The evolution of social behaviors and risk preferences in settings with uncertainty.


Journal

Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876

Informations de publication

Date de publication:
23 Jul 2024
Historique:
medline: 17 7 2024
pubmed: 17 7 2024
entrez: 17 7 2024
Statut: ppublish

Résumé

Humans update their social behavior in response to past experiences and changing environments. Behavioral decisions are further complicated by uncertainty in the outcome of social interactions. Faced with uncertainty, some individuals exhibit risk aversion while others seek risk. Attitudes toward risk may depend on socioeconomic status; and individuals may update their risk preferences over time, which will feedback on their social behavior. Here, we study how uncertainty and risk preferences shape the evolution of social behaviors. We extend the game-theoretic framework for behavioral evolution to incorporate uncertainty about payoffs and variation in how individuals respond to this uncertainty. We find that different attitudes toward risk can substantially alter behavior and long-term outcomes, as individuals seek to optimize their rewards from social interactions. In a standard setting without risk, for example, defection always overtakes a well-mixed population engaged in the classic Prisoner's Dilemma, whereas risk aversion can reverse the direction of evolution, promoting cooperation over defection. When individuals update their risk preferences along with their strategic behaviors, a population can oscillate between periods dominated by risk-averse cooperators and periods of risk-seeking defectors. Our analysis provides a systematic account of how risk preferences modulate, and even coevolve with, behavior in an uncertain social world.

Identifiants

pubmed: 39018189
doi: 10.1073/pnas.2406993121
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2406993121

Subventions

Organisme : China Scholarship Council (CSC)
ID : 202306010132
Organisme : Shanghai Pujiang Program
ID : 23PJ1405500
Organisme : MOST | National Natural Science Foundation of China (NSFC)
ID : 62036002
Organisme : John Templeton Foundation (JTF)
ID : 62281

Déclaration de conflit d'intérêts

Competing interests statement:The authors declare no competing interest.

Auteurs

Guocheng Wang (G)

Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, China.
Department of Biology, University of Pennsylvania, Philadelphia, PA 19104.

Qi Su (Q)

Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China.
Ministry of Education of China, Key Laboratory of System Control and Information Processing, Shanghai 200240, China.
Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai 200240, China.

Long Wang (L)

Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, China.
Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing 100871, China.

Joshua B Plotkin (JB)

Department of Biology, University of Pennsylvania, Philadelphia, PA 19104.
Center for Mathematical Biology, University of Pennsylvania, Philadelphia, PA 19014.

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Classifications MeSH