{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NLCVXP7YXDZULDKMRUONKCUZTI","short_pith_number":"pith:NLCVXP7Y","schema_version":"1.0","canonical_sha256":"6ac55bbff8b8f3458d4c8d1cd50a999a08527bd22fe52eab8a614d02a6686171","source":{"kind":"arxiv","id":"1904.06866","version":3},"attestation_state":"computed","paper":{"title":"Predicting human decisions with behavioral theories and machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GT","cs.LG"],"primary_cat":"cs.AI","authors_text":"Daniel Reichman, David Bourgin, Evan C. Carter, Eyal Ert, Ido Erev, James F. Cavanagh, Joshua C. Peterson, Moshe Tennenholtz, Ori Plonsky, Reut Apel, Stuart J. Russell, Thomas L. Griffiths","submitted_at":"2019-04-15T06:12:44Z","abstract_excerpt":"Predicting human decisions under risk and uncertainty remains a fundamental challenge across disciplines. Existing models often struggle even in highly stylized tasks like choice between lotteries. We introduce BEAST Gradient Boosting (BEAST-GB), a hybrid model integrating behavioral theory (BEAST) with machine learning. We first present CPC18, a competition for predicting risky choice, in which BEAST-GB won. Then, using two large datasets, we demonstrate BEAST-GB predicts more accurately than neural networks trained on extensive data and dozens of existing behavioral models. BEAST-GB also gen"},"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":"1904.06866","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2019-04-15T06:12:44Z","cross_cats_sorted":["cs.GT","cs.LG"],"title_canon_sha256":"4d86a5a6629dfd3f2adb49003a5ecf5fa6ec3d73a8b872e2f0782103362e78c7","abstract_canon_sha256":"7be4eadad93ef4e532b5ed92f7e486f91d6adfcda705524b71b28c2a0a1a601a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:51.189540Z","signature_b64":"8L8Jkz+Rg9JDERe4xqKKrlLsO1f41ggo/lFahws1pnJurGheRazM8v9rR9bGKgcrr+DYb3NA/0WvDA6XxkpaBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ac55bbff8b8f3458d4c8d1cd50a999a08527bd22fe52eab8a614d02a6686171","last_reissued_at":"2026-07-05T11:43:51.189042Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:51.189042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting human decisions with behavioral theories and machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GT","cs.LG"],"primary_cat":"cs.AI","authors_text":"Daniel Reichman, David Bourgin, Evan C. Carter, Eyal Ert, Ido Erev, James F. Cavanagh, Joshua C. Peterson, Moshe Tennenholtz, Ori Plonsky, Reut Apel, Stuart J. Russell, Thomas L. Griffiths","submitted_at":"2019-04-15T06:12:44Z","abstract_excerpt":"Predicting human decisions under risk and uncertainty remains a fundamental challenge across disciplines. Existing models often struggle even in highly stylized tasks like choice between lotteries. We introduce BEAST Gradient Boosting (BEAST-GB), a hybrid model integrating behavioral theory (BEAST) with machine learning. We first present CPC18, a competition for predicting risky choice, in which BEAST-GB won. Then, using two large datasets, we demonstrate BEAST-GB predicts more accurately than neural networks trained on extensive data and dozens of existing behavioral models. BEAST-GB also gen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.06866","kind":"arxiv","version":3},"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/1904.06866/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":"1904.06866","created_at":"2026-07-05T11:43:51.189104+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.06866v3","created_at":"2026-07-05T11:43:51.189104+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.06866","created_at":"2026-07-05T11:43:51.189104+00:00"},{"alias_kind":"pith_short_12","alias_value":"NLCVXP7YXDZU","created_at":"2026-07-05T11:43:51.189104+00:00"},{"alias_kind":"pith_short_16","alias_value":"NLCVXP7YXDZULDKM","created_at":"2026-07-05T11:43:51.189104+00:00"},{"alias_kind":"pith_short_8","alias_value":"NLCVXP7Y","created_at":"2026-07-05T11:43:51.189104+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1907.02100","citing_title":"Machine learning and behavioral economics for personalized choice architecture","ref_index":69,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI","json":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI.json","graph_json":"https://pith.science/api/pith-number/NLCVXP7YXDZULDKMRUONKCUZTI/graph.json","events_json":"https://pith.science/api/pith-number/NLCVXP7YXDZULDKMRUONKCUZTI/events.json","paper":"https://pith.science/paper/NLCVXP7Y"},"agent_actions":{"view_html":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI","download_json":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI.json","view_paper":"https://pith.science/paper/NLCVXP7Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.06866&json=true","fetch_graph":"https://pith.science/api/pith-number/NLCVXP7YXDZULDKMRUONKCUZTI/graph.json","fetch_events":"https://pith.science/api/pith-number/NLCVXP7YXDZULDKMRUONKCUZTI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI/action/storage_attestation","attest_author":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI/action/author_attestation","sign_citation":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI/action/citation_signature","submit_replication":"https://pith.science/pith/NLCVXP7YXDZULDKMRUONKCUZTI/action/replication_record"}},"created_at":"2026-07-05T11:43:51.189104+00:00","updated_at":"2026-07-05T11:43:51.189104+00:00"}