Bibliographic record
Abstract
This paper studies the optimal design of a liability-sharing arrangement as an infinitely repeated game.We construct a noncooperative model with two agents: one active and one passive.The active agent can take a costly and unobservable action to reduce the incidence of crisis, but a crisis is costly for both agents.When a crisis occurs, each agent decides unilaterally how much to contribute mitigating it.For the one-shot game, when the avoidance cost is too high relative to the expected loss of crisis for the active agent, the first-best is not achievable.That is, the active agent cannot be induced to put in effort to minimize the incidence of crisis in a static game.We show that with the same stage-game environment, the first-best cannot be implemented as a perfect public equilibrium (PPE) of the infinitely repeated game either.Instead, at any constrained efficient PPE, the active agent "shirks" infinitely often, and when crisis happens, the active agent is "bailed out" infinitely often.The frequencies of crisis and bailout are endogenously determined in equilibrium.The welfare optimal equilibrium being characterized by recurrent crises and bailouts is consistent with historical episodes of financial crises, which features varying frequency and varied external responses for troubled institutions and countries in the real world.We explore some comparative statics of the PPEs of the repeated game numerically.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".