{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FLRN2ELN7HXIBDKD7ZUBRG7TEE","short_pith_number":"pith:FLRN2ELN","schema_version":"1.0","canonical_sha256":"2ae2dd116df9ee808d43fe68189bf3210fd5d623c9334ab855c36cc73ab34337","source":{"kind":"arxiv","id":"2302.03071","version":1},"attestation_state":"computed","paper":{"title":"Optimally Interpolating between Ex-Ante Fairness and Welfare","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.GT","authors_text":"Chris Schwiegelshohn, Mikael M{\\o}ller H{\\o}gsgaard, Nidhi Rathi, Panagiotis Karras, Wenyue Ma","submitted_at":"2023-02-06T19:18:37Z","abstract_excerpt":"For the fundamental problem of allocating a set of resources among individuals with varied preferences, the quality of an allocation relates to the degree of fairness and the collective welfare achieved. Unfortunately, in many resource-allocation settings, it is computationally hard to maximize welfare while achieving fairness goals.\n  In this work, we consider ex-ante notions of fairness; popular examples include the \\emph{randomized round-robin algorithm} and \\emph{sortition mechanism}. We propose a general framework to systematically study the \\emph{interpolation} between fairness and welfa"},"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":"2302.03071","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GT","submitted_at":"2023-02-06T19:18:37Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"e7b1ddd7724fa36dbe11e2bcc703caea251e86d4ed9ee0d695314c47d29b3f7b","abstract_canon_sha256":"b45eb1430731e6551a93adf211aae488781928cb20def69c93487ed7bbb54683"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:34.448323Z","signature_b64":"kJ0dHYlofQubjLEiFGwqc7XLj5VanKwkwOsva5qEu22bsjB/tj7iqKfBM0fqmQSejFlREqcMQsd4vxon4ISuAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ae2dd116df9ee808d43fe68189bf3210fd5d623c9334ab855c36cc73ab34337","last_reissued_at":"2026-07-05T09:00:34.447810Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:34.447810Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimally Interpolating between Ex-Ante Fairness and Welfare","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.GT","authors_text":"Chris Schwiegelshohn, Mikael M{\\o}ller H{\\o}gsgaard, Nidhi Rathi, Panagiotis Karras, Wenyue Ma","submitted_at":"2023-02-06T19:18:37Z","abstract_excerpt":"For the fundamental problem of allocating a set of resources among individuals with varied preferences, the quality of an allocation relates to the degree of fairness and the collective welfare achieved. Unfortunately, in many resource-allocation settings, it is computationally hard to maximize welfare while achieving fairness goals.\n  In this work, we consider ex-ante notions of fairness; popular examples include the \\emph{randomized round-robin algorithm} and \\emph{sortition mechanism}. We propose a general framework to systematically study the \\emph{interpolation} between fairness and welfa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03071","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/2302.03071/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":"2302.03071","created_at":"2026-07-05T09:00:34.447872+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.03071v1","created_at":"2026-07-05T09:00:34.447872+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03071","created_at":"2026-07-05T09:00:34.447872+00:00"},{"alias_kind":"pith_short_12","alias_value":"FLRN2ELN7HXI","created_at":"2026-07-05T09:00:34.447872+00:00"},{"alias_kind":"pith_short_16","alias_value":"FLRN2ELN7HXIBDKD","created_at":"2026-07-05T09:00:34.447872+00:00"},{"alias_kind":"pith_short_8","alias_value":"FLRN2ELN","created_at":"2026-07-05T09:00:34.447872+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.03953","citing_title":"Fairness Aware Reinforcement Learning via Proximal Policy Optimization","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE","json":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE.json","graph_json":"https://pith.science/api/pith-number/FLRN2ELN7HXIBDKD7ZUBRG7TEE/graph.json","events_json":"https://pith.science/api/pith-number/FLRN2ELN7HXIBDKD7ZUBRG7TEE/events.json","paper":"https://pith.science/paper/FLRN2ELN"},"agent_actions":{"view_html":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE","download_json":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE.json","view_paper":"https://pith.science/paper/FLRN2ELN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.03071&json=true","fetch_graph":"https://pith.science/api/pith-number/FLRN2ELN7HXIBDKD7ZUBRG7TEE/graph.json","fetch_events":"https://pith.science/api/pith-number/FLRN2ELN7HXIBDKD7ZUBRG7TEE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE/action/storage_attestation","attest_author":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE/action/author_attestation","sign_citation":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE/action/citation_signature","submit_replication":"https://pith.science/pith/FLRN2ELN7HXIBDKD7ZUBRG7TEE/action/replication_record"}},"created_at":"2026-07-05T09:00:34.447872+00:00","updated_at":"2026-07-05T09:00:34.447872+00:00"}