Abstract
Real-world agents, humans as well as animals, observe each other during interactions and choose their own actions taking the partners’ ongoing behaviour into account. Yet, classical game theory assumes that players act either strictly sequentially or strictly simultaneously without knowing each other’s current choices. To account for action visibility and provide a more realistic model of interactions under time constraints, we introduce a new game-theoretic setting called transparent games, where each player has a certain probability of observing the partner’s choice before deciding on its own action. By means of evolutionary simulations, we demonstrate that even a small probability of seeing the partner’s choice before one’s own decision substantially changes the evolutionary successful strategies. Action visibility enhances cooperation in an iterated coordination game, but reduces cooperation in a more competitive iterated Prisoner’s Dilemma. In both games, “Win–stay, lose–shift” and “Tit-for-tat” strategies are predominant for moderate transparency, while a “Leader-Follower” strategy emerges for high transparency. Our results have implications for studies of human and animal social behaviour, especially for the analysis of dyadic and group interactions.
Original language | English |
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Article number | e1007588 |
Number of pages | 32 |
Journal | PLoS Computational Biology |
Volume | 16 |
Issue number | 1 |
DOIs | |
Publication status | Published - 09 Jan 2020 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2020 Unakafov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords
- Animals
- Behavior, Animal
- Computational Biology
- Cooperative Behavior
- Decision Making/physiology
- Game Theory
- Humans
- Interpersonal Relations
- Models, Biological
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Modelling and Simulation
- Ecology
- Molecular Biology
- Genetics
- Cellular and Molecular Neuroscience
- Computational Theory and Mathematics