{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4S3QB2KJPI2RWAUPEHK6TTKP23","short_pith_number":"pith:4S3QB2KJ","schema_version":"1.0","canonical_sha256":"e4b700e9497a351b028f21d5e9cd4fd6fbc63eb15150b631eb1370221f29e84e","source":{"kind":"arxiv","id":"2607.28854","version":1},"attestation_state":"computed","paper":{"title":"An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alex Davis, Destenie Nock, Sheng Lun Christine Cao","submitted_at":"2026-07-30T21:38:37Z","abstract_excerpt":"Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the boundaries of discrete choice modeling for policy-based preference elicitation by adopting a data-driven approach or learning individual preferences. However, there is limited knowledge of how well machine learning methods can estimate individual discrete choice rules under individual heterogeneity, especially in the context of challenges often experienced during preference elicitation. This study evaluates four mach"},"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":"2607.28854","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-30T21:38:37Z","cross_cats_sorted":[],"title_canon_sha256":"2788d5b3bed9b946167ef623e141ef3dbf28a95aa7aa9dd3d0610dfd6edb8b00","abstract_canon_sha256":"2bd6f9c1656f84047e1e5f3a14ff32fdb25d3e6b87b9328abb0c6e1c60d38ff5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-03T01:17:13.143306Z","signature_b64":"rMn2XEjxo+HXRXNp1MiZ//zjKOzsoGlkfUASXaoC8rswAB5nX4dWDMnMNiJMta3wFsU/M+8mjNoWBAeLVnqUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4b700e9497a351b028f21d5e9cd4fd6fbc63eb15150b631eb1370221f29e84e","last_reissued_at":"2026-08-03T01:17:13.141731Z","signature_status":"signed_v1","first_computed_at":"2026-08-03T01:17:13.141731Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alex Davis, Destenie Nock, Sheng Lun Christine Cao","submitted_at":"2026-07-30T21:38:37Z","abstract_excerpt":"Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the boundaries of discrete choice modeling for policy-based preference elicitation by adopting a data-driven approach or learning individual preferences. However, there is limited knowledge of how well machine learning methods can estimate individual discrete choice rules under individual heterogeneity, especially in the context of challenges often experienced during preference elicitation. This study evaluates four mach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28854","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/2607.28854/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":"2607.28854","created_at":"2026-08-03T01:17:13.142690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.28854v1","created_at":"2026-08-03T01:17:13.142690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28854","created_at":"2026-08-03T01:17:13.142690+00:00"},{"alias_kind":"pith_short_12","alias_value":"4S3QB2KJPI2R","created_at":"2026-08-03T01:17:13.142690+00:00"},{"alias_kind":"pith_short_16","alias_value":"4S3QB2KJPI2RWAUP","created_at":"2026-08-03T01:17:13.142690+00:00"},{"alias_kind":"pith_short_8","alias_value":"4S3QB2KJ","created_at":"2026-08-03T01:17:13.142690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23","json":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23.json","graph_json":"https://pith.science/api/pith-number/4S3QB2KJPI2RWAUPEHK6TTKP23/graph.json","events_json":"https://pith.science/api/pith-number/4S3QB2KJPI2RWAUPEHK6TTKP23/events.json","paper":"https://pith.science/paper/4S3QB2KJ"},"agent_actions":{"view_html":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23","download_json":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23.json","view_paper":"https://pith.science/paper/4S3QB2KJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.28854&json=true","fetch_graph":"https://pith.science/api/pith-number/4S3QB2KJPI2RWAUPEHK6TTKP23/graph.json","fetch_events":"https://pith.science/api/pith-number/4S3QB2KJPI2RWAUPEHK6TTKP23/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23/action/storage_attestation","attest_author":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23/action/author_attestation","sign_citation":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23/action/citation_signature","submit_replication":"https://pith.science/pith/4S3QB2KJPI2RWAUPEHK6TTKP23/action/replication_record"}},"created_at":"2026-08-03T01:17:13.142690+00:00","updated_at":"2026-08-03T01:17:13.142690+00:00"}