{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VIZB6NXX5AC3DPILTY5CFUOT7T","short_pith_number":"pith:VIZB6NXX","schema_version":"1.0","canonical_sha256":"aa321f36f7e805b1bd0b9e3a22d1d3fcf791344be410b417da7403142436789b","source":{"kind":"arxiv","id":"2403.12093","version":4},"attestation_state":"computed","paper":{"title":"Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"econ.TH","authors_text":"Chengdong Ma, Haifeng Zhang, Jun Wang, Mengyue Yang, Qirui Mi, Siyu Xia, Yan Song, Zhiyu Zhao","submitted_at":"2024-03-14T13:22:31Z","abstract_excerpt":"Macroeconomic outcomes emerge from individuals' decisions, making it essential to model how agents interact with macro policy via consumption, investment, and labor choices. We formulate this as a dynamic Stackelberg game: the government (leader) sets policies, and agents (followers) respond by optimizing their behavior over time. Unlike static models, this dynamic formulation captures temporal dependencies and strategic feedback critical to policy design. However, as the number of agents increases, explicitly simulating all agent-agent and agent-government interactions becomes computationally"},"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":"2403.12093","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.TH","submitted_at":"2024-03-14T13:22:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ae4bd2573510dd31581170456a3ad5b418245fdf059b5102edcf4d58aa1843c9","abstract_canon_sha256":"4e6afc47bb5e066ed9893503acd257bdb8aafad1acd3d22763a6ed2f59436445"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:08.266252Z","signature_b64":"XVkf80IlCNaruW0RGCSWerMWAt7r0joVpUgJaR1+KNQ+vAxpXfNm6GaIAmJBlVwtmA9ucyKzZqPcr2el64KeCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa321f36f7e805b1bd0b9e3a22d1d3fcf791344be410b417da7403142436789b","last_reissued_at":"2026-07-05T11:13:08.265662Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:08.265662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"econ.TH","authors_text":"Chengdong Ma, Haifeng Zhang, Jun Wang, Mengyue Yang, Qirui Mi, Siyu Xia, Yan Song, Zhiyu Zhao","submitted_at":"2024-03-14T13:22:31Z","abstract_excerpt":"Macroeconomic outcomes emerge from individuals' decisions, making it essential to model how agents interact with macro policy via consumption, investment, and labor choices. We formulate this as a dynamic Stackelberg game: the government (leader) sets policies, and agents (followers) respond by optimizing their behavior over time. Unlike static models, this dynamic formulation captures temporal dependencies and strategic feedback critical to policy design. However, as the number of agents increases, explicitly simulating all agent-agent and agent-government interactions becomes computationally"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12093","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.12093/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":"2403.12093","created_at":"2026-07-05T11:13:08.265734+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.12093v4","created_at":"2026-07-05T11:13:08.265734+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12093","created_at":"2026-07-05T11:13:08.265734+00:00"},{"alias_kind":"pith_short_12","alias_value":"VIZB6NXX5AC3","created_at":"2026-07-05T11:13:08.265734+00:00"},{"alias_kind":"pith_short_16","alias_value":"VIZB6NXX5AC3DPIL","created_at":"2026-07-05T11:13:08.265734+00:00"},{"alias_kind":"pith_short_8","alias_value":"VIZB6NXX","created_at":"2026-07-05T11:13:08.265734+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.04741","citing_title":"Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04741","citing_title":"Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T","json":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T.json","graph_json":"https://pith.science/api/pith-number/VIZB6NXX5AC3DPILTY5CFUOT7T/graph.json","events_json":"https://pith.science/api/pith-number/VIZB6NXX5AC3DPILTY5CFUOT7T/events.json","paper":"https://pith.science/paper/VIZB6NXX"},"agent_actions":{"view_html":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T","download_json":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T.json","view_paper":"https://pith.science/paper/VIZB6NXX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.12093&json=true","fetch_graph":"https://pith.science/api/pith-number/VIZB6NXX5AC3DPILTY5CFUOT7T/graph.json","fetch_events":"https://pith.science/api/pith-number/VIZB6NXX5AC3DPILTY5CFUOT7T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T/action/storage_attestation","attest_author":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T/action/author_attestation","sign_citation":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T/action/citation_signature","submit_replication":"https://pith.science/pith/VIZB6NXX5AC3DPILTY5CFUOT7T/action/replication_record"}},"created_at":"2026-07-05T11:13:08.265734+00:00","updated_at":"2026-07-05T11:13:08.265734+00:00"}