{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/modeling-human-ad-hoc-coordination","title":"Modeling Human Ad Hoc Coordination","arxiv_id":"1602.03924","date":"2016-02-11","proceeding":null,"authors":["Peter M. Krafft","Chris L. Baker","Alex Pentland","Joshua B. Tenenbaum"],"abstract":"Whether in groups of humans or groups of computer agents, collaboration is\nmost effective between individuals who have the ability to coordinate on a\njoint strategy for collective action. However, in general a rational actor will\nonly intend to coordinate if that actor believes the other group members have\nthe same intention. This circular dependence makes rational coordination\ndifficult in uncertain environments if communication between actors is\nunreliable and no prior agreements have been made. An important normative\nquestion with regard to coordination in these ad hoc settings is therefore how\none can come to believe that other actors will coordinate, and with regard to\nsystems involving humans, an important empirical question is how humans arrive\nat these expectations. We introduce an exact algorithm for computing the\ninfinitely recursive hierarchy of graded beliefs required for rational\ncoordination in uncertain environments, and we introduce a novel mechanism for\nmultiagent coordination that uses it. Our algorithm is valid in any environment\nwith a finite state space, and extensions to certain countably infinite state\nspaces are likely possible. We test our mechanism for multiagent coordination\nas a model for human decisions in a simple coordination game using existing\nexperimental data. We then explore via simulations whether modeling humans in\nthis way may improve human-agent collaboration.","url_abs":"http://arxiv.org/abs/1602.03924v1","url_pdf":"http://arxiv.org/pdf/1602.03924v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"modeling-human-ad-hoc-coordination","repo_url":"https://github.com/pkrafft/modeling-human-ad-hoc-coordination","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"human-agent-collaboration","task_name":"Human Agent Collaboration"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}