{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PCBMTFERU6A4TNNIH4NL2AOSQ5","short_pith_number":"pith:PCBMTFER","schema_version":"1.0","canonical_sha256":"7882c99491a781c9b5a83f1abd01d2874560928bb1ac34425410846921a51c49","source":{"kind":"arxiv","id":"2412.15291","version":4},"attestation_state":"computed","paper":{"title":"A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.CL","authors_text":"Chenxiao Yu, Emilio Ferrara, Jinyi Ye, Xiyang Hu, Yuangang Li, Yue Zhao, Zheng Li","submitted_at":"2024-12-19T07:10:51Z","abstract_excerpt":"While LLMs have demonstrated remarkable capabilities in text generation and reasoning, their ability to simulate human decision-making -- particularly in political contexts -- remains an open question. However, modeling voter behavior presents unique challenges due to limited voter-level data, evolving political landscapes, and the complexity of human reasoning. In this study, we develop a theory-driven, multi-step reasoning framework that integrates demographic, temporal and ideological factors to simulate voter decision-making at scale. Using synthetic personas calibrated to real-world voter"},"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":"2412.15291","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-19T07:10:51Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"964c37b7e3f5eed91b6ccb025abcbffc37550d4a573d94899ca327f47dacbdc5","abstract_canon_sha256":"47038184e6251d5160e0cfd4db3865a139302dffbf594edd47a7dc3cc4fb936b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:59.533839Z","signature_b64":"WEkO/ovmmemqo4BGEhpvdHARRCNDHCZGrsvN/b1rBIHe+DFI7A8QLGUZzkVfKv1WfJJ5E8CzRBjPtk/9PxW9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7882c99491a781c9b5a83f1abd01d2874560928bb1ac34425410846921a51c49","last_reissued_at":"2026-07-05T10:46:59.533353Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:59.533353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.CL","authors_text":"Chenxiao Yu, Emilio Ferrara, Jinyi Ye, Xiyang Hu, Yuangang Li, Yue Zhao, Zheng Li","submitted_at":"2024-12-19T07:10:51Z","abstract_excerpt":"While LLMs have demonstrated remarkable capabilities in text generation and reasoning, their ability to simulate human decision-making -- particularly in political contexts -- remains an open question. However, modeling voter behavior presents unique challenges due to limited voter-level data, evolving political landscapes, and the complexity of human reasoning. In this study, we develop a theory-driven, multi-step reasoning framework that integrates demographic, temporal and ideological factors to simulate voter decision-making at scale. Using synthetic personas calibrated to real-world voter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15291","kind":"arxiv","version":4},"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/2412.15291/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":"2412.15291","created_at":"2026-07-05T10:46:59.533411+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.15291v4","created_at":"2026-07-05T10:46:59.533411+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15291","created_at":"2026-07-05T10:46:59.533411+00:00"},{"alias_kind":"pith_short_12","alias_value":"PCBMTFERU6A4","created_at":"2026-07-05T10:46:59.533411+00:00"},{"alias_kind":"pith_short_16","alias_value":"PCBMTFERU6A4TNNI","created_at":"2026-07-05T10:46:59.533411+00:00"},{"alias_kind":"pith_short_8","alias_value":"PCBMTFER","created_at":"2026-07-05T10:46:59.533411+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22741","citing_title":"GRADE: Graph Representation of LLM Agent Dependency and Execution","ref_index":111,"is_internal_anchor":false},{"citing_arxiv_id":"2607.00937","citing_title":"Persona Non Grata: LLM Persona-Driven Generations in MCQA are Unstable in Distinct Dimensions","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12545","citing_title":"Cross-Cultural Simulation of Citizen Emotional Responses to Bureaucratic Red Tape Using LLM Agents","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5","json":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5.json","graph_json":"https://pith.science/api/pith-number/PCBMTFERU6A4TNNIH4NL2AOSQ5/graph.json","events_json":"https://pith.science/api/pith-number/PCBMTFERU6A4TNNIH4NL2AOSQ5/events.json","paper":"https://pith.science/paper/PCBMTFER"},"agent_actions":{"view_html":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5","download_json":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5.json","view_paper":"https://pith.science/paper/PCBMTFER","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.15291&json=true","fetch_graph":"https://pith.science/api/pith-number/PCBMTFERU6A4TNNIH4NL2AOSQ5/graph.json","fetch_events":"https://pith.science/api/pith-number/PCBMTFERU6A4TNNIH4NL2AOSQ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5/action/storage_attestation","attest_author":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5/action/author_attestation","sign_citation":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5/action/citation_signature","submit_replication":"https://pith.science/pith/PCBMTFERU6A4TNNIH4NL2AOSQ5/action/replication_record"}},"created_at":"2026-07-05T10:46:59.533411+00:00","updated_at":"2026-07-05T10:46:59.533411+00:00"}