Back

The Cannibalistic Trade-Off: Why Human Cannibalism Emerges and Why Taboos Suppress It

Misiak, M.; Turecek, P.

2026-02-10 evolutionary biology
10.64898/2026.02.10.705014 bioRxiv
Show abstract

Cannibalism is among the strongest and most widespread food taboos in human societies, yet archaeological, ethnographic, and historical evidence indicates that it has repeatedly emerged across diverse human populations. This coexistence of recurrent practice and persistent prohibition raises a fundamental question: when does cannibalism become adaptive, and why is it typically suppressed? 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 modelled 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. Analysing 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 disfavoured. 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 under extreme resource scarcity, when acquisition costs are low and infection risks are constrained, while sustained or high-order 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 behavioural anomaly, with cultural taboos functioning as adaptive responses to nonlinear epidemiological risks.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Philosophical Transactions of the Royal Society B
51 papers in training set
Top 0.1%
18.5%
2
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 3%
14.5%
3
Proceedings of the Royal Society B: Biological Sciences
341 papers in training set
Top 0.7%
8.3%
4
The American Naturalist
114 papers in training set
Top 0.3%
6.3%
5
Journal of The Royal Society Interface
189 papers in training set
Top 0.8%
4.8%
50% of probability mass above
6
Philosophical Transactions of the Royal Society B: Biological Sciences
53 papers in training set
Top 0.1%
4.3%
7
Royal Society Open Science
193 papers in training set
Top 0.5%
3.9%
8
Evolution
199 papers in training set
Top 0.9%
3.5%
9
PLOS ONE
4510 papers in training set
Top 40%
3.5%
10
Nature Communications
4913 papers in training set
Top 45%
2.4%
11
Current Biology
596 papers in training set
Top 7%
2.3%
12
eLife
5422 papers in training set
Top 34%
2.3%
13
Science
429 papers in training set
Top 12%
2.1%
14
Science Advances
1098 papers in training set
Top 15%
1.9%
15
PLOS Biology
408 papers in training set
Top 10%
1.7%
16
Nature Ecology & Evolution
113 papers in training set
Top 3%
1.6%
17
PLOS Computational Biology
1633 papers in training set
Top 18%
1.5%
18
Evolution, Medicine, and Public Health
14 papers in training set
Top 0.2%
1.3%
19
Scientific Reports
3102 papers in training set
Top 67%
1.2%
20
Evolution Letters
71 papers in training set
Top 1%
0.9%
21
Peer Community Journal
254 papers in training set
Top 3%
0.8%
22
Journal of Theoretical Biology
144 papers in training set
Top 2%
0.7%
23
Ecology Letters
121 papers in training set
Top 1%
0.7%
24
Nature Human Behaviour
85 papers in training set
Top 5%
0.6%
25
Molecular Biology and Evolution
488 papers in training set
Top 5%
0.6%
26
Genetics
225 papers in training set
Top 5%
0.6%
27
BMC Biology
248 papers in training set
Top 6%
0.6%