Rationality, Imitation, and Rational Imitation in Spatial Public Goods Games
Bibliographic record
Abstract
In both economic and evolutionary theories of games, two general classes of evolution can be identified: 1) dynamics based on myopic optimization and 2) dynamics based on imitations or replications. The collective behavior of structured populations governed by these dynamics can vary significantly. Particularly in social dilemmas, myopic optimizations typically lead to Nash equilibrium payoffs that are well below the optimum, e.g.,the tragedy of the commons, whereas imitations can hinder equilibration while allowing higher cooperation levels and payoffs. Motivated by economic and behavioral studies, in this article, we investigate how the benefits of the two dynamics can be combined in an intuitive decision rule,rational imitation, that is to mimic successful others only if it earns you a higher payoff. In contrast to purely rational (best-response) or purely imitative decision rules, the combination inrational imitationdynamics both guaranteesfinite time convergenceto an imitation equilibrium profile on arbitrary networksandcan facilitatehigh levels of cooperationfor small public goods multipliers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".