{"type":"ScholarlyArticle","title":"Fostering Collective Action in Complex Societies Using Community-Based Agents","authors":[{"name":"Jonathan Skaggs","slug":"jonathan-skaggs","given":"Jonathan","family":"Skaggs","affiliation":"Computer Science Department , Brigham Young University , Provo , UT , USA"},{"name":"Michael Richards","slug":"michael-richards","given":"Michael","family":"Richards","affiliation":"Computer Science Department , Brigham Young University , Provo , UT , USA"},{"name":"Melissa Morris","slug":"melissa-morris","given":"Melissa","family":"Morris","affiliation":"Computer Science Department , Brigham Young University , Provo , UT , USA"},{"name":"Michael A. Goodrich","slug":"michael-a-goodrich","given":"Michael A.","family":"Goodrich","affiliation":"Computer Science Department , Brigham Young University , Provo , UT , USA"},{"name":"Jacob W. Crandall","slug":"jacob-w-crandall","given":"Jacob W.","family":"Crandall","affiliation":"Computer Science Department , Brigham Young University , Provo , UT , USA"}],"abstract":"As AI integrates into human societies, its ability to engage in collective action is increasingly important.  Human social systems have large and flexible strategy spaces, conflicting interests, power asymmetry, and interdependence among members, which together make it challenging for agents to learn collective action.  In this paper, we explore the ability of community-based agents to learn collective action within a novel model of complex social systems.  We first present this social model, called the Junior High Game (JHG).  The JHG embodies key elements of human social systems that require players to act collectively.  We then describe an agent, called CAB, which is based on community detection and formation algorithms.  Via simulations and user studies, we evaluate the ability of CAB agents to interact in JHG societies consisting of humans and AI agents.  These evaluations both identify requirements for successful collective behaviors in the JHG and identify important unsolved problems for developing AI agents capable of collective action in complex social systems.","doi":"10.24963/ijcai.2024/24","url":"https://ijcai.org/proceedings/2024/24","pdf":"https://ijcai.org/proceedings/2024/0024.pdf","markdown":"https://ijcai.org/proceedings/2024/24.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/24","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":24,"pages":{"first":211,"last":219},"keywords":["Agent-based and Multi-agent Systems: MAS: Human-agent interaction","Agent-based and Multi-agent Systems: MAS: Coordination and cooperation","Agent-based and Multi-agent Systems: MAS: Trust and reputation","Agent-based and Multi-agent Systems: MAS: Agent societies"],"videos":[],"references":[{"position":1,"raw":"[Acemoglu and Robinson, 2013] Daron Acemoglu and James A. 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