Affiliation:
1. School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
2. Experimental Center of Data Science and Intelligent Decision-Making, Hangzhou Dianzi University, Hangzhou 310018, China
3. Research Center for Big Data Intelligence, Zhejiang Lab, Hangzhou 310000, China
Abstract
In public goods games, it is common for agents to learn strategies from those who possess the highest utility. However, in reality, because of the lack of information, strategies and utilities from others cannot be obtained or predicted during learning and updating. To address this issue, we introduce a learning update mechanism based on aspirations. To make this model more universal, we study goods that can be shared with k-hop neighbors. Additionally, when a free rider accesses an investor, it is required to pay an access cost to him. We investigate the influence of aspiration, shared scope k, and access cost on the social invest level and utility. It is shown that large shared scope k, moderate aspiration, and moderate access cost are conducive to the maximum utilization of social benefits. However, with low aspiration, the utilities of investors are very close and limited, while both the high aspiration and high access cost could disrupt the social stability.
Funder
National Natural Science Foundation of China
Scientific Research Fund of Zhejiang Provincial Education Department
Zhejiang Lab
Zhejiang Provincial Natural Science Foundation of China
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Cited by
1 articles.
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