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Are those who help the bad good or bad? The answer depends on adaptive architectures.

Date:
March 9, 2018
Source:
Rissho University
Summary:
Are those who help the bad good or bad? Game theoreticians reveal that the answer depends on whether the society adopts 'individualism' or 'dividualism'.
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A research team ECOSOS (Evolving Cooperation and Social Simulation) led by Hitoshi Yamamoto from Rissho University develops a game theoretical method to analyze what role the diversity of social norms plays in the process of evolving cooperation. The team revealed that a social norm that regards those who help the bad as bad becomes extinct if the members of the society learn their norms based on "dividualism." The study was carried out by collaborating with colleagues Satoshi Uchida (RINRI Institute), Isamu Okada (Soka University), and Tatsuya Sasaki (University of Vienna) and is published in Frontiers in Physics.

People often help others at their own expense without expecting any direct return from the beneficiaries. In order for stable cooperation among people to evolve, the help must be channeled away from exploiters such as defectors who never help others, and directed preferentially towards helpers. This is made possible by a mechanism by which a positive evaluation of the helpful action is shared with others, and the helping person receives a help from a third party at some future time. This mechanism is generally referred to as indirect reciprocity.

Indirect reciprocity relies on social norms that distinguish the good from the bad. Many game theoretical researches have searched for such norms that cooperation is not directed toward the bad, under the assumption that at most a few norms are shared among people. One conclusion of those researches is that assessing those who help the bad as bad stabilizes cooperation. In reality, however, different people often follow different norms, which lead to different opinions of the same person. Little is theoretically known about the evolution of cooperation in "norm ecosystems" in which individuals with different norms interact through helping games.

To address this issue, the team developed an analytical tool which can deal with norm ecosystems. The norm ecosystem studied in the research is so complex that one needs to solve a system of more than sixty thousands equations. But the team at the same time established a method that reduces the system to 512 equations, which can computationally be analyzed. The analysis tells that the norm that assess those who help the bad as bad is stably sustained if people learn their norms based on "individualism" that gives a view that any person cannot be divided into parts and that a person must be treated as a whole. But the same norm becomes extinct once the society adopts "dividualism" that provides a view that a person is constructed from independent parts.

Yamamoto says, "This is a surprising finding since all the theoretical researches so far are based on individualism. Our approach gives a first opportunity to review that premise."


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Materials provided by Rissho University. Note: Content may be edited for style and length.


Journal Reference:

  1. Satoshi Uchida, Hitoshi Yamamoto, Isamu Okada, Tatsuya Sasaki. A Theoretical Approach to Norm Ecosystems: Two Adaptive Architectures of Indirect Reciprocity Show Different Paths to the Evolution of Cooperation. Frontiers in Physics, 2018; 6 DOI: 10.3389/fphy.2018.00014

Cite This Page:

Rissho University. "Are those who help the bad good or bad? The answer depends on adaptive architectures.." ScienceDaily. ScienceDaily, 9 March 2018. <www.sciencedaily.com/releases/2018/03/180309095523.htm>.
Rissho University. (2018, March 9). Are those who help the bad good or bad? The answer depends on adaptive architectures.. ScienceDaily. Retrieved November 9, 2024 from www.sciencedaily.com/releases/2018/03/180309095523.htm
Rissho University. "Are those who help the bad good or bad? The answer depends on adaptive architectures.." ScienceDaily. www.sciencedaily.com/releases/2018/03/180309095523.htm (accessed November 9, 2024).

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