also uses demographic and economic references without a reproducible calculation connecting them to each cell. They help frame a scenario; they do not validate its numbers.
The strongest objection
The source names India, while the grid scores South Asia. A region can have a slightly negative average while a particular country gains. India might attract manufacturing even if its neighbours face different costs. The regional table therefore cannot disprove the country-level claim. It shows that the claim needs a country-level matrix, a definition of “win”, and an account of which conflict, trade routes and coalition choices are assumed.
There is another trap in the cooperative scenario. It assigns positive scores to almost every region and sector. That is a useful counterfactual, but cooperation itself is the difficult condition. If it fails, the outcome changes. A 25% label in the original file is an uncalibrated subjective weight, not a measured chance of peace.
A testable version of the question
The next model should define the shock and its horizon, score India separately from South Asia, and state weights for economic output, physical safety and household welfare before naming a winner. It should also record which observable changes would move the analysis from conflict toward US renewal or cooperation. Until then, “all-weather” is a hypothesis about resilience, not a result.
Source and status, 1 October 2026. The scenario labels and 168 impact scores are transcribed from the author-supplied Czech HTML file modelski_impact.html (undated, received 1 October 2026; horizon 2026–2060 versus a 2025 baseline). The scenario probabilities and the scores are subjective judgments. The equal-weight averages and sign counts are AU calculations from those scores. For the historical framing, see George Modelski, Long Cycles in World Politics (1987); for the dated expenditure example, see SIPRI’s 2024 military expenditure fact sheet. No country-level finding has been inferred from the regional grid. Inspect the full matrix and method. Version 1; no corrections recorded.