Voluntary Contributions to a Mutual Insurance Pool
Bibliographic record
Abstract
Abstract We study mutual‐aid games in which individuals choose to contribute to an informal mutual insurance pool. Individual coverage is determined by the aggregate level of contributions and a sharing rule. We analyze theoretically and experimentally the ( ex ante ) efficiency of equal and contribution‐based coverage. The equal coverage mechanism leads to a unique no‐insurance equilibrium while contribution‐based coverage develops multiple equilibria and improves efficiency. Experimentally, the latter treatment reduces the amount of transfers from high contributors to low contributors and generates a “dual interior equilibrium.” That dual equilibrium is consistent with the co‐existence of different prior norms which correspond to notable equilibria derived in the theory. This results in asymmetric outcomes with a majority of high contributors less than fully reimbursing the global losses and a significant minority of low contributors less than fully defecting. Such behavioral heterogeneity may be attributed to risk attitudes (risk tolerance vs risk aversion) which is natural in a risky context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".