{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:KYYQKR3ZWRUDKDBUYKHF2QCPE5","short_pith_number":"pith:KYYQKR3Z","schema_version":"1.0","canonical_sha256":"5631054779b468350c34c28e5d404f2752333a536ae1fc6c4451625dda0e1125","source":{"kind":"arxiv","id":"1710.02044","version":1},"attestation_state":"computed","paper":{"title":"A comparison of control strategies applied to a pricing problem in retail","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.DS"],"primary_cat":"math.OC","authors_text":"Asbj{\\o}rn N. Riseth, Chris L. Farmer, Jeff N. Dewynne","submitted_at":"2017-10-05T14:33:32Z","abstract_excerpt":"When sales of a product are affected by randomness in demand, retailers can use dynamic pricing strategies to maximise their profits. In this article the pricing problem is formulated as a stochastic optimal control problem, where the optimal policy can be found by solving the associated Bellman equation. The aim is to investigate Approximate Dynamic Programming algorithms for this problem. For realistic retail applications, modelling the problem and solving it to optimality is intractable. Thus practitioners make simplifying assumptions and design suboptimal policies, but a thorough investiga"},"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":"1710.02044","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-10-05T14:33:32Z","cross_cats_sorted":["math.DS"],"title_canon_sha256":"23acb9cf38aff622c6dacf7af7f36a665adcb2e82aaaa49f8f2b948b51c299a9","abstract_canon_sha256":"5e6858e27e17661e83496468e7d28f593459d243483fa54419ce2340a2261d4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:54.723894Z","signature_b64":"VBk2ZsVvnkp6qnXUHFlgJ5tpggkddRCTG6Z/IuV4TofasmgHONYRs022sExk4z9sh9uZ+jF5IS6/tSjHHpz6Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5631054779b468350c34c28e5d404f2752333a536ae1fc6c4451625dda0e1125","last_reissued_at":"2026-05-18T00:32:54.723259Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:54.723259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A comparison of control strategies applied to a pricing problem in retail","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.DS"],"primary_cat":"math.OC","authors_text":"Asbj{\\o}rn N. Riseth, Chris L. Farmer, Jeff N. Dewynne","submitted_at":"2017-10-05T14:33:32Z","abstract_excerpt":"When sales of a product are affected by randomness in demand, retailers can use dynamic pricing strategies to maximise their profits. In this article the pricing problem is formulated as a stochastic optimal control problem, where the optimal policy can be found by solving the associated Bellman equation. The aim is to investigate Approximate Dynamic Programming algorithms for this problem. For realistic retail applications, modelling the problem and solving it to optimality is intractable. Thus practitioners make simplifying assumptions and design suboptimal policies, but a thorough investiga"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.02044","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":""},"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":"1710.02044","created_at":"2026-05-18T00:32:54.723383+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.02044v1","created_at":"2026-05-18T00:32:54.723383+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.02044","created_at":"2026-05-18T00:32:54.723383+00:00"},{"alias_kind":"pith_short_12","alias_value":"KYYQKR3ZWRUD","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"KYYQKR3ZWRUDKDBU","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"KYYQKR3Z","created_at":"2026-05-18T12:31:28.150371+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/KYYQKR3ZWRUDKDBUYKHF2QCPE5","json":"https://pith.science/pith/KYYQKR3ZWRUDKDBUYKHF2QCPE5.json","graph_json":"https://pith.science/api/pith-number/KYYQKR3ZWRUDKDBUYKHF2QCPE5/graph.json","events_json":"https://pith.science/api/pith-number/KYYQKR3ZWRUDKDBUYKHF2QCPE5/events.json","paper":"https://pith.science/paper/KYYQKR3Z"},"agent_actions":{"view_html":"https://pith.science/pith/KYYQKR3ZWRUDKDBUYKHF2QCPE5","download_json":"https://pith.science/pith/KYYQKR3ZWRUDKDBUYKHF2QCPE5.json","view_paper":"https://pith.science/paper/KYYQKR3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.02044&json=true","fetch_graph":"https://pith.science/api/pith-number/KYYQKR3ZWRUDKDBUYKHF2QCPE5/graph.json","fetch_events":"https://pith.science/api/pith-number/KYYQKR3ZWRUDKDBUYKHF2QCPE5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KYYQKR3ZWRUDKDBUYKHF2QCPE5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KYYQKR3ZWRUDKDBUYKHF2QCPE5/action/storage_attestation","attest_author":"https://pith.science/pith/KYYQKR3ZWRUDKDBUYKHF2QCPE5/action/author_attestation","sign_citation":"https://pith.science/pith/KYYQKR3ZWRUDKDBUYKHF2QCPE5/action/citation_signature","submit_replication":"https://pith.science/pith/KYYQKR3ZWRUDKDBUYKHF2QCPE5/action/replication_record"}},"created_at":"2026-05-18T00:32:54.723383+00:00","updated_at":"2026-05-18T00:32:54.723383+00:00"}