{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QJYSK5XU2363CILNAPJNCLYMJS","short_pith_number":"pith:QJYSK5XU","schema_version":"1.0","canonical_sha256":"82712576f4d6fdb1216d03d2d12f0c4ca2079a87356bb2cf6ba24d23096a4f2f","source":{"kind":"arxiv","id":"1903.08322","version":2},"attestation_state":"computed","paper":{"title":"A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.AI","authors_text":"Tushant Jha, Yair Zick","submitted_at":"2019-03-20T02:39:50Z","abstract_excerpt":"The past few years have seen several works on learning economic solutions from data; these include optimal auction design, function optimization, stable payoffs in cooperative games and more. In this work, we provide a unified learning-theoretic methodology for modeling such problems, and establish tools for determining whether a given economic solution concept can be learned from data. Our learning theoretic framework generalizes a notion of function space dimension -- the graph dimension -- adapting it to the solution concept learning domain. We identify sufficient conditions for the PAC lea"},"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":"1903.08322","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-03-20T02:39:50Z","cross_cats_sorted":["cs.GT"],"title_canon_sha256":"41fe4721a57a888e0d9d4f1e9bf451c409bd985e26ac5b9e909f9db56a959148","abstract_canon_sha256":"8114bfd2107d4bb29ba4ba234acf7a7cdb238e419f3a1319c4cc478cd31cf36f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:32.109124Z","signature_b64":"TMq/Ou+yypl/6P12MjRUxx0/lU59MVA2uoHV9IzYi0US0P2pf5rpJlwzHulNsa3Wxu4UtTj5edkRTep87nszAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82712576f4d6fdb1216d03d2d12f0c4ca2079a87356bb2cf6ba24d23096a4f2f","last_reissued_at":"2026-07-05T11:04:32.108639Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:32.108639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.AI","authors_text":"Tushant Jha, Yair Zick","submitted_at":"2019-03-20T02:39:50Z","abstract_excerpt":"The past few years have seen several works on learning economic solutions from data; these include optimal auction design, function optimization, stable payoffs in cooperative games and more. In this work, we provide a unified learning-theoretic methodology for modeling such problems, and establish tools for determining whether a given economic solution concept can be learned from data. Our learning theoretic framework generalizes a notion of function space dimension -- the graph dimension -- adapting it to the solution concept learning domain. We identify sufficient conditions for the PAC lea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08322","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1903.08322/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":"1903.08322","created_at":"2026-07-05T11:04:32.108697+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.08322v2","created_at":"2026-07-05T11:04:32.108697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08322","created_at":"2026-07-05T11:04:32.108697+00:00"},{"alias_kind":"pith_short_12","alias_value":"QJYSK5XU2363","created_at":"2026-07-05T11:04:32.108697+00:00"},{"alias_kind":"pith_short_16","alias_value":"QJYSK5XU2363CILN","created_at":"2026-07-05T11:04:32.108697+00:00"},{"alias_kind":"pith_short_8","alias_value":"QJYSK5XU","created_at":"2026-07-05T11:04:32.108697+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/QJYSK5XU2363CILNAPJNCLYMJS","json":"https://pith.science/pith/QJYSK5XU2363CILNAPJNCLYMJS.json","graph_json":"https://pith.science/api/pith-number/QJYSK5XU2363CILNAPJNCLYMJS/graph.json","events_json":"https://pith.science/api/pith-number/QJYSK5XU2363CILNAPJNCLYMJS/events.json","paper":"https://pith.science/paper/QJYSK5XU"},"agent_actions":{"view_html":"https://pith.science/pith/QJYSK5XU2363CILNAPJNCLYMJS","download_json":"https://pith.science/pith/QJYSK5XU2363CILNAPJNCLYMJS.json","view_paper":"https://pith.science/paper/QJYSK5XU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.08322&json=true","fetch_graph":"https://pith.science/api/pith-number/QJYSK5XU2363CILNAPJNCLYMJS/graph.json","fetch_events":"https://pith.science/api/pith-number/QJYSK5XU2363CILNAPJNCLYMJS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QJYSK5XU2363CILNAPJNCLYMJS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QJYSK5XU2363CILNAPJNCLYMJS/action/storage_attestation","attest_author":"https://pith.science/pith/QJYSK5XU2363CILNAPJNCLYMJS/action/author_attestation","sign_citation":"https://pith.science/pith/QJYSK5XU2363CILNAPJNCLYMJS/action/citation_signature","submit_replication":"https://pith.science/pith/QJYSK5XU2363CILNAPJNCLYMJS/action/replication_record"}},"created_at":"2026-07-05T11:04:32.108697+00:00","updated_at":"2026-07-05T11:04:32.108697+00:00"}