{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5YKOPRXCE5EOUK2PTN77L46HFJ","short_pith_number":"pith:5YKOPRXC","schema_version":"1.0","canonical_sha256":"ee14e7c6e22748ea2b4f9b7ff5f3c72a4c514d3a67410de4ebb6b505479530eb","source":{"kind":"arxiv","id":"2307.04986","version":1},"attestation_state":"computed","paper":{"title":"Epidemic Modeling with Generative Agents","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.MA","econ.GN","nlin.AO","physics.soc-ph","q-fin.EC"],"primary_cat":"cs.AI","authors_text":"Aritra Majumdar, Navid Ghaffarzadegan, Niyousha HosseiniChimeh, Ross Williams","submitted_at":"2023-07-11T02:52:32Z","abstract_excerpt":"This study offers a new paradigm of individual-level modeling to address the grand challenge of incorporating human behavior in epidemic models. Using generative artificial intelligence in an agent-based epidemic model, each agent is empowered to make its own reasonings and decisions via connecting to a large language model such as ChatGPT. Through various simulation experiments, we present compelling evidence that generative agents mimic real-world behaviors such as quarantining when sick and self-isolation when cases rise. Collectively, the agents demonstrate patterns akin to multiple waves "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2307.04986","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2023-07-11T02:52:32Z","cross_cats_sorted":["cs.MA","econ.GN","nlin.AO","physics.soc-ph","q-fin.EC"],"title_canon_sha256":"e26c09bddc423685a24bd4fa08ef4ffc1526a6309148f15283627e3875c956e3","abstract_canon_sha256":"057865a16df84add396904255223d75054ca5670feaf18222b8b1374c0c0c10f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:29:24.442690Z","signature_b64":"bn9mwTAsr4QDCrwt0mkBl2FwChwgTuQZbdBd2pwE5m3m4hlsK0mUTeBfYdxD7vmJmAch8vOIuUBOf4ofVcDuAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee14e7c6e22748ea2b4f9b7ff5f3c72a4c514d3a67410de4ebb6b505479530eb","last_reissued_at":"2026-07-05T06:29:24.442283Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:29:24.442283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Epidemic Modeling with Generative Agents","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.MA","econ.GN","nlin.AO","physics.soc-ph","q-fin.EC"],"primary_cat":"cs.AI","authors_text":"Aritra Majumdar, Navid Ghaffarzadegan, Niyousha HosseiniChimeh, Ross Williams","submitted_at":"2023-07-11T02:52:32Z","abstract_excerpt":"This study offers a new paradigm of individual-level modeling to address the grand challenge of incorporating human behavior in epidemic models. Using generative artificial intelligence in an agent-based epidemic model, each agent is empowered to make its own reasonings and decisions via connecting to a large language model such as ChatGPT. Through various simulation experiments, we present compelling evidence that generative agents mimic real-world behaviors such as quarantining when sick and self-isolation when cases rise. Collectively, the agents demonstrate patterns akin to multiple waves "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.04986","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.04986/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2307.04986","created_at":"2026-07-05T06:29:24.442338+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.04986v1","created_at":"2026-07-05T06:29:24.442338+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.04986","created_at":"2026-07-05T06:29:24.442338+00:00"},{"alias_kind":"pith_short_12","alias_value":"5YKOPRXCE5EO","created_at":"2026-07-05T06:29:24.442338+00:00"},{"alias_kind":"pith_short_16","alias_value":"5YKOPRXCE5EOUK2P","created_at":"2026-07-05T06:29:24.442338+00:00"},{"alias_kind":"pith_short_8","alias_value":"5YKOPRXC","created_at":"2026-07-05T06:29:24.442338+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":15,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06757","citing_title":"LLM-powered reasoning in agent-based modeling","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2606.19904","citing_title":"Toward Temporal Realism in City-Scale Crisis Response Simulation using LLM Agents","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06360","citing_title":"An Infectious Disease Spread Simulation Based on Large Language Model Decision Making","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05513","citing_title":"EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2606.02867","citing_title":"The Epi-LLM Framework: probing LLM behavioral priors through epidemiological agent-based models","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28456","citing_title":"Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26704","citing_title":"SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12824","citing_title":"Mechanism Plausibility in Generative Agent-Based Modeling","ref_index":85,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19915","citing_title":"LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2308.11432","citing_title":"A Survey on Large Language Model based Autonomous Agents","ref_index":109,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12824","citing_title":"Mechanism Plausibility in Generative Agent-Based Modeling","ref_index":85,"is_internal_anchor":false},{"citing_arxiv_id":"2402.01680","citing_title":"Large Language Model based Multi-Agents: A Survey of Progress and Challenges","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09197","citing_title":"An Experimental Method to Study Opinion Diffusion in Human-AI Hybrid Societies","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06196","citing_title":"The Granularity Axis: A Micro-to-Macro Latent Direction for Social Roles in Language Models","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07462","citing_title":"The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment","ref_index":67,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ","json":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ.json","graph_json":"https://pith.science/api/pith-number/5YKOPRXCE5EOUK2PTN77L46HFJ/graph.json","events_json":"https://pith.science/api/pith-number/5YKOPRXCE5EOUK2PTN77L46HFJ/events.json","paper":"https://pith.science/paper/5YKOPRXC"},"agent_actions":{"view_html":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ","download_json":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ.json","view_paper":"https://pith.science/paper/5YKOPRXC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.04986&json=true","fetch_graph":"https://pith.science/api/pith-number/5YKOPRXCE5EOUK2PTN77L46HFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/5YKOPRXCE5EOUK2PTN77L46HFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ/action/storage_attestation","attest_author":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ/action/author_attestation","sign_citation":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ/action/citation_signature","submit_replication":"https://pith.science/pith/5YKOPRXCE5EOUK2PTN77L46HFJ/action/replication_record"}},"created_at":"2026-07-05T06:29:24.442338+00:00","updated_at":"2026-07-05T06:29:24.442338+00:00"}