Understanding and Addressing Cultural Variation in Costly Antisocial Punishment
Altruistic punishment (AP)—punishment of those contributing little to the public good—has been proposed as an explanation for the extraordinary extent of human culture relative to other species. AP is seen as supporting the high levels of altruism necessary for the cooperation underlying this culture, including information exchange. However, humans will also sometimes punish those who contribute greatly to the public good, even when those contributions directly benefit the punisher. This behaviour—antisocial punishment (ASP)—is negatively correlated with gross domestic product, and may be a hindrance to overall wellbeing. In this chapter, we pursue a better understanding of ASP in particular and costly punishment in general. We explore existing data showing cultural variation in the propensity to punish, and ask how such sanctioning, whether of those who give much or little, affects the individuals who conduct it. We hypothesise that costly punishment is a mechanism for regulating investment between different levels of society, for example, whether an individual’s current focus should be on their nation, village, family or self. We suggest that people are less likely to antisocially punish those they consider to be ‘ingroup’ and that the propensity to apply this identity to strangers may vary with socio-economic–political context and resulting individual experience. In particular, an increased propensity to express ASP should correlate with a lower probability of benefiting from public goods, as may be the case where there is a low rule of law. We show analysis of both behavioural economics experiments and evolutionary social simulations to support our hypotheses and suggest implications for policymakers and other organisations that may wish to intervene to improve general economic wellbeing.
KeywordsAntisocial punishment (ASP) Altruistic punishment (AP) Costly punishment Public goods Public goods games (PGG) Behavioural economics Altruism Cooperation Ingroup/outgroup assessment
We would like to thank Benedikt Herrmann for his advice and help with theory building, the literature, and his assistance in understanding his own data set. We would also like to thank to Simon Gächter for meetings and occasional e-mail assistance, and Daniel Taylor for many conversations and useful analysis. We thank Will Lowe for his help with data, statistics, software and analysis, and to Gideon Gluckman for support in writing. From October 2010 to September 2011, much of this effort was supported by the US Air Force Office of Scientific Research, Air Force Material Command, USAF, under grant number FA8655-10-1-3050. We would also like to thank the Department of Computer Science and the University of Bath for further financial support.
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