{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HRHELLBTKANKBTDIMWS7Y6EQTR","short_pith_number":"pith:HRHELLBT","schema_version":"1.0","canonical_sha256":"3c4e45ac33501aa0cc6865a5fc78909c4b9988a6f1e8df3636066217bb3290cb","source":{"kind":"arxiv","id":"2108.02904","version":1},"attestation_state":"computed","paper":{"title":"Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MA","econ.EM","econ.GN","q-fin.EC"],"primary_cat":"cs.LG","authors_text":"Alexander Trott, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng, Sunil Srinivasa","submitted_at":"2021-08-06T01:30:41Z","abstract_excerpt":"Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism design problem. A policy designer needs to consider multiple objectives, policy levers, and behavioral responses from strategic actors who optimize for their individual objectives. Moreover, real-world policies should be explainable and robust to simulation-to-reality gaps, e.g., due to calibration issues. Existing approaches are often limited to a narrow set of policy levers or objectives that are hard to measure, "},"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":"2108.02904","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-06T01:30:41Z","cross_cats_sorted":["cs.AI","cs.MA","econ.EM","econ.GN","q-fin.EC"],"title_canon_sha256":"bd7255c5bc59eeca837492a8692caf21d030997b641f4f02fa255ac7e6d17be8","abstract_canon_sha256":"056dc7f04730dd882ea4d46fe9864323f44ccc1f598923ffd50051a8e1122b60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:03:43.445778Z","signature_b64":"z1QuIELWy9WeT1H+a+l652y+DET+y6134WHpOLwSQuKaTI6K19i+8pMVf7lfHYicPr9UGmr+SABS2qaKTzjwAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c4e45ac33501aa0cc6865a5fc78909c4b9988a6f1e8df3636066217bb3290cb","last_reissued_at":"2026-07-05T03:03:43.445400Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:03:43.445400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MA","econ.EM","econ.GN","q-fin.EC"],"primary_cat":"cs.LG","authors_text":"Alexander Trott, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng, Sunil Srinivasa","submitted_at":"2021-08-06T01:30:41Z","abstract_excerpt":"Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism design problem. A policy designer needs to consider multiple objectives, policy levers, and behavioral responses from strategic actors who optimize for their individual objectives. Moreover, real-world policies should be explainable and robust to simulation-to-reality gaps, e.g., due to calibration issues. Existing approaches are often limited to a narrow set of policy levers or objectives that are hard to measure, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.02904","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.02904/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":"2108.02904","created_at":"2026-07-05T03:03:43.445454+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.02904v1","created_at":"2026-07-05T03:03:43.445454+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.02904","created_at":"2026-07-05T03:03:43.445454+00:00"},{"alias_kind":"pith_short_12","alias_value":"HRHELLBTKANK","created_at":"2026-07-05T03:03:43.445454+00:00"},{"alias_kind":"pith_short_16","alias_value":"HRHELLBTKANKBTDI","created_at":"2026-07-05T03:03:43.445454+00:00"},{"alias_kind":"pith_short_8","alias_value":"HRHELLBT","created_at":"2026-07-05T03:03:43.445454+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2201.03544","citing_title":"The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13762","citing_title":"EconAI: Dynamic Persona Evolution and Memory-Aware Agents in Evolving Economic Environments","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04741","citing_title":"Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04741","citing_title":"Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR","json":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR.json","graph_json":"https://pith.science/api/pith-number/HRHELLBTKANKBTDIMWS7Y6EQTR/graph.json","events_json":"https://pith.science/api/pith-number/HRHELLBTKANKBTDIMWS7Y6EQTR/events.json","paper":"https://pith.science/paper/HRHELLBT"},"agent_actions":{"view_html":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR","download_json":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR.json","view_paper":"https://pith.science/paper/HRHELLBT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.02904&json=true","fetch_graph":"https://pith.science/api/pith-number/HRHELLBTKANKBTDIMWS7Y6EQTR/graph.json","fetch_events":"https://pith.science/api/pith-number/HRHELLBTKANKBTDIMWS7Y6EQTR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR/action/storage_attestation","attest_author":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR/action/author_attestation","sign_citation":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR/action/citation_signature","submit_replication":"https://pith.science/pith/HRHELLBTKANKBTDIMWS7Y6EQTR/action/replication_record"}},"created_at":"2026-07-05T03:03:43.445454+00:00","updated_at":"2026-07-05T03:03:43.445454+00:00"}