A model of emotion in the prisoner’s dilemma
Bibliographic record
Abstract
This paper adopts the definition ldquoan emotion is a scalar summary of complex environmental circumstancesrdquo in the context of the iterated prisonerpsilas dilemma. Prisonerpsilas dilemma players, represented both as look-up tables and artificial neural nets, are evolved with and without emotion and noise. The availability of emotion is found to have a substantial impact on the evolution of cooperation and interacts with noise in a complex manner. The impact of having a single bit of emotional information on lookup tables is also found to be different from the analogous impact on neural nets. The emotion used in this experiment is a single bit which is set if an agentpsilas opponent has defected into cooperation more often than the agent itself has done so. This simple emotion has an substantial, non-uniform impact on the behavior of evolving populations of prisonerpsilas dilemma agents. The emotion implemented in this study is only one of many possible emotions, suggesting that even this limited and mathematically tractable definition of emotion yields a rich collection of possible research topics. The most recognizable impact of adding emotion to agents in this study is to move their behavior away from the middle of the behavioral spectrum toward either sustained cooperation or defection.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".