Calculated Punishment

49 Pages Posted: 8 Jul 2020 Last revised: 14 Dec 2020

See all articles by Fadong Chen

Fadong Chen

Zhejiang University - School of Management

Gideon Nave

University of Pennsylvania - The Wharton School

Lei Wang

Zhejiang University - School of Management

Date Written: June 13, 2020

Abstract

Punishment is fundamental to the evolution of cooperative norms in organizations and societies, yet little is known about the decision processes that underlie it. Based on findings that people are faster when punishing (relative to when withholding punishment), scholars proposed that humans have an intuitive inclination to punish, where withholding punishment involves deliberation. Here, we report a public goods game (PGG) experiment that tests the generality of this dual-process hypothesis, and propose an alternative single-process theory of punishment decisions. By experimentally manipulating the cost and impact of punishment across the game, we show that punishment response times (RTs) are sensitive to cost-benefit tradeoffs, where participants are sometimes relatively slow to punish. The patterns in our data are at odds with the dual-process account of punishment RTs, yet confirm two key predictions of single-process sequential sampling models (SSMs): an inverted-U-shaped relationship between RTs and the strength of preferences for punishing, and a positive association between punishment rates and the relative speed of punishment across individuals. We further explore the utility of modelling punishment via SSMs in a computational analysis using the drift-diffusion model (DDM), and find that the model successfully incorporates choice and RT data to improve out-of-sample prediction of punishment behavior, compared to computationally naïve models. We conclude that punishment behavior arises from a value-computation process that shares a common mechanism with decisions in other domains, where the DDM provides a unified single-process framework for studying the micro-foundations of punishment and allows generating behavioral predictions using process measures.

Keywords: prosocial punishment; antisocial punishment; cooperation; drift-diffusion; computational modeling; response times

Suggested Citation

Chen, Fadong and Nave, Gideon and Wang, Lei, Calculated Punishment (June 13, 2020). Available at SSRN: https://ssrn.com/abstract=3626299 or http://dx.doi.org/10.2139/ssrn.3626299

Fadong Chen

Zhejiang University - School of Management ( email )

Hangzhou, Zhejiang Province 310058
China

Gideon Nave (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3730 Walnut St
JMHH Suite 700
Philadelphia, PA 19104-6365
United States

Lei Wang

Zhejiang University - School of Management ( email )

Hangzhou, Zhejiang Province 310058
China

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