Social Learning Dynamics in Multi-Agent Systems: A Framework for Collective Knowledge Building

Social Learning Dynamics in Multi-Agent Systems: A Framework for Collective Knowledge Building | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Social Learning Dynamics in Multi-Agent Systems: A Framework for Collective Knowledge Building Safiye Turgay, Sena Nur Adıyaman, Ayşe Ünlü, Pankaj Bhambri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8129755/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Complex and dynamic environments often require collective intelligence where many autonomous agents cooperate to find solutions and maximize group utility. One of the critical challenges of MAS is how to achieve emergent cooperation with efficient knowledge diffusion in situations where agents have only limited local information or when inherent social dilemmas exist. This paper presents a novel Social Learning Framework that enables Collective Knowledge Building in decentralized multi-agent systems, thereby addressing the limitations of purely self-interested reinforcement learning methods. While autonomous, agents in this framework make use of social information to enhance decision-making and hasten the learning process. Each agent observes the strategies and the performance outcomes of its peers rather than relying solely on independent trial-and-error learning and builds internal models of rewarding behaviors present within the environment. Further, agents selectively adopt high-performing policies demonstrated by others through a selective imitation mechanism that enables them to adapt and improve their capabilities at a faster pace while avoiding inefficient learning trajectories. Additionally, a shared knowledge aggregation process is established within the framework, wherein the validated and effective local experiences are aggregated into a collective knowledge base or common policy. This shared repository continuously evolves during the progress of the system and allows agents to adapt based not only on their direct interactions with the environment but also on the emerging collective intelligence within the group. By promoting cooperation and strategic imitation, the proposed approach allows for an enhanced integration of social learning principles into decentralized MAS and gives rise to robust, scalable, and adaptive collective intelligence. We show through empirical evaluation across a range of cooperative and sequential social dilemma environments that the proposed framework significantly improves convergence towards the optimal collective performance and yields higher long-term stability than purely independent or centralized learning. The present work provides a robust, scalable basis for the engineering of AI societies that can effectively construct, maintain, and leverage collective knowledge in order to solve complex real-world problems. Multi-Agent Systems (MAS) Social Learning Collective Knowledge Building Learning Dynamics Multi-Agent Reinforcement Learning (MARL) Collective Intelligence Cooperation and Coordination Emergent Behavior Knowledge Transfer Decentralized Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8129755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579854057,"identity":"e7a240e8-9f2e-48a0-b40c-72b29c3a64e9","order_by":0,"name":"Safiye Turgay","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Safiye","middleName":"","lastName":"Turgay","suffix":""},{"id":579854059,"identity":"10846a4f-04cf-41b8-a127-ae3167b748d7","order_by":1,"name":"Sena Nur Adıyaman","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Sena","middleName":"Nur","lastName":"Adıyaman","suffix":""},{"id":579854061,"identity":"92f2d338-76cb-441d-aa52-4887b5be011e","order_by":2,"name":"Ayşe Ünlü","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Ayşe","middleName":"","lastName":"Ünlü","suffix":""},{"id":579854063,"identity":"11a4116c-7918-487c-b110-fc5aab02a16f","order_by":3,"name":"Pankaj Bhambri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYFCCxAcg0oCB4fDBAwlAFh+Iy4NXS7IBVMuxBLAWNhK08BgcYCBGizl7MgNzQc0dY/7GMx8OPNxhJ8cm3cD44G0bQzTEBExg2fOYgXnGsWdmEgfObjiQeCbZmE3mALPh3DaG3A04tBjcyD/AzMN22IYBrKUNiCQS2KR58WoBOozn32Eb+QNnHsC0sP8mqIW37bCZwYEzDHBbmPFpAfnl8My+w8aGB44ZANUD/SKR2Cw555xE7kwcWoAhxvi44Nthw3k3Dj98+LPNTo5fIvnghzdlNrl9uBwGxIfBLAm4CsYGEJdBAY8WZjCLvwFNSh5dYBSMglEwCkYqAAA5aGcaUnCdjQAAAABJRU5ErkJggg==","orcid":"","institution":"Lincoln University College","correspondingAuthor":true,"prefix":"","firstName":"Pankaj","middleName":"","lastName":"Bhambri","suffix":""}],"badges":[],"createdAt":"2025-11-16 23:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8129755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8129755/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101828876,"identity":"bea6c099-d733-4b91-a889-06c2bb0c2c44","added_by":"auto","created_at":"2026-02-04 05:55:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4672715,"visible":true,"origin":"","legend":"","description":"","filename":"MAKALE17HAFTA5SAFIYETURGAYSENANURADIYAMANSocialLEarningDynamics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8129755/v1_covered_1f9d2a79-26df-435d-90b2-1385dae2aa96.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Learning Dynamics in Multi-Agent Systems: A Framework for Collective Knowledge Building","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multi-Agent Systems (MAS), Social Learning, Collective Knowledge Building, Learning Dynamics, Multi-Agent Reinforcement Learning (MARL), Collective Intelligence, Cooperation and Coordination, Emergent Behavior, Knowledge Transfer, Decentralized Learning","lastPublishedDoi":"10.21203/rs.3.rs-8129755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8129755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComplex and dynamic environments often require collective intelligence where many autonomous agents cooperate to find solutions and maximize group utility. One of the critical challenges of MAS is how to achieve emergent cooperation with efficient knowledge diffusion in situations where agents have only limited local information or when inherent social dilemmas exist.\u003c/p\u003e \u003cp\u003eThis paper presents a novel Social Learning Framework that enables Collective Knowledge Building in decentralized multi-agent systems, thereby addressing the limitations of purely self-interested reinforcement learning methods. While autonomous, agents in this framework make use of social information to enhance decision-making and hasten the learning process. Each agent observes the strategies and the performance outcomes of its peers rather than relying solely on independent trial-and-error learning and builds internal models of rewarding behaviors present within the environment. Further, agents selectively adopt high-performing policies demonstrated by others through a selective imitation mechanism that enables them to adapt and improve their capabilities at a faster pace while avoiding inefficient learning trajectories. Additionally, a shared knowledge aggregation process is established within the framework, wherein the validated and effective local experiences are aggregated into a collective knowledge base or common policy. This shared repository continuously evolves during the progress of the system and allows agents to adapt based not only on their direct interactions with the environment but also on the emerging collective intelligence within the group. By promoting cooperation and strategic imitation, the proposed approach allows for an enhanced integration of social learning principles into decentralized MAS and gives rise to robust, scalable, and adaptive collective intelligence.\u003c/p\u003e \u003cp\u003eWe show through empirical evaluation across a range of cooperative and sequential social dilemma environments that the proposed framework significantly improves convergence towards the optimal collective performance and yields higher long-term stability than purely independent or centralized learning. The present work provides a robust, scalable basis for the engineering of AI societies that can effectively construct, maintain, and leverage collective knowledge in order to solve complex real-world problems.\u003c/p\u003e","manuscriptTitle":"Social Learning Dynamics in Multi-Agent Systems: A Framework for Collective Knowledge Building","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 09:55:49","doi":"10.21203/rs.3.rs-8129755/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a42d4ab-423d-4e3b-8bf8-39766274cfe6","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T05:55:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 09:55:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8129755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8129755","identity":"rs-8129755","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}