[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84875-en":3,"doc-seo-84875-105":29,"detail-sidebar-cat-0-en-105":83},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84875,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","AgoraSim A Hybrid Agent-Based Modeling Framework","LLM-agent simulations make natural-language social scenarios easy to instantiate, yet outputs can be misread as predictions and are hard to compare with explicit social dynamics. AgoraSim introduces a hybrid agent-based modeling framework for scenario-oriented social reaction analysis. It converts textual or multimodal artifacts into editable ABM configurations, runs ratio-controlled mixed populations (LLM, vision-language, custom-endpoint, random, and classical agents), and compares each scenario to matched classical reference dynamics. Shared structured decision objects support common action spaces, interaction protocols, metrics, and audit records via UI, SDK/CLI, and REST API.","AgoraSim: A Hybrid Agent-Based Modeling Framework  \nChung-Chi Chen  \nHuman-Agent Ally Lab (HAA Lab) National Institute of Informatics, Japan [chen@nii.ac.jp](chen@nii.ac.jp)  \narXiv :2607 .05999v 1 [ cs .AI ] 7 Jul 2026  \nAbstract  \nLLM-agent simulations make natural-language social scenarios easy to instantiate, but their outputs can be overread as predictions and are often difficult to compare with explicit social dynamics. We present AgoraSim, a hybrid agent-based modeling framework for scenario-oriented social reaction analysis. AgoraSim resolves textual or multimodal artifacts  \ninto editable ABM configurations, runs ratiocontrolled populations that mix LLM, visionlanguage, custom-endpoint, random, and classical agents, and compares the same scenario against matched classical reference dynamics.  \nAll agents emit a shared structured decision object, enabling common action spaces, interaction protocols, metrics, and audit records. Exposed through a local UI, Python SDK/CLI, and REST API, AgoraSim helps users inspect scenario trajectories, compare modeling assumptions, and identify cases that warrant empirical validation.  \n1 Introduction  \nMany consequential decisions begin as naturallanguage or multimodal artifacts: a policy announcement, a product launch, a public apology, a campaign message, a social-media post, a video, or an advertisement. Before such an artifact is deployed, stakeholders often want to explore how different publics might react. The useful output is rarely a single sentiment label or a point forecast of public opinion. In early-stage decision making, users need to inspect scenario trajectories: which reactions are plausible, which social exposures might amplify or dampen them, which assumptions make the trajectory change, and where a pattern appears robust or fragile. This is a natural setting for an NLP demo because the input is language and media, the agents’ evidence and rationales are expressed in language, and the output must remain legible to users comparing alternatives.  \nWe frame this task as scenario-oriented social simulation. The goal is not to reproduce a real population in fine detail or to claim predictive authority over future behavior. Social simulation is most useful when it helps explain collective patterns, construct hypotheses, and make modeling assumptions explicit (Wu et al., 2026) . This distinction is especially important for LLM-based social simulation. Language-model agents can produce fluent and plausible reactions, but plausible text alone does not establish that a simulation captures behavioral variance, subgroup differences, tipping points, or path-dependent dynamics. A useful demo should therefore help users see how a scenario behaves under explicit assumptions, not invite them to treat synthetic reactions as public opinion.  \nAgent-based modeling (ABM) provides the classical modeling language for this objective. ABM represents social phenomena through local rules, heterogeneous agents, networks, feedback, and interaction over time. Foundational models show how simple micro-level assumptions can generate macro-level patterns such as segregation, cascades, adoption, consensus, contagion, and polarization (Schelling, 1971 ; Granovetter, 1978 ; Bass, 1969 ; DeGroot, 1974 ; Clifford and Sudbury, 1973 ; Axelrod, 1997 ; Epstein and Axtell, 1996 ; Bonabeau, 2002 ; Deffuant et al., 2000) . Its value is not that any single rule family is universally realistic, but that assumptions are explicit, parameterized, inspectable, and comparable. ABM gives natural-language social simulation a methodological scaffold: it turns a scenario into agents, states, actions, exposure rules, heterogeneity assumptions, and collective metrics.  \nTraditional ABM, however, places a heavy modeling burden on the user. To ask how a community might react to a particular apology, policy brief, advertisement, image, or product announcement, the analyst must first translate the artifact into states, threshold","cbCaii8mBTUhoxgF","https://ap.wps.com/l/cbCaii8mBTUhoxgF","pdf",2736241,1,10,"English","en",105,"# Introduction\n## Scenario-oriented social simulation\n## Agent-based modeling as a scaffold\n## Limits of traditional ABM and the role of LLM agents\n## Need for comparison with classical ABM","[{\"question\":\"What comparison does AgoraSim perform to validate scenario behavior?\",\"answer\":\"AgoraSim compares the same scenario against matched classical reference dynamics. 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