Alignment Patterns, Crisis Bargaining, and Extended Deterrence: A Game-Theoretic Analysis
Bibliographic record
Abstract
To explore the impact of alignment patterns in a rudimentary state system, we develop and analyze the Tripartite Crisis Game, a three-person game among Challenger, Defender, and Protégé. This model captures some of the tensions implicit in the “Alliance” and “Adversary” games, two related but theoretically isolated models due to Snyder. Our analysis enables us to delineate and explore the circumstances that give rise to the “deterrence versus restraint” dilemma. It also provides an answer to Fearon's empirical puzzle: when convincing commitments are possible, why are halfhearted signals sometimes sent? Our most surprising result concerns the impact of Protégé's threat on Challenger's optimal behavior. When Challenger is willing to fight to back up its demand, but is nonetheless only weakly or moderately motivated, Protégé's threat to realign—though directed at Defender— can dissuade Challenger from initiating a crisis. But when Challenger is willing to fight and stands to gain a great deal, Protégé's threat may actually prompt Challenger to make a demand. Our analysis uncovers this unexpected pattern of behavior and suggests when it occurs. That Protégé's threat to realign sometimes bolsters deterrence, and sometimes undermines it, has implications for the selection bias issue in studies of alliance reliability and helps to explain why some alliances are stabilizing while others are associated with crises and war. The nonlinear consequences of Protégé's commitment seem to us to constitute another “paradox of war.”
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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) | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".