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Bringing together “old” and “new” ways of solving social dilemmas? The case of Spanish Gitanos

Espín, Antonio M. and Brañas-Garza, Pablo and Gamella, Juan and Herrmann, Benedikt and Martin, Jesus (2019): Bringing together “old” and “new” ways of solving social dilemmas? The case of Spanish Gitanos.

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Humans often punish non-cooperators in one-shot interactions among genetically-unrelated individuals. So-called altruistic punishment poses an evolutionary puzzle because it enforces a cooperation norm that benefits the whole group, but is costly for the punisher. Under the “big mistake” (or “mismatch”) hypothesis, social behavior such as punishment evolved by individual selection at a time when repeated interactions with kin prevailed. It then misfired in modern humans, who “mistakenly” apply it in sporadic interactions with unrelated individuals. In contrast, cultural group selection theories emphasize cultural differences in normative behavior and the role of intergroup competition and punishment for the emergence of large-scale cooperation in the absence of genetic relatedness. We conducted a series of multilateral-cooperation economic experiments with a sample of Spanish Romani people (Gitanos), who represent a unique cultural group to test the predictions of the two accounts: Gitano communities rely heavily on close kin-based networks, maintain high consanguinity rates and display a particularly strong sense of ethnic identity. A total of 320 Gitano and non-Gitano (i.e., the majority Spanish population) participants played a one-shot public goods game with punishment in either ethnically homogeneous or ethnically mixed (half Gitano and half non-Gitano) four-person groups. In the homogeneous groups, punishment was commonly used by non-Gitanos but virtually inexistent among Gitanos. In the mixed groups, however, Gitanos who did not cooperate were severely punished by other Gitanos, but also by non-Gitanos (particularly males in both cases). The results are more consistent with cultural group selection and also qualify some of its predictions.

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