The DOI matches its Crossref record; 436 of the 436 references it cites were found in a registry or an index.
A mutualistic approach to morality: The evolution of fairness by partner choice
Open accessAbstract
What makes humans moral beings? This question can be understood either as a proximate “how” question or as an ultimate “why” question. The “how” question is about the mental and social mechanisms that produce moral judgments and interactions, and has been investigated by psychologists and social scientists. The “why” question is about the fitness consequences that explain why humans have morality, and has been discussed by evolutionary biologists in the context of the evolution of cooperation. Our goal here is to contribute to a fruitful articulation of such proximate and ultimate explanations of human morality. We develop an approach to morality as an adaptation to an environment in which individuals were in competition to be chosen and recruited in mutually advantageous cooperative interactions. In this environment, the best strategy is to treat others with impartiality and to share the costs and benefits of cooperation equally. Those who offer less than others will be left out of cooperation; conversely, those who offer more will be exploited by their partners. In line with this mutualistic approach, the study of a range of economic games involving property rights, collective actions, mutual help and punishment shows that participants' distributions aim at sharing the costs and benefits of interactions in an impartial way. In particular, the distribution of resources is influenced by effort and talent, and the perception of each participant's rights on the resources to be distributed.
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Inference of trustworthiness from intuitive moral judgments.
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Baumard, N., & Boyer, P. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.04.003 · Cited by 230
Self-citationThe logic of universalization guides moral judgment
Levine, S., Kleiman-Weiner, M., Schulz, L., Tenenbaum, J., & Cushman, F. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2014505117 · Cited by 88
Metadata
- Authors
- Baumard, N., André, J.-B., & Sperber, D.
- Venue
- Behavioral and Brain Sciences
- Publisher
- Cambridge University Press (CUP)
- Published
- 2013-02
- Volume
- 36
- Issue
- 1
- Pages
- 59-78
- ISSN
- 0140-525X (ISSN-L)
- 1469-1825
- OpenAlex
- W2027076429
- Cited by
- 668
- References
- 436
- FWCI
- 144.25
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.