By regulating agent interactions, norms facilitate coordination in multiagent systems. We investigate challenges and opportunities in the emergence of norms of prosociality, such as vaccination and mask wearing. Little research on norm emergence has incorporated social preferences, which determines how agents behave when others are involved.
We evaluate the influence of preference distributions in a society on the emergence of prosocial norms. We adopt the Social Value Orientation (SVO) framework, which places value preferences along the dimensions of self and other. SVO brings forth the aspects of values most relevant to prosociality. Therefore, it provides an effective basis to structure our evaluation.
We find that including SVO in agents enables (1) better social experience; and (2) robust norm emergence.
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Prosocial Norm Emergence in Multi-agent Systems
Multi-agent systems provide a basis for developing systems of autonomous entities and thus find application in a variety of domains. We consider a setting where not only the member agents are adaptive but also the multi-agent system viewed as an entity in its own right is adaptive. Specifically, the social structure of a multi-agent system can be reflected in the social norms among its members. It is well recognized that the norms that arise in society are not always beneficial to its members. We focus on prosocial norms, which help achieve positive outcomes for society and often provide guidance to agents to act in a manner that takes into account the welfare of others. Specifically, we propose Cha, a framework for the emergence of prosocial norms. Unlike previous norm emergence approaches, Cha supports continual change to a system (agents may enter and leave) and dynamism (norms may change when the environment changes). Importantly, Cha agents incorporate prosocial decision-making based on inequity aversion theory, reflecting an intuition of guilt arising from being antisocial. In this manner, Cha brings together two important themes in prosociality: decision-making by individuals and fairness of system-level outcomes. We demonstrate via simulation that Cha can improve aggregate societal gains and fairness of outcomes.
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- Award ID(s):
- 2116751
- NSF-PAR ID:
- 10357055
- Date Published:
- Journal Name:
- ACM Transactions on Autonomous and Adaptive Systems
- Volume:
- 17
- Issue:
- 1-2
- ISSN:
- 1556-4665
- Page Range / eLocation ID:
- 1 to 24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3540202
