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Calculated Punishment

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Abstract

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.

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Data and Code Availability

The data and code used to analyze it as well as the experimental program are publicly available at https://doi.org/10.17605/OSF.IO/FBZ9T.

Notes

  1. Dual-process theories differ in their assumptions about the timing of different processes and whether they are exclusive (De Neys 2023; Bago and De Neys 2019, 2017).

  2. Although the moral tradeoff system operates unconsciously, conscious deliberations can play a role in judgment. For example, arguments and reflection can change which social cognitive systems are activated, and this can affect intuitive judgment.

  3. The sequential sampling framework, in particular the DDM, has been shown to adequately characterize decisions and their RTs across domains including value-based economic decisions (Amasino et al., 2019; Chen et al., 2024a; Clithero 2018; Stewart et al., 2016), social decisions (Chen and Krajbich 2018; Chen et al., 2024b; Hutcherson et al., 2015; Son et al., 2019), perceptual decisions (Frydman and Nave 2017; Polanía et al., 2014), moral judgment (Baron and Gürçay 2017; Yu et al., 2021), conformity (Tump et al., 2020), and ethical decisions (Wu et al., 2021).

  4. Research has shown that behavior in the PGG does not significantly differ between one-shot and multiple rounds played with random stranger-matching (Fehr and Gächter 2000).

  5. This caused some delay in comparison to the pure RT. However, the delay is uncorrelated with any aspects of the decision, as the sequence of prosocial/antisocial and punishment/non-punishment decisions was randomized (Chen and Fischbacher 2020).

  6. Although the two decisions were randomly selected after participants finished the two tasks and we told participants that the two tasks were independent (Online Appendix F), participants might hedge in one of the two decisions.

  7. The punishment rates under punishment level 2:2 are lower than those under punishment 2:8 (two-sided Wilcoxon signed rank test, p < 0.001 for both prosocial and antisocial situations). The punishment rates under punishment level 2:8 are lower than those under punishment level 0:2 (p < 0.001 for both prosocial and antisocial situations). The punishment rates under punishment 0:2 are lower than those under punishment level 0:8 for antisocial situations (p = 0.031), but not for prosocial situations (p = 0.352).

  8. The punishment pattern is similar to that in Herrmann et al. (2018), as shown in Online Appendix Fig. A3.

  9. Additional regressions including controls for period and decision number (decision sequence within each period) but without interactions between the three factors are shown in Online Appendix Table A2. Additional regressions including controls for gender and age are shown in Online Appendix Table A3. And additional regressions that pool prosocial and antisocial punishment together with a dummy for prosocial situation are shown in Online Appendix Table A4.

  10. The experimental program was stuck for a few seconds after period 16 in one of the sessions, which resulted relatively greater RTs in Period 17. Excluding these data does not qualitatively change our results.

  11. This procedure, by its nature, addresses potential issues of overfitting. We replicate all results if the procedure changed, i.e., from odd trials to even trials (see Online Appendix B).

  12. Figure A4 (panels A and B) in Online Appendix A displays the relationship between the utility difference and RTs in odd trials, which is similar to the relationship between the utility difference and logged RTs.

  13. Figure A4 (panels C and D) in Online Appendix A displays the relationship between the punishment rates and RT difference between punishing and withholding in odd trials, which is similar to the relationship between the punishment rates and logged RT difference between punishing and withholding.

  14. As participants did not know whether the situation was prosocial or antisocial until the information appeared on the decision screen, the situation indicator and other contextual factors do not affect the prior (z) in our model.

  15. If we excluded the six participants with low SVO angle (1 participant with − 16.26, and 5 participants with − 7.82), the SVO angle was correlated with the tendency to punish in antisocial situations (r(126) = − 0.188, 95% CI = [− 0.35, − 0.01], t(124) = − 2.13, p = 0.035), but not in prosocial situations (r(126) = − 0.092, 95% CI = [− 0.26, 0.09], t(123) = − 1.02, p = 0.309). Heterogeneity in punishment behavior across participants with different SVO levels can be partly attributed to the evaluative tendencies during the decisions process (see Online Appendix E).

  16. For instance, the cost–impact ratio is 1:3 in Stüber (2020).

  17. The aphorism “revenge is best served cold” suggests that punishment could also arise from cold deliberation.

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Acknowledgements

Fadong Chen gratefully acknowledges support from the National Natural Science Foundation of China (Grant Nos. 72322009, 72173113, 71803174), Zhejiang Provincial Philosophy and Social Sciences Planning Project (23SYS04ZD), and the German Research Foundation (DFG) through research unit FOR 1882 “Psychoeconomics.” Gideon Nave gratefully acknowledges support from the Wharton Neuroscience Initiative and Carlos and Rosa de La Cruz. Lei Wang gratefully acknowledges support from the Ministry of Science and Technology [STI 2030—Major Projects 2021ZD0200409] and the National Natural Science Foundation of China (Grant Nos. 72371226, 71871199).

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Chen, F., Nave, G. & Wang, L. Calculated Punishment. J Bus Ethics 200, 715–731 (2025). https://doi.org/10.1007/s10551-024-05865-y

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