{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:M22GNPWPBGVXSDZLXE5GSK2QRL","short_pith_number":"pith:M22GNPWP","schema_version":"1.0","canonical_sha256":"66b466becf09ab790f2bb93a692b508af27ea1d82cae7ba8a1d30726dd3c13e2","source":{"kind":"arxiv","id":"2408.02784","version":1},"attestation_state":"computed","paper":{"title":"LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andrew W. Lo, Jillian Ross, Yoon Kim","submitted_at":"2024-08-05T19:00:43Z","abstract_excerpt":"Humans are not homo economicus (i.e., rational economic beings). As humans, we exhibit systematic behavioral biases such as loss aversion, anchoring, framing, etc., which lead us to make suboptimal economic decisions. Insofar as such biases may be embedded in text data on which large language models (LLMs) are trained, to what extent are LLMs prone to the same behavioral biases? Understanding these biases in LLMs is crucial for deploying LLMs to support human decision-making. We propose utility theory-a paradigm at the core of modern economic theory-as an approach to evaluate the economic bias"},"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":"2408.02784","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-05T19:00:43Z","cross_cats_sorted":[],"title_canon_sha256":"4f758b460f21ce85128b3e92f025eebf6c19b203fddd6d3cf46e44a1f32bb0e9","abstract_canon_sha256":"5c9257780541caa60b387f2318114d512e708126461ab96ea8fe76f9e3b88968"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:40.033409Z","signature_b64":"MPvotE916e5lo4fG5leCXEdl+a6/0HS1uDWVQqZgqf0sQhuVjVT3kAEI6lsVLhbR6ZqZdeCETjyHmhGb1HbZDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66b466becf09ab790f2bb93a692b508af27ea1d82cae7ba8a1d30726dd3c13e2","last_reissued_at":"2026-07-05T08:52:40.032973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:40.032973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andrew W. Lo, Jillian Ross, Yoon Kim","submitted_at":"2024-08-05T19:00:43Z","abstract_excerpt":"Humans are not homo economicus (i.e., rational economic beings). As humans, we exhibit systematic behavioral biases such as loss aversion, anchoring, framing, etc., which lead us to make suboptimal economic decisions. Insofar as such biases may be embedded in text data on which large language models (LLMs) are trained, to what extent are LLMs prone to the same behavioral biases? Understanding these biases in LLMs is crucial for deploying LLMs to support human decision-making. We propose utility theory-a paradigm at the core of modern economic theory-as an approach to evaluate the economic bias"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.02784","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/2408.02784/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":"2408.02784","created_at":"2026-07-05T08:52:40.033032+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.02784v1","created_at":"2026-07-05T08:52:40.033032+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.02784","created_at":"2026-07-05T08:52:40.033032+00:00"},{"alias_kind":"pith_short_12","alias_value":"M22GNPWPBGVX","created_at":"2026-07-05T08:52:40.033032+00:00"},{"alias_kind":"pith_short_16","alias_value":"M22GNPWPBGVXSDZL","created_at":"2026-07-05T08:52:40.033032+00:00"},{"alias_kind":"pith_short_8","alias_value":"M22GNPWP","created_at":"2026-07-05T08:52:40.033032+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.02464","citing_title":"Will Scaling Improve Social Simulation with LLMs?","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05194","citing_title":"Temporal Preference Concepts and their Functions in a Large Language Model","ref_index":94,"is_internal_anchor":false},{"citing_arxiv_id":"2503.22693","citing_title":"Bridging Language Models and Financial Analysis","ref_index":80,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL","json":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL.json","graph_json":"https://pith.science/api/pith-number/M22GNPWPBGVXSDZLXE5GSK2QRL/graph.json","events_json":"https://pith.science/api/pith-number/M22GNPWPBGVXSDZLXE5GSK2QRL/events.json","paper":"https://pith.science/paper/M22GNPWP"},"agent_actions":{"view_html":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL","download_json":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL.json","view_paper":"https://pith.science/paper/M22GNPWP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.02784&json=true","fetch_graph":"https://pith.science/api/pith-number/M22GNPWPBGVXSDZLXE5GSK2QRL/graph.json","fetch_events":"https://pith.science/api/pith-number/M22GNPWPBGVXSDZLXE5GSK2QRL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL/action/storage_attestation","attest_author":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL/action/author_attestation","sign_citation":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL/action/citation_signature","submit_replication":"https://pith.science/pith/M22GNPWPBGVXSDZLXE5GSK2QRL/action/replication_record"}},"created_at":"2026-07-05T08:52:40.033032+00:00","updated_at":"2026-07-05T08:52:40.033032+00:00"}