{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:SZSDF5WF3ODWLGUQWV3GR36PPP","short_pith_number":"pith:SZSDF5WF","schema_version":"1.0","canonical_sha256":"966432f6c5db87659a90b57668efcf7bc549d94b16f2855d1d38028f65d5153f","source":{"kind":"arxiv","id":"1605.05422","version":2},"attestation_state":"computed","paper":{"title":"Optimization Beyond Prediction: Prescriptive Price Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Ryohei Fujimaki, Shinji Ito","submitted_at":"2016-05-18T02:46:14Z","abstract_excerpt":"This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple products, on the basis of historical data, which reveal complex relationships between sales and prices, such as price elasticity of demand and cannibalization. Then, it constructs a mathematical optimization problem on the basis of those predictive formulas. We present that the opt"},"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":"1605.05422","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-05-18T02:46:14Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"224071222a672f9cb9bc9d3a9afce2f1a62aa4f0f0e01b56c90e4733c13c03ab","abstract_canon_sha256":"7d440e6a9fef165865aac368f1a9a956115696213f03b5009998f768d914b17f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:46.849622Z","signature_b64":"3Fa1r7n4NggqBEqjsChRDSSQTZifXmcpcyiQXygx2zta7XFXUXD5djymDt4u315s6XvAgCS9F/MHX+u6xyrLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"966432f6c5db87659a90b57668efcf7bc549d94b16f2855d1d38028f65d5153f","last_reissued_at":"2026-05-18T01:13:46.848873Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:46.848873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimization Beyond Prediction: Prescriptive Price Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Ryohei Fujimaki, Shinji Ito","submitted_at":"2016-05-18T02:46:14Z","abstract_excerpt":"This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple products, on the basis of historical data, which reveal complex relationships between sales and prices, such as price elasticity of demand and cannibalization. Then, it constructs a mathematical optimization problem on the basis of those predictive formulas. We present that the opt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.05422","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1605.05422","created_at":"2026-05-18T01:13:46.848988+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.05422v2","created_at":"2026-05-18T01:13:46.848988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.05422","created_at":"2026-05-18T01:13:46.848988+00:00"},{"alias_kind":"pith_short_12","alias_value":"SZSDF5WF3ODW","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"SZSDF5WF3ODWLGUQ","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"SZSDF5WF","created_at":"2026-05-18T12:30:44.179134+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/SZSDF5WF3ODWLGUQWV3GR36PPP","json":"https://pith.science/pith/SZSDF5WF3ODWLGUQWV3GR36PPP.json","graph_json":"https://pith.science/api/pith-number/SZSDF5WF3ODWLGUQWV3GR36PPP/graph.json","events_json":"https://pith.science/api/pith-number/SZSDF5WF3ODWLGUQWV3GR36PPP/events.json","paper":"https://pith.science/paper/SZSDF5WF"},"agent_actions":{"view_html":"https://pith.science/pith/SZSDF5WF3ODWLGUQWV3GR36PPP","download_json":"https://pith.science/pith/SZSDF5WF3ODWLGUQWV3GR36PPP.json","view_paper":"https://pith.science/paper/SZSDF5WF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.05422&json=true","fetch_graph":"https://pith.science/api/pith-number/SZSDF5WF3ODWLGUQWV3GR36PPP/graph.json","fetch_events":"https://pith.science/api/pith-number/SZSDF5WF3ODWLGUQWV3GR36PPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SZSDF5WF3ODWLGUQWV3GR36PPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SZSDF5WF3ODWLGUQWV3GR36PPP/action/storage_attestation","attest_author":"https://pith.science/pith/SZSDF5WF3ODWLGUQWV3GR36PPP/action/author_attestation","sign_citation":"https://pith.science/pith/SZSDF5WF3ODWLGUQWV3GR36PPP/action/citation_signature","submit_replication":"https://pith.science/pith/SZSDF5WF3ODWLGUQWV3GR36PPP/action/replication_record"}},"created_at":"2026-05-18T01:13:46.848988+00:00","updated_at":"2026-05-18T01:13:46.848988+00:00"}