Paper Info
Title
Learning dynamics explains human behaviour in prisoner's dilemma on networks.
Abstract
Cooperative behaviour lies at the very basis of human societies, yet its evolutionary origin remains a key unsolved puzzle. Whereas reciprocity or conditional cooperation is one of the most prominent mechanisms proposed to explain the emergence of cooperation in social dilemmas, recent experimental findings on networked Prisoner's Dilemma games suggest that conditional cooperation also depends on the previous action of the player-namely on the 'mood' in which the player is currently in. Roughly, a majority of people behave as conditional cooperators if they cooperated in the past, whereas they ignore the context and free ride with high probability if they did not. However, the ultimate origin of this behaviour represents a conundrum itself. Here, we aim specifically to provide an evolutionary explanation of moody conditional cooperation (MCC). To this end, we perform an extensive analysis of different evolutionary dynamics for players' behavioural traits-ranging from standard processes used in game theory based on pay-off comparison to others that include non-economic or social factors. Our results show that only a dynamic built upon reinforcement learning is able to give rise to evolutionarily stable MCC, and at the end to reproduce the human behaviours observed in the experiments.
Year
DOI
Venue
2014
10.1098/rsif.2013.1186
JOURNAL OF THE ROYAL SOCIETY INTERFACE
Keywords
Field
DocType
evolutionary game theory,Prisoner's Dilemma,social networks,moody conditional cooperation,reinforcement learning
Ecology,Prisoner's dilemma,Cognitive psychology,Game theory,Artificial intelligence,Reciprocity (social psychology),Evolutionary dynamics,Dilemma,Evolutionary game theory,Social dilemma,Mathematics,Reinforcement learning
Journal
Volume
Issue
ISSN
11
94
1742-5689
Citations 
PageRank 
References 
2
0.40
2
Authors
2
Name
Order
Citations
PageRank
Giulio Cimini1535.58
Ángel Sánchez216124.61