{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:J2D4JG67RLNN5REXDP2U42BA7V","short_pith_number":"pith:J2D4JG67","schema_version":"1.0","canonical_sha256":"4e87c49bdf8adadec4971bf54e6820fd5934345750314e197ccf63080e7882af","source":{"kind":"arxiv","id":"1809.03154","version":1},"attestation_state":"computed","paper":{"title":"Learning Time Dependent Choice","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.GT","authors_text":"Siddharth Prasad, Zachary Chase","submitted_at":"2018-09-10T06:49:07Z","abstract_excerpt":"We explore questions dealing with the learnability of models of choice over time. We present a large class of preference models defined by a structural criterion for which we are able to obtain an exponential improvement over previously known learning bounds for more general preference models. This in particular implies that the three most important discounted utility models of intertemporal choice -- exponential, hyperbolic, and quasi-hyperbolic discounting -- are learnable in the PAC setting with VC dimension that grows logarithmically in the number of time periods. We also examine these mod"},"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":"1809.03154","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2018-09-10T06:49:07Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"64c63e2c0e90491eb7845178fc4300d3b7995a25e1e05ccf9f147420170bdd20","abstract_canon_sha256":"e47d84f1ae445989be5cde6d7eb29d9a71b906f0654e066d7bd0069b44004136"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:09.829900Z","signature_b64":"BtUl9Nsx21a3tAE/zqXEWC6YKGhGUzjZSeoV8MbyhEWv3HwBC3q2N3ikLIWBGXHBSkFNwbuSFNUGoKX5JNFjBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e87c49bdf8adadec4971bf54e6820fd5934345750314e197ccf63080e7882af","last_reissued_at":"2026-05-18T00:06:09.829249Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:09.829249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Time Dependent Choice","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.GT","authors_text":"Siddharth Prasad, Zachary Chase","submitted_at":"2018-09-10T06:49:07Z","abstract_excerpt":"We explore questions dealing with the learnability of models of choice over time. We present a large class of preference models defined by a structural criterion for which we are able to obtain an exponential improvement over previously known learning bounds for more general preference models. This in particular implies that the three most important discounted utility models of intertemporal choice -- exponential, hyperbolic, and quasi-hyperbolic discounting -- are learnable in the PAC setting with VC dimension that grows logarithmically in the number of time periods. We also examine these mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.03154","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":"1809.03154","created_at":"2026-05-18T00:06:09.829332+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.03154v1","created_at":"2026-05-18T00:06:09.829332+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.03154","created_at":"2026-05-18T00:06:09.829332+00:00"},{"alias_kind":"pith_short_12","alias_value":"J2D4JG67RLNN","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"J2D4JG67RLNN5REX","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"J2D4JG67","created_at":"2026-05-18T12:32:31.084164+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/J2D4JG67RLNN5REXDP2U42BA7V","json":"https://pith.science/pith/J2D4JG67RLNN5REXDP2U42BA7V.json","graph_json":"https://pith.science/api/pith-number/J2D4JG67RLNN5REXDP2U42BA7V/graph.json","events_json":"https://pith.science/api/pith-number/J2D4JG67RLNN5REXDP2U42BA7V/events.json","paper":"https://pith.science/paper/J2D4JG67"},"agent_actions":{"view_html":"https://pith.science/pith/J2D4JG67RLNN5REXDP2U42BA7V","download_json":"https://pith.science/pith/J2D4JG67RLNN5REXDP2U42BA7V.json","view_paper":"https://pith.science/paper/J2D4JG67","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.03154&json=true","fetch_graph":"https://pith.science/api/pith-number/J2D4JG67RLNN5REXDP2U42BA7V/graph.json","fetch_events":"https://pith.science/api/pith-number/J2D4JG67RLNN5REXDP2U42BA7V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J2D4JG67RLNN5REXDP2U42BA7V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J2D4JG67RLNN5REXDP2U42BA7V/action/storage_attestation","attest_author":"https://pith.science/pith/J2D4JG67RLNN5REXDP2U42BA7V/action/author_attestation","sign_citation":"https://pith.science/pith/J2D4JG67RLNN5REXDP2U42BA7V/action/citation_signature","submit_replication":"https://pith.science/pith/J2D4JG67RLNN5REXDP2U42BA7V/action/replication_record"}},"created_at":"2026-05-18T00:06:09.829332+00:00","updated_at":"2026-05-18T00:06:09.829332+00:00"}