{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:PKWMII4MYFREECF4MWDOI2OWZS","short_pith_number":"pith:PKWMII4M","canonical_record":{"source":{"id":"1402.5481","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-22T05:10:56Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"d26e40ccc4d5cb5727974bd0f70cfea71b46ad8d34c10145e78f41c24504e00d","abstract_canon_sha256":"ceefbe98961c9d5208e62db98bfe1c4032ec951d4548c2455735f835b8327934"},"schema_version":"1.0"},"canonical_sha256":"7aacc4238cc1624208bc6586e469d6cca2cb1c2739f0d5110cfa285069086dc9","source":{"kind":"arxiv","id":"1402.5481","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.5481","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"arxiv_version","alias_value":"1402.5481v4","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.5481","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"pith_short_12","alias_value":"PKWMII4MYFRE","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"PKWMII4MYFREECF4","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"PKWMII4M","created_at":"2026-05-18T12:28:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:PKWMII4MYFREECF4MWDOI2OWZS","target":"record","payload":{"canonical_record":{"source":{"id":"1402.5481","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-22T05:10:56Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"d26e40ccc4d5cb5727974bd0f70cfea71b46ad8d34c10145e78f41c24504e00d","abstract_canon_sha256":"ceefbe98961c9d5208e62db98bfe1c4032ec951d4548c2455735f835b8327934"},"schema_version":"1.0"},"canonical_sha256":"7aacc4238cc1624208bc6586e469d6cca2cb1c2739f0d5110cfa285069086dc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:24.824253Z","signature_b64":"PTDb3HPgp12FvwvBXOEITCshN0HHQ5o7JQsUjne0MwMXHFrNRiqT9gOgp59tspImsWInE3pTpej06HrCOrFYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7aacc4238cc1624208bc6586e469d6cca2cb1c2739f0d5110cfa285069086dc9","last_reissued_at":"2026-05-18T00:10:24.823620Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:24.823620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1402.5481","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/qvgVPYQpRS1r6W/KBRCgIbXZRfMQ2Id7Uh0GUJvLNsOWNkedAhkmFjj0aoM0RPXZvj6xTjn6PaJA3tOcfG4BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T05:55:06.428508Z"},"content_sha256":"3d69cf69ce805ba62a97cec1f95b6653c793753bc92a0637f8f740529a1ed615","schema_version":"1.0","event_id":"sha256:3d69cf69ce805ba62a97cec1f95b6653c793753bc92a0637f8f740529a1ed615"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:PKWMII4MYFREECF4MWDOI2OWZS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Predictive to Prescriptive Analytics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Dimitris Bertsimas, Nathan Kallus","submitted_at":"2014-02-22T05:10:56Z","abstract_excerpt":"In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practical experience with the data available in applications of OR/MS, we consider data consisting, not only of observations of quantities with direct effect on costs/revenues, such as demand or returns, but predominantly of observations of associated auxiliary quantities. The main prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.5481","kind":"arxiv","version":4},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QG6+TEKKnGE1l9i+I+uBsfyRaXWk4UnXw4BhaQoHdsKnISRGGc6tO0GzSZY/+KJyGyqxArd4rPGmiEgfMKW+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T05:55:06.428863Z"},"content_sha256":"4082912167edefa8d5e3b561fee0eddd0501df9c67987aaa07e8880d865aa814","schema_version":"1.0","event_id":"sha256:4082912167edefa8d5e3b561fee0eddd0501df9c67987aaa07e8880d865aa814"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PKWMII4MYFREECF4MWDOI2OWZS/bundle.json","state_url":"https://pith.science/pith/PKWMII4MYFREECF4MWDOI2OWZS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PKWMII4MYFREECF4MWDOI2OWZS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T05:55:06Z","links":{"resolver":"https://pith.science/pith/PKWMII4MYFREECF4MWDOI2OWZS","bundle":"https://pith.science/pith/PKWMII4MYFREECF4MWDOI2OWZS/bundle.json","state":"https://pith.science/pith/PKWMII4MYFREECF4MWDOI2OWZS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PKWMII4MYFREECF4MWDOI2OWZS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:PKWMII4MYFREECF4MWDOI2OWZS","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":"ceefbe98961c9d5208e62db98bfe1c4032ec951d4548c2455735f835b8327934","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-22T05:10:56Z","title_canon_sha256":"d26e40ccc4d5cb5727974bd0f70cfea71b46ad8d34c10145e78f41c24504e00d"},"schema_version":"1.0","source":{"id":"1402.5481","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.5481","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"arxiv_version","alias_value":"1402.5481v4","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.5481","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"pith_short_12","alias_value":"PKWMII4MYFRE","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"PKWMII4MYFREECF4","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"PKWMII4M","created_at":"2026-05-18T12:28:43Z"}],"graph_snapshots":[{"event_id":"sha256:4082912167edefa8d5e3b561fee0eddd0501df9c67987aaa07e8880d865aa814","target":"graph","created_at":"2026-05-18T00:10:24Z","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"},"paper":{"abstract_excerpt":"In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practical experience with the data available in applications of OR/MS, we consider data consisting, not only of observations of quantities with direct effect on costs/revenues, such as demand or returns, but predominantly of observations of associated auxiliary quantities. The main prob","authors_text":"Dimitris Bertsimas, Nathan Kallus","cross_cats":["cs.LG","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-22T05:10:56Z","title":"From Predictive to Prescriptive Analytics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.5481","kind":"arxiv","version":4},"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":"sha256:3d69cf69ce805ba62a97cec1f95b6653c793753bc92a0637f8f740529a1ed615","target":"record","created_at":"2026-05-18T00:10:24Z","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":"ceefbe98961c9d5208e62db98bfe1c4032ec951d4548c2455735f835b8327934","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-22T05:10:56Z","title_canon_sha256":"d26e40ccc4d5cb5727974bd0f70cfea71b46ad8d34c10145e78f41c24504e00d"},"schema_version":"1.0","source":{"id":"1402.5481","kind":"arxiv","version":4}},"canonical_sha256":"7aacc4238cc1624208bc6586e469d6cca2cb1c2739f0d5110cfa285069086dc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7aacc4238cc1624208bc6586e469d6cca2cb1c2739f0d5110cfa285069086dc9","first_computed_at":"2026-05-18T00:10:24.823620Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:24.823620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PTDb3HPgp12FvwvBXOEITCshN0HHQ5o7JQsUjne0MwMXHFrNRiqT9gOgp59tspImsWInE3pTpej06HrCOrFYCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:24.824253Z","signed_message":"canonical_sha256_bytes"},"source_id":"1402.5481","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d69cf69ce805ba62a97cec1f95b6653c793753bc92a0637f8f740529a1ed615","sha256:4082912167edefa8d5e3b561fee0eddd0501df9c67987aaa07e8880d865aa814"],"state_sha256":"9f2c1c1f29e92783e0f54707d64d96d178e3220f6f869642237bc3fc1b41e5c2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CFt1sqBCIVdvefBKWp6s9ttiXvezhJstVRfPwy3MInPE9cShP67YDR7gnGOgL3+uRUufdht6CrZtOpqvmc3nCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T05:55:06.430792Z","bundle_sha256":"07e7a02bb270b7074e0edfb83337c6b4609e89cabf6ea37baf0f2db64b2bfe23"}}