Precommitment in Stochastic Versus Deterministic Social Dilemmas
Bibliographic record
Abstract
ABSTRACT Many real‐world social dilemmas require interdependent people to repeatedly protect against a large loss that has a low probability of occurring. Examples include protecting against disease outbreak (e.g., COVID‐19), terrorism (shared border security), or extreme weather events (from climate change). Decisions on whether to invest in protection may be made period by period (e.g., month by month), or investment may be precommitted in advance for a number of periods. How does precommitment influence cooperation in these situations? A series of four studies (plus one supplemental study) investigates this question, using incentive‐compatible, repeated social dilemmas with large‐magnitude, low‐probability losses. These studies found that in stochastic social dilemmas, binding precommitment increases cooperation, but nonbinding precommitment has little effect, and in deterministic social dilemmas, binding precommitment decreases cooperation. These patterns were driven by changes in responsiveness to probabilities and interactions with counterparts, with implications for how to structure real‐world dilemmas to increase cooperative investment in protection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".