{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DTZXYVJ63ML6VBK7OZL7W6JUND","short_pith_number":"pith:DTZXYVJ6","schema_version":"1.0","canonical_sha256":"1cf37c553edb17ea855f7657fb793468ec121551e2491f8388c7823978eb23f8","source":{"kind":"arxiv","id":"2302.03719","version":2},"attestation_state":"computed","paper":{"title":"Persuading a Behavioral Agent: Approximately Best Responding and Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","econ.TH"],"primary_cat":"cs.GT","authors_text":"Tao Lin, Yiling Chen","submitted_at":"2023-02-07T19:12:46Z","abstract_excerpt":"The classic Bayesian persuasion model assumes a Bayesian and best-responding receiver. We study a relaxation of the Bayesian persuasion model where the receiver can approximately best respond to the sender's signaling scheme. We show that, under natural assumptions, (1) the sender can find a signaling scheme that guarantees itself an expected utility almost as good as its optimal utility in the classic model, no matter what approximately best-responding strategy the receiver uses; (2) on the other hand, there is no signaling scheme that gives the sender much more utility than its optimal utili"},"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":"2302.03719","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2023-02-07T19:12:46Z","cross_cats_sorted":["cs.LG","econ.TH"],"title_canon_sha256":"e872c729c16d5638c8a1ef1addf79d2bb72d486ea915e638b65d6c725a2cd6bc","abstract_canon_sha256":"7b2ed496e9b7b89220fcd3eb9b72af3e667e0145bde58a83edad1bbc2945adf2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:55.175911Z","signature_b64":"jrPlwtnSfKwcRNDpm3mJhmY3NJyUy0qEaXmJh8i7qTZoQlrrIN8UjVplplmiD/6FarC/bim2v6UBDvRa2ghLAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cf37c553edb17ea855f7657fb793468ec121551e2491f8388c7823978eb23f8","last_reissued_at":"2026-07-05T07:47:55.175511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:55.175511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Persuading a Behavioral Agent: Approximately Best Responding and Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","econ.TH"],"primary_cat":"cs.GT","authors_text":"Tao Lin, Yiling Chen","submitted_at":"2023-02-07T19:12:46Z","abstract_excerpt":"The classic Bayesian persuasion model assumes a Bayesian and best-responding receiver. We study a relaxation of the Bayesian persuasion model where the receiver can approximately best respond to the sender's signaling scheme. We show that, under natural assumptions, (1) the sender can find a signaling scheme that guarantees itself an expected utility almost as good as its optimal utility in the classic model, no matter what approximately best-responding strategy the receiver uses; (2) on the other hand, there is no signaling scheme that gives the sender much more utility than its optimal utili"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03719","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.03719/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":"2302.03719","created_at":"2026-07-05T07:47:55.175566+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.03719v2","created_at":"2026-07-05T07:47:55.175566+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03719","created_at":"2026-07-05T07:47:55.175566+00:00"},{"alias_kind":"pith_short_12","alias_value":"DTZXYVJ63ML6","created_at":"2026-07-05T07:47:55.175566+00:00"},{"alias_kind":"pith_short_16","alias_value":"DTZXYVJ63ML6VBK7","created_at":"2026-07-05T07:47:55.175566+00:00"},{"alias_kind":"pith_short_8","alias_value":"DTZXYVJ6","created_at":"2026-07-05T07:47:55.175566+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.18297","citing_title":"Learning to Play Against Unknown Opponents","ref_index":2006,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND","json":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND.json","graph_json":"https://pith.science/api/pith-number/DTZXYVJ63ML6VBK7OZL7W6JUND/graph.json","events_json":"https://pith.science/api/pith-number/DTZXYVJ63ML6VBK7OZL7W6JUND/events.json","paper":"https://pith.science/paper/DTZXYVJ6"},"agent_actions":{"view_html":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND","download_json":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND.json","view_paper":"https://pith.science/paper/DTZXYVJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.03719&json=true","fetch_graph":"https://pith.science/api/pith-number/DTZXYVJ63ML6VBK7OZL7W6JUND/graph.json","fetch_events":"https://pith.science/api/pith-number/DTZXYVJ63ML6VBK7OZL7W6JUND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND/action/storage_attestation","attest_author":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND/action/author_attestation","sign_citation":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND/action/citation_signature","submit_replication":"https://pith.science/pith/DTZXYVJ63ML6VBK7OZL7W6JUND/action/replication_record"}},"created_at":"2026-07-05T07:47:55.175566+00:00","updated_at":"2026-07-05T07:47:55.175566+00:00"}