{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:K4ROG2TQA6DJVE3GKSBAO25FPW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"59ebbe78a16d62b28cc514ca49f3aca005fa6496d9814a2cfe813cbb5803dd54","cross_cats_sorted":["cs.MA","econ.GN","q-fin.EC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2025-08-26T02:02:18Z","title_canon_sha256":"93de15a6bcd7f2e7a4ae0e21da901f6c3f812457502deafa2800c7c053a5bedf"},"schema_version":"1.0","source":{"id":"2508.18600","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.18600","created_at":"2026-07-05T11:59:27Z"},{"alias_kind":"arxiv_version","alias_value":"2508.18600v1","created_at":"2026-07-05T11:59:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18600","created_at":"2026-07-05T11:59:27Z"},{"alias_kind":"pith_short_12","alias_value":"K4ROG2TQA6DJ","created_at":"2026-07-05T11:59:27Z"},{"alias_kind":"pith_short_16","alias_value":"K4ROG2TQA6DJVE3G","created_at":"2026-07-05T11:59:27Z"},{"alias_kind":"pith_short_8","alias_value":"K4ROG2TQ","created_at":"2026-07-05T11:59:27Z"}],"graph_snapshots":[{"event_id":"sha256:11ddcaba8713a7a097d6953c1fd4b330d59d9ab1af5edf0ec03e5a2679492c6d","target":"graph","created_at":"2026-07-05T11:59:27Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.18600/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) are increasingly used to simulate human decision-making, but their intrinsic biases often diverge from real human behavior--limiting their ability to reflect population-level diversity. We address this challenge with a persona-based approach that leverages individual-level behavioral data from behavioral economics to adjust model biases. Applying this method to the ultimatum game--a standard but difficult benchmark for LLMs--we observe improved alignment between simulated and empirical behavior, particularly on the responder side. While further refinement of trait ","authors_text":"Ayato Kitadai, Nariaki Nishino, Yusuke Fukasawa","cross_cats":["cs.MA","econ.GN","q-fin.EC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2025-08-26T02:02:18Z","title":"Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18600","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3ced8daa147a4a215ceeb23c03b00b822451b95a3e391775d0c132da0bfeaae6","target":"record","created_at":"2026-07-05T11:59:27Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"59ebbe78a16d62b28cc514ca49f3aca005fa6496d9814a2cfe813cbb5803dd54","cross_cats_sorted":["cs.MA","econ.GN","q-fin.EC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2025-08-26T02:02:18Z","title_canon_sha256":"93de15a6bcd7f2e7a4ae0e21da901f6c3f812457502deafa2800c7c053a5bedf"},"schema_version":"1.0","source":{"id":"2508.18600","kind":"arxiv","version":1}},"canonical_sha256":"5722e36a7007869a93665482076ba57db13a14db3876b55fbe72afd0fe852d83","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5722e36a7007869a93665482076ba57db13a14db3876b55fbe72afd0fe852d83","first_computed_at":"2026-07-05T11:59:27.506558Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:59:27.506558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X3OjagCwDPQFqdI+vaXFvMTfBc+4mPBlp60b8ZBk5iKI2zh7GyRxBOfc4AlcCtPSjlq8eTrPcMgYk/FWkDECAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:59:27.507001Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.18600","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ced8daa147a4a215ceeb23c03b00b822451b95a3e391775d0c132da0bfeaae6","sha256:11ddcaba8713a7a097d6953c1fd4b330d59d9ab1af5edf0ec03e5a2679492c6d"],"state_sha256":"ded7ac6e8e62dd1b9e0cdea5ed467322a83582a0d4aa36998d0b643970f2ada5"}