Emotions in Games: Toward a Unified Process-Level Account
Bibliographic record
Abstract
Strategic decision-making is chiefly studied in behavioral economics using multi-agent games. Decades of empirical research has revealed that emotions play a crucial role in strategic decision-making, calling into question the “emotionless” homo economicus. In this work, we present a unified process-level account of a broad range of empirical findings on the effect of emotions in Prisoner’s Dilemma and Ultimatum games—the two most studied games in behavioral sciences. Under the empirically well-supported assumption that emotions modulate loss aversion, we show that Nobandegani et al.’s (2018) sample-based expected utility model can account for the effect of emotions on: (i) cooperation rate in Prisoner’s Dilemma, and (ii) the rejection rate of unfair offers in the Ultimatum game. We conclude by discussing the implications of our work for emotion research, and for developing a unified process-level account of the role of emotions in strategic decision-making.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".