Coalition formation: a game-theoretic analysis
Bibliographic record
Abstract
There are many examples of individuals forming coalitions to obtain or protect a valuable resource. We present an analytical model of coalition formation in which individuals seek alliances if they judge themselves too weak to secure the resource alone. We allow coalition seeking to carry an investment cost (θ) and let contest outcomes depend probabilistically on the relative fighting strengths of contesting parties, with effective coalition strength directly proportional to combined partner strength. We identify the evolutionarily stable strength thresholds, below which individuals within triads should seek a coalition. We show that if θ exceeds a critical value, then unilateral fighting over resources is an evolutionarily stable strategy (ESS). Universal (3-way) coalitions are also an ESS outcome if θ is less than a second critical value. Both of these extreme solutions are less likely to arise, the greater the variance in fighting strengths and the greater the benefit from dominating opponents. Our analysis also identifies intermediate solutions in which only the weaker individuals seek coalitions: only then can a true coalition (2 vs. 1) form. We characterize these ESSs and show that true coalitions are more likely to arise when the effective strength of a coalition is less than the sum of its individual strengths (antergy). Alliances in primates are characterized by antergy, high reliability of strength as a predictor of contest outcome, and high variability in strengths. These are precisely the conditions in which in our model most favors true coalition formation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".