[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82968-en":3,"doc-seo-82968-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82968,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Collective Cognition in Hybrid Groups: A Network Science Synthesis","The growing integration of AI agents into human teams demands a principled explanation of how collective intelligence emerges in hybrid systems. While prior work clarifies roles of attention, memory, and reasoning at individual and dyadic levels, a scalable account of how cognitive differences shape group dynamics remains missing. This chapter synthesizes network science, collective cognition, and multi-agent research to analyze how attention, memory, reasoning, task environments, topologies, and incentives drive collective outcomes.","arXiv :2607 .05593v 1 [ cs .HC] 6 Jul 2026  \nCollective Cognition in Hybrid Groups: A Network Science Synthesis  \nBabak Hemmatian1 , Razan Baltaji2 , Lav R. Varshney1  \n1 AI Innovation Institute, Stony Brook University, New York, United States  \n2 Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Illinois, United States  \nAbstract  \nThe growing integration of AI agents into human teams calls for a principled understanding of how collective intelligence emerges in hybrid systems. Recent frameworks have clarified how attention, memory, and reasoning differences shape human–AI interaction at individual and dyadic levels. Yet a formal account of how these cognitive differences scale to group-level dynamics is lacking. Most network science research has examined either human-only networks or multi-agent AI-only systems, leaving open how insights translate to hybrid groups. Understanding how findings and parametrizations from non-hybrid networks extend to hybrid settings situates hybrid intelligence research within established traditions of collective cognition and collaboration.  \nThis chapter synthesizes research in network science, collective cognition, and multi-agent systems, alongside emerging work on hybrid systems, through the lens of attention, memory, and reasoning capacities. We review findings from cognitive modeling of human-only and AI-only networks, showing how task environments, group topologies, agent-level processes, and incentive structures shape collective outcomes.  \nWe then examine how these results extend to hybrid settings, conceptualizing hybrid networks as composed of heterogeneous human-AI nodes and links, each with distinct individual and transactive cognitive constraints. Our comparative analysis identifies which network effects appear robust across agent types, and which require theoretical revision in hybrid contexts. It also highlights configurations that were peripheral in human-only or AI-only research traditions, such as human gatekeepers of AI sub-networks, but become structurally central in hybrid teams, clarifying where established predictions may diverge from hybrid results.  \nIntegrating a cognitive systems perspective with network science, the chapter reconciles largely separate literatures and clarifies how established models of exploration–exploitation and efficiency–redundancy trade-offs may operate differently in hybrid teams. We conclude with implications for organizational design, governance, and the responsible development of hybrid intelligence systems.  \nKeywords: hybrid intelligence; collective intelligence; network science; human–AI teaming; multi-agent systems; collective cognition; transactive systems; exploration–exploitation; efficiency– redundancy  \n1 Introduction and Relevance  \nArtificial intelligence (AI) agents are no longer only tools that individuals use in isolation but are increasingly woven into everyday groups and teams. “Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behavior alone”(Tsvetkova et al., 2024, p. 1864) . The collective cognition of such a network (its capacity to pursue, as a unit, the goal for which it was assembled) is an emergent property of how human and machine agents are wired together, what each contributes, and how information moves among them. This chapter asks  \nhow the tools of network science, combined with a cognitive account of the agents, can describe, explain, and ultimately predict that emergent behavior.  \nThe settings humans and machines engage together are strikingly varied, and the differences are consequential. They range from competition, as in multiplayer games that pit human players against cheating bots; to coordination, as when managerial work is routed through agentic AI workflows; to collective decision-making, as in multi-agent clinic","cbCaiceXLm3rmLmK","https://ap.wps.com/l/cbCaiceXLm3rmLmK","pdf",495771,2,1,27,"English","en",105,"# Abstract\n# Introduction and Relevance\n## Hybrid networks and collective cognition\n## Neighboring disciplines and prior research","[{\"question\":\"Why is collective cognition in hybrid human–AI groups difficult to characterize?\",\"answer\":\"Emergent outcomes depend on how human and machine agents are wired together and how information moves among them. Neither behavior of humans nor machines alone suffices to deduce the collective result.\"},{\"question\":\"What gap does the chapter target?\",\"answer\":\"A formal, group-level account is lacking for how cognitive differences (attention, memory, reasoning) scale to group dynamics. Existing network science often focuses on human-only or AI-only settings.\"},{\"question\":\"How does the chapter connect network science to cognitive modeling?\",\"answer\":\"It reviews findings from cognitive modeling of human-only and AI-only networks, then examines how these translate to hybrid contexts using heterogeneous human–AI nodes and links with distinct cognitive constraints.\"}]",1784184377,68,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"collective-cognition-in-hybrid-groups-a-network-science-synthesis","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/collective-cognition-in-hybrid-groups-a-network-science-synthesis/82968/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-21","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is collective cognition in hybrid human–AI groups difficult to characterize?","Question",{"text":75,"@type":76},"Emergent outcomes depend on how human and machine agents are wired together and how information moves among them. Neither behavior of humans nor machines alone suffices to deduce the collective result.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What gap does the chapter target?",{"text":80,"@type":76},"A formal, group-level account is lacking for how cognitive differences (attention, memory, reasoning) scale to group dynamics. Existing network science often focuses on human-only or AI-only settings.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the chapter connect network science to cognitive modeling?",{"text":84,"@type":76},"It reviews findings from cognitive modeling of human-only and AI-only networks, then examines how these translate to hybrid contexts using heterogeneous human–AI nodes and links with distinct cognitive constraints.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]