{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:S5YC577EBDBKEC77QVFCBLC4UJ","short_pith_number":"pith:S5YC577E","schema_version":"1.0","canonical_sha256":"97702effe408c2a20bff854a20ac5ca27646ccc57ae1d39c26c8ba090a49843e","source":{"kind":"arxiv","id":"1711.03156","version":2},"attestation_state":"computed","paper":{"title":"Learning Deep Mean Field Games for Modeling Large Population Behavior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Hongyuan Zha, Huan Xu, Jiachen Yang, Rakshit Trivedi, Xiaojing Ye","submitted_at":"2017-11-08T20:43:39Z","abstract_excerpt":"We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models "},"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":"1711.03156","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-08T20:43:39Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"9ccd8326e61828f19c76d9cabd977edd05063b3e2d5008cde0d5d06dac93b419","abstract_canon_sha256":"7c39db8ad997304e7f4bfc3fda3739069628ac2521cfe4b6b67724708ea4d8f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:54.180127Z","signature_b64":"JvWsOmcI36bNPh9PeSTlqFIV7CW+2llZP2HD0nVBPcjXfgkZ1afNebeYLRx7Ce6bbTMHPk4Br5hlDnRwNwrvAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97702effe408c2a20bff854a20ac5ca27646ccc57ae1d39c26c8ba090a49843e","last_reissued_at":"2026-05-18T00:17:54.179365Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:54.179365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Deep Mean Field Games for Modeling Large Population Behavior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Hongyuan Zha, Huan Xu, Jiachen Yang, Rakshit Trivedi, Xiaojing Ye","submitted_at":"2017-11-08T20:43:39Z","abstract_excerpt":"We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.03156","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":"1711.03156","created_at":"2026-05-18T00:17:54.179479+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.03156v2","created_at":"2026-05-18T00:17:54.179479+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.03156","created_at":"2026-05-18T00:17:54.179479+00:00"},{"alias_kind":"pith_short_12","alias_value":"S5YC577EBDBK","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"S5YC577EBDBKEC77","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"S5YC577E","created_at":"2026-05-18T12:31:43.269735+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.05685","citing_title":"Discrete Mean Field Games on Finite Graphs as Initial Value Optimization","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ","json":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ.json","graph_json":"https://pith.science/api/pith-number/S5YC577EBDBKEC77QVFCBLC4UJ/graph.json","events_json":"https://pith.science/api/pith-number/S5YC577EBDBKEC77QVFCBLC4UJ/events.json","paper":"https://pith.science/paper/S5YC577E"},"agent_actions":{"view_html":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ","download_json":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ.json","view_paper":"https://pith.science/paper/S5YC577E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.03156&json=true","fetch_graph":"https://pith.science/api/pith-number/S5YC577EBDBKEC77QVFCBLC4UJ/graph.json","fetch_events":"https://pith.science/api/pith-number/S5YC577EBDBKEC77QVFCBLC4UJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ/action/storage_attestation","attest_author":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ/action/author_attestation","sign_citation":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ/action/citation_signature","submit_replication":"https://pith.science/pith/S5YC577EBDBKEC77QVFCBLC4UJ/action/replication_record"}},"created_at":"2026-05-18T00:17:54.179479+00:00","updated_at":"2026-05-18T00:17:54.179479+00:00"}