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Published on: June 5, 2016
The cannibalistic trade-off: Why human cannibalism emerges and why taboos suppress it
Michal Misiak1,2, Petr Tureček3,4
1Institute of Psychology, Faculty of Historical and Pedagogical Sciences, University of Wrocław, Wrocław 50-527, Poland.
Abstract:
Cannibalism is among the most widespread taboos in human societies, yet archaeological, ethnographic, and historical evidence indicates that it has repeatedly emerged across human populations. This coexistence of recurrent practice and persistent prohibition raises a fundamental question: when does cannibalism become adaptive, and what mechanisms lead to its suppression? We address this problem using a formal model that treats cannibalism as a potential food source subject to energetic benefits and multiple sources of cost. Nutritional gains are modeled using a saturating function of caloric intake, while costs arise from acquisition, digestion, and infection. Infection costs are represented as a stochastic process whose mean increases with the length of the trophic transmission chain, capturing the risks associated with repeated within-species consumption. Analyzing the expected energetic balance across levels of food availability and cannibalism order reveals narrow ecological conditions in which cannibalism yields a positive expected balance and broader conditions in which it is strongly disfavored. The model provides a framework for interpreting archaeological and ethnographic findings by specifying boundary conditions and identifying the most probable ecological scenarios under which different forms of cannibalism are expected to occur. The results predict that cannibalism is most likely to emerge under extreme resource scarcity, when acquisition costs are low and infection risks are constrained, while sustained cannibalism rapidly becomes unviable due to escalating infection costs. Overall, the findings suggest that cannibalism is best understood as a conditional trade-off rather than a behavioral anomaly, with cultural taboos functioning as adaptive responses to nonlinear epidemiological risks.
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