{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XSEM4G6EHNFVGVYZMT7QS6U2ZG","short_pith_number":"pith:XSEM4G6E","schema_version":"1.0","canonical_sha256":"bc88ce1bc43b4b53571964ff097a9ac9a078ffb2b8f519d967221f0cab2cbd9c","source":{"kind":"arxiv","id":"2405.02835","version":2},"attestation_state":"computed","paper":{"title":"Algorithmic collusion in a two-sided market: A rideshare example","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.GT","authors_text":"Forrest Laine, Pravesh Koirala","submitted_at":"2024-05-05T07:23:26Z","abstract_excerpt":"With dynamic pricing on the rise, firms are using sophisticated algorithms for price determination. These algorithms are often non-interpretable and there has been a recent interest in their seemingly emergent ability to tacitly collude with each other without any prior communication whatsoever. Most of the previous works investigate algorithmic collusion on simple reinforcement learning (RL) based algorithms operating on a basic market model. Instead, we explore the collusive tendencies of Proximal Policy Optimization (PPO), a state-of-the-art continuous state/action space RL algorithm, on a "},"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":"2405.02835","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2024-05-05T07:23:26Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"20b6735e17f3a83ede126e97def03b76860c56eb0838fb57e02db3b79bb1a78f","abstract_canon_sha256":"3d945d3cc330a036fcffb89b16a231c947baaf360018cc05555d575e1debc44f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:25:19.859760Z","signature_b64":"2/5M3rGhXAgmhS0YmvUu4e/XtBBMTJLTJEsyOgc+uvUF164xjxuQLKO7ArXzhjX+h6c++m4U38yDkjaT54EbAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc88ce1bc43b4b53571964ff097a9ac9a078ffb2b8f519d967221f0cab2cbd9c","last_reissued_at":"2026-07-05T09:25:19.859282Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:25:19.859282Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Algorithmic collusion in a two-sided market: A rideshare example","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.GT","authors_text":"Forrest Laine, Pravesh Koirala","submitted_at":"2024-05-05T07:23:26Z","abstract_excerpt":"With dynamic pricing on the rise, firms are using sophisticated algorithms for price determination. These algorithms are often non-interpretable and there has been a recent interest in their seemingly emergent ability to tacitly collude with each other without any prior communication whatsoever. Most of the previous works investigate algorithmic collusion on simple reinforcement learning (RL) based algorithms operating on a basic market model. Instead, we explore the collusive tendencies of Proximal Policy Optimization (PPO), a state-of-the-art continuous state/action space RL algorithm, on a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.02835","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/2405.02835/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":"2405.02835","created_at":"2026-07-05T09:25:19.859340+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.02835v2","created_at":"2026-07-05T09:25:19.859340+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.02835","created_at":"2026-07-05T09:25:19.859340+00:00"},{"alias_kind":"pith_short_12","alias_value":"XSEM4G6EHNFV","created_at":"2026-07-05T09:25:19.859340+00:00"},{"alias_kind":"pith_short_16","alias_value":"XSEM4G6EHNFVGVYZ","created_at":"2026-07-05T09:25:19.859340+00:00"},{"alias_kind":"pith_short_8","alias_value":"XSEM4G6E","created_at":"2026-07-05T09:25:19.859340+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.13432","citing_title":"MaMe & MaRe: Matrix-Based Token Merging and Restoration for Efficient Visual Perception and Synthesis","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG","json":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG.json","graph_json":"https://pith.science/api/pith-number/XSEM4G6EHNFVGVYZMT7QS6U2ZG/graph.json","events_json":"https://pith.science/api/pith-number/XSEM4G6EHNFVGVYZMT7QS6U2ZG/events.json","paper":"https://pith.science/paper/XSEM4G6E"},"agent_actions":{"view_html":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG","download_json":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG.json","view_paper":"https://pith.science/paper/XSEM4G6E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.02835&json=true","fetch_graph":"https://pith.science/api/pith-number/XSEM4G6EHNFVGVYZMT7QS6U2ZG/graph.json","fetch_events":"https://pith.science/api/pith-number/XSEM4G6EHNFVGVYZMT7QS6U2ZG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG/action/storage_attestation","attest_author":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG/action/author_attestation","sign_citation":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG/action/citation_signature","submit_replication":"https://pith.science/pith/XSEM4G6EHNFVGVYZMT7QS6U2ZG/action/replication_record"}},"created_at":"2026-07-05T09:25:19.859340+00:00","updated_at":"2026-07-05T09:25:19.859340+00:00"}