[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83171-en":3,"doc-seo-83171-105":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},83171,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Modeling Misinformation as a Commons Problem","Misinformation harms society by eroding the shared trust people use to judge credibility, not merely by promoting individual false beliefs. This paper presents an agent-based simulation treating trust as a collective resource and attention as a scarce private budget: when aggregate attention shifts toward low-credibility content, the trust environment degrades, making credible information harder to process and correct. Four recurring regimes emerge—credible stability, misinformation dominance, polarization, and a mixed baseline—with measurable trust and network signatures. The model separates trust repair from harm and tests how homophily and rewiring shape cross-cutting exposure. It provides a transparent testbed for comparing interventions that must jointly restore trust and maintain structural conditions for corrective reach.","MODELING MISINFORMATION AS A COMMONS PROBLEM  \nVrinda Malhotra  \nGeorge Mason University  \narXiv :2607 .06984v 1 [ cs .CY] 8 Jul 2026  \nABSTRACT  \nMisinformation often harms society not just by spreading a single false belief, but by breaking down the shared trust people rely on to evaluate what is true. This paper presents an agent-based simulation that frames trust as a collective resource and attention as a scarce private budget: when aggregate attention shifts toward low-credibility content, the trust environment degrades, making credible information harder to process and correct. Across experiments, the model produces four recurring modes: credible stability, misinformation dominance, polarization, and a mixed baseline, with distinct signatures in trust trajectories and network structure. The results separate two control problems that matter for simulation-based policy exploration: the balance of trust repair versus harm largely determines whether the system recovers or collapses, while homophily and rewiring determine whether disagreement remains integrated or separates into persistent clusters. This foundation provides a transparent testbed for comparative experiments on interventions that must address both trust restoration and structural conditions for cross-cutting exposure.  \nKeywords: agent-based simulation; attention economy; epistemic commons; adaptive networks; polarization.  \n1 INTRODUCTION  \nDigital information environments make attention scarce and highly leveraged: what users attend to influences what they learn, share, and reinforce, while platform-mediated networks can amplify and concentrate exposure. In such settings, misinformation not only misleads; it can degrade the epistemic environment by undermining trust, creating conditions where low-credibility narratives become easier to adopt and harder to correct [1, 2] .  \nA large literature models belief change through interpersonal influence and bounded updating. Boundedconfidence models capture the empirically plausible idea that influence is limited when beliefs are too dissimilar, yielding consensus, fragmentation, or polarization depending on parameters and network structure [3, 4] . Work on bounded rationality emphasizes that attention is limited and processing is costly, shaping selective exposure and heuristic reliance [5, 6, 7] . Adaptive-network research shows that when ties co-evolve with agent states, the network can sort into segregated, metastable polarized communities [8, 9] .  \nSeveral agent-based models directly address misinformation and polarization. Del Vicario et al. [10] document empirically that misinformation and credible content spread through largely separate echo chambers on social media, with homophily driving segregation. Tambuscio et al. [11] model fact-checking as a competing contagion, showing that network topology and corrective density jointly determine whether false narratives are suppressed. Sasahara et al. [12] show that selective unfollowing accelerates echo chamber formation even among initially heterogeneous agents. These models capture important aspects of information spread and network sorting, but they treat the epistemic environment, that is, the shared pool of trusted sources and  \nProc. of the 2026 Annual Modeling and Simulation Conference (ANNSIM’26) May 4-7, 2026, University of Central Florida, Orlando, Florida, USA  \nG. Rabadi, V. Prabhu, R. Cárdenas, A. Bany Abdelnabi, J. Jabbour, and M. Germanos, eds.  \n©2026 Society for Modeling & Simulation International (SCS)  \nVrinda Malhotra  \ncredible signals that a population draws on, as a fixed backdrop rather than a depletable resource shaped by collective behavior.  \nThe knowledge commons literature suggests a different framing. Hess and Ostrom [13] identify shared information environments as exhibiting commons-like properties: they are collectively produced, difficult to exclude individuals from, and vulnerable to degradation when overused or neglected. Frischm","cbCaiiX3cOqqfOoI","https://ap.wps.com/l/cbCaiiX3cOqqfOoI","pdf",2616351,2,1,24,"English","en",105,"# Introduction\n## Trust, attention scarcity, and epistemic degradation\n## Modeling approaches to belief change and polarization\n## Related misinformation and polarization models\n## Commons-based reframing and paper contributions","[{\"question\":\"What key mechanism does the model use to connect misinformation to societal harm?\",\"answer\":\"The model treats trust as a depletable collective resource and attention as a scarce private budget. As aggregate attention moves toward low-credibility content, the trust environment degrades, reducing the ability to process and correct credible information.\"},{\"question\":\"What recurring behavioral regimes does the simulation produce?\",\"answer\":\"Across experiments, the model generates four recurring modes: credible stability, misinformation dominance, polarization, and a mixed baseline. Each regime shows distinct signatures in trust trajectories and network structure.\"},{\"question\":\"Which two control problems are identified as most important for recovery versus collapse and for how disagreement clusters form?\",\"answer\":\"Whether trust repair versus harm dominates determines if the system recovers or collapses. Separately, homophily and rewiring determine whether disagreement stays integrated or separates into persistent clusters.\"}]",1784185731,60,{"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},"modeling-misinformation-as-a-commons-problem","",{"@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/modeling-misinformation-as-a-commons-problem/83171/",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","2026-07-24","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},"What key mechanism does the model use to connect misinformation to societal harm?","Question",{"text":75,"@type":76},"The model treats trust as a depletable collective resource and attention as a scarce private budget. As aggregate attention moves toward low-credibility content, the trust environment degrades, reducing the ability to process and correct credible information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What recurring behavioral regimes does the simulation produce?",{"text":80,"@type":76},"Across experiments, the model generates four recurring modes: credible stability, misinformation dominance, polarization, and a mixed baseline. Each regime shows distinct signatures in trust trajectories and network structure.",{"name":82,"@type":73,"acceptedAnswer":83},"Which two control problems are identified as most important for recovery versus collapse and for how disagreement clusters form?",{"text":84,"@type":76},"Whether trust repair versus harm dominates determines if the system recovers or collapses. Separately, homophily and rewiring determine whether disagreement stays integrated or separates into persistent clusters.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]