An Economic Model of Human Cooperation Based on Indirect Reciprocity and Its Implication on Environmental Protection
Bibliographic record
Abstract
There has been an urgent challenge for environmental protection due to issues like population increase, climate change, and pollution. To address this challenge, sustained human cooperation is critical. However, how cooperation in human beings evolves is one of the 125 most challenging scientific questions, as announced by Science in its 125th anniversary. In this paper, we contribute to answering this question by building an economic game model based on indirect reciprocity and altruism behavior. In our model, there are three types of participants: cooperator, defector, and discriminator. In every round of the game, the cooperator chooses cooperation, the defector chooses non-cooperation, and the choice of the discriminator depends on the choice of his partner in the last round. Our analysis and main result shows that there is no stable evolution equilibrium in this game, which implies that the proportions of different types of players will keep changing instead of reaching a stable equilibrium. In other words, there is no guarantee that cooperation will be dominant in this game. An implication of this result is that to achieve cooperation and protect the environment more effectively, cooperators and discriminators in our society should be provided with incentives.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".