Yuyuan Liu, Lichen Wang, Ruqiang Guo, Shijia Hua, Linjie Liu, Liang Zhang, The Anh Han
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引用次数: 0
Abstract
Trust game is commonly used to study the evolution of trust among unrelated individuals. It offers valuable insights into human interactions in a range of disciplines, including economics, sociology and psychology. Previous research has revealed that reward and punishment systems can effectively promote the evolution of trust. However, these investigations overlook the gaming environment, leaving unresolved the optimal conditions for employing distinct incentives to effectively facilitate trust level. To bridge this gap, we introduce a transformation incentive mechanism in an N-player trust game, where trustees are given different forms of incentives depending on the number of trustees in the group. Using the Markov decision process approach, our research shows that as incentives increase, the level of trust rises continuously, eventually reaching a high level of coexistence between investors and trustworthy trustees. Specifically, in the case of smaller incentives, rewarding trustworthy trustees is more effective. Conversely, in the case of larger incentives, punishing untrustworthy trustees is more effective. Additionally, we find that moderate incentives have a positive impact on increasing the average payoff within the group.
期刊介绍:
J. R. Soc. Interface welcomes articles of high quality research at the interface of the physical and life sciences. It provides a high-quality forum to publish rapidly and interact across this boundary in two main ways: J. R. Soc. Interface publishes research applying chemistry, engineering, materials science, mathematics and physics to the biological and medical sciences; it also highlights discoveries in the life sciences of relevance to the physical sciences. Both sides of the interface are considered equally and it is one of the only journals to cover this exciting new territory. J. R. Soc. Interface welcomes contributions on a diverse range of topics, including but not limited to; biocomplexity, bioengineering, bioinformatics, biomaterials, biomechanics, bionanoscience, biophysics, chemical biology, computer science (as applied to the life sciences), medical physics, synthetic biology, systems biology, theoretical biology and tissue engineering.