Regular Article - Statistical and Nonlinear Physics
Evolutionary dynamics of trust in the N-player trust game with individual reward and punishment
School of Mathematical Sciences, University of Electronic Science and Technology of China, 611731, Chengdu, China
Accepted: 13 August 2021
Published online: 31 August 2021
Trust plays an important role in human society. However, how does trust evolve is a huge challenge. The trust game is a well-known paradigm to measure the evolution of trust in a population. Reward and punishment as the common types of incentives can be used to improve the trustworthiness. However, it remains unclear how reward and punishment actually influence the evolutionary dynamics of trust. Here, we introduce individual reward and punishment into the N-player trust game model in an infinite well-mixed population, where investors use a part of the returned fund to reward trustworthy trustees and meanwhile punish untrustworthy trustees. We then investigate the evolutionary dynamics of trust by means of replicator equations. We show that the introduction of reward and punishment can lead to the stable coexistence state of investors and trustworthy trustees, which indicates that the evolution of trust can be greatly promoted. We reveal that the attraction domain of the coexistence state becomes larger as investors increase the incentive strength from the returned fund for reward and punishment. In addition, we find that the increase of the reward coefficient can enlarge the attraction domain of the coexistence state, which implies that reward can better promote the evolution of trust than punishment.
© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2021