{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:WWC6JD6GLL6JEXLQGGGRRWP756","short_pith_number":"pith:WWC6JD6G","schema_version":"1.0","canonical_sha256":"b585e48fc65afc925d70318d18d9ffef984f3c0c691eceaac75b9739056c9a21","source":{"kind":"arxiv","id":"2110.11404","version":1},"attestation_state":"computed","paper":{"title":"Statistical discrimination in learning agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.GT","cs.MA"],"primary_cat":"cs.LG","authors_text":"Alexander Sasha Vezhnevets, Ben Coppin, Edgar A. Du\\'e\\~nez-Guzm\\'an, Joel Z. Leibo, Karl Tuyls, Kevin R. McKee, Michiel A. Bakker, Silvia Chiappa, Suzanne Sadedin, William Isaac, Yiran Mao, Yoram Bachrach","submitted_at":"2021-10-21T18:28:57Z","abstract_excerpt":"Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One primary example is \\textit{statistical discrimination} -- selecting social partners based not on their underlying attributes, but on readily perceptible characteristics that covary with their suitability for the task at hand. We present a theoretical model to examine how information processing influences statistical discrimination and test its predictions using multi-agent reinforcement learning with various agent arch"},"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":"2110.11404","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T18:28:57Z","cross_cats_sorted":["cs.AI","cs.GT","cs.MA"],"title_canon_sha256":"e95522fcc5bf2e7855f0bf63162eea2658d52704a61a141cb7674e9867a78577","abstract_canon_sha256":"3d2baea197b82d19b1c6043dd1cc14d16ae45dbe1ab873490a72a1842c7af211"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:24:46.714798Z","signature_b64":"Vlr1Q7BO7qJiAV45Yn/4mXunRnZhzVMoXyLnpGMy0UY5LZtFs2kO6abINC9aO51Qnn0y3QkMXP7ww4sns55pAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b585e48fc65afc925d70318d18d9ffef984f3c0c691eceaac75b9739056c9a21","last_reissued_at":"2026-07-05T03:24:46.714291Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:24:46.714291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Statistical discrimination in learning agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.GT","cs.MA"],"primary_cat":"cs.LG","authors_text":"Alexander Sasha Vezhnevets, Ben Coppin, Edgar A. Du\\'e\\~nez-Guzm\\'an, Joel Z. Leibo, Karl Tuyls, Kevin R. McKee, Michiel A. Bakker, Silvia Chiappa, Suzanne Sadedin, William Isaac, Yiran Mao, Yoram Bachrach","submitted_at":"2021-10-21T18:28:57Z","abstract_excerpt":"Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One primary example is \\textit{statistical discrimination} -- selecting social partners based not on their underlying attributes, but on readily perceptible characteristics that covary with their suitability for the task at hand. We present a theoretical model to examine how information processing influences statistical discrimination and test its predictions using multi-agent reinforcement learning with various agent arch"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.11404","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/2110.11404/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":"2110.11404","created_at":"2026-07-05T03:24:46.714364+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.11404v1","created_at":"2026-07-05T03:24:46.714364+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.11404","created_at":"2026-07-05T03:24:46.714364+00:00"},{"alias_kind":"pith_short_12","alias_value":"WWC6JD6GLL6J","created_at":"2026-07-05T03:24:46.714364+00:00"},{"alias_kind":"pith_short_16","alias_value":"WWC6JD6GLL6JEXLQ","created_at":"2026-07-05T03:24:46.714364+00:00"},{"alias_kind":"pith_short_8","alias_value":"WWC6JD6G","created_at":"2026-07-05T03:24:46.714364+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.19010","citing_title":"A theory of appropriateness with applications to generative artificial intelligence","ref_index":2019,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756","json":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756.json","graph_json":"https://pith.science/api/pith-number/WWC6JD6GLL6JEXLQGGGRRWP756/graph.json","events_json":"https://pith.science/api/pith-number/WWC6JD6GLL6JEXLQGGGRRWP756/events.json","paper":"https://pith.science/paper/WWC6JD6G"},"agent_actions":{"view_html":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756","download_json":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756.json","view_paper":"https://pith.science/paper/WWC6JD6G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.11404&json=true","fetch_graph":"https://pith.science/api/pith-number/WWC6JD6GLL6JEXLQGGGRRWP756/graph.json","fetch_events":"https://pith.science/api/pith-number/WWC6JD6GLL6JEXLQGGGRRWP756/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756/action/storage_attestation","attest_author":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756/action/author_attestation","sign_citation":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756/action/citation_signature","submit_replication":"https://pith.science/pith/WWC6JD6GLL6JEXLQGGGRRWP756/action/replication_record"}},"created_at":"2026-07-05T03:24:46.714364+00:00","updated_at":"2026-07-05T03:24:46.714364+00:00"}