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Calculated Punishment
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DOI：10.1007/s10551-024-05865-y.png)
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Punishment is fundamental to the evolution of cooperative norms in teams, organizations, and societies. Based on findings that people are faster when punishing others (relative to when withholding punishment), dual-process theories of punishment assert that humans have an intuitive tendency to punish, which requires effortful deliberation to overcome. Here, we propose an alternative single-process theory that models punishment decisions as a sequential sampling process. We provide supporting evidence for this theory using a public goods game experiment that experimentally manipulates the cost-benefit tradeoff across the game. We show that people are not systematically faster when punishing (versus withholding) across tradeoffs. We also find an inverted-U-shaped relationship between response times and the strength of preferences for punishing, and a negative association between punishment rates and the relative speed of punishment across individuals. Further computational analysis using the drift-diffusion model (DDM) reveals that, on average, people exhibit a pre-disposition to withhold punishment. Our study provides a unified single-process framework for studying the micro-foundations of punishment and integrating process measures to better describe and predict behavior.
Keyword:
Prosocial and antisocial punishment
Sequential sampling process
Computational modeling
Response times
期刊
IF:
6.7
论文数:
1.0W
被引数:
5.6W
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