The Ultimatum Game in Complex Networks

Abstract

We address the problem of how cooperative (altruistic-like) behavior arises in natural and social systems by analyzing an ultimatum game in complex networks. Specifically, three types of players are considered: (a) empathetic, whose aspiration level and offer are equal, (b) pragmatic, who do not distinguish between the different roles and aim to obtain the same benefit, and (c) agents whose aspiration level and offer are independent. We analyze the asymptotic behavior of pure populations on different topologies using two kinds of strategic update rules. Natural selection, which relies on replicator dynamics, and Social Penalty, inspired in the Bak-Sneppen dynamics, in which players are subjected to a social selection rule penalizing not only the less fitted individuals, but also their first neighbors. We discuss the emergence of fairness in the different settings and network topologies.

version published

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Cited by in corpus (18)

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• Dynamical organization towards consensus in the Axelrod model on complex networks
• Social Diversity Reduces the Complexity and Cost of Fostering Fairness
• Evolution of favoritism and group fairness in a co-evolving three-person ultimatum game
• Evolution of Fairness in the Not Quite Ultimatum Game
• Roles of mutation rate and co-existence of multiple strategy updating rules in evolutionary prisoner's dilemma games
• A reversed form of public goods game: equivalence and difference
• Pinning control of social fairness in the Ultimatum game
• Randomness and Arbitrary Coordination in the Reactive Ultimatum Game
• Limitation of network inhomogeneity in improving cooperation in coevolutionary dynamics
• Probabilistic fair behaviors spark its boost in the Ultimatum Game: the strength of good Samaritans
• Decoding fairness: a reinforcement learning perspective
• Imitation of Success Leads to Cost of Living Mediated Fairness in the Ultimatum Game
• A belief-based evolutionarily stable strategy