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It is not a universal Nash-convergence theorem for arbitrary multiplayer/general-sum learning or unrestricted imperfect-recall abstractions. Blackwell approachability/regret matching are inherited. Source theorem statements read, proofs not independently replayed.", "domain": "Imperfect-information multiagent learning" }, "sources": [ { "citation": "Regret Minimization in Games with Incomplete Information", "url": "https://proceedings.neurips.cc/paper/2007/file/08d98638c6fcd194a4b1e6992063e944-Paper.pdf" } ], "statement": "# Counterfactual regret minimization in extensive-form games\n\nDecomposed overall strategic regret into information-set counterfactual regrets, enabling locally maintained regret-matching updates and approximate-equilibrium computation in large imperfect-information games.\n\n## Scientific scope\n\nFinite perfect-recall extensive games underpin the stated regret decomposition; average self-play strategies yield the equilibrium guarantee in the two-player zero-sum setting. It is not a universal Nash-convergence theorem for arbitrary multiplayer/general-sum learning or unrestricted imperfect-recall abstractions. Blackwell approachability/regret matching are inherited. Source theorem statements read, proofs not independently replayed.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMartin Zinkevich; Michael Johanson; Michael Bowling; Carmelo Piccione\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Regret Minimization in Games with Incomplete Information](https://proceedings.neurips.cc/paper/2007/file/08d98638c6fcd194a4b1e6992063e944-Paper.pdf)\n", "summary": "Decomposed overall strategic regret into information-set counterfactual regrets, enabling locally maintained regret-matching updates and approximate-equilibrium computation in large imperfect-information games.", "title": "Counterfactual regret minimization in extensive-form games" }, "record_id": "urn:uuid:2b57f2da-d92b-41cc-a43f-975afeb27973", "version": 1, "source_ref": { "record_id": "urn:uuid:2b57f2da-d92b-41cc-a43f-975afeb27973", "version": 1, "content_hash": "sha256:4b1566b29f65b1044e1897f144b11acaf2ef83fb2891a2e326ad86946a951670" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:54497a82be28bd42a799412425ccbcc285c4b617c3c330a576eda5b778c10738" }, "target": { "record_id": "urn:uuid:2b57f2da-d92b-41cc-a43f-975afeb27973", "version": 1, "content_hash": "sha256:4b1566b29f65b1044e1897f144b11acaf2ef83fb2891a2e326ad86946a951670" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "zinkevich-et-al-counterfactual-regret-minimization-2007", "entry_id": "zinkevich-et-al-counterfactual-regret-minimization-2007", "revision": 1, "title": "Counterfactual regret minimization in extensive-form games", "context": "Imperfect-information multiagent learning", "track": "Human-designed research method or resource", "band": "A", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Decomposed overall strategic regret into information-set counterfactual regrets, enabling locally maintained regret-matching updates and approximate-equilibrium computation in large imperfect-information games.", "scope": "Decomposed overall strategic regret into information-set counterfactual regrets, enabling locally maintained regret-matching updates and approximate-equilibrium computation in large imperfect-information games. Finite perfect-recall extensive games underpin the stated regret decomposition; average self-play strategies yield the equilibrium guarantee in the two-player zero-sum setting. It is not a universal Nash-convergence theorem for arbitrary multiplayer/general-sum learning or unrestricted imperfect-recall abstractions. Blackwell approachability/regret matching are inherited. Source theorem statements read, proofs not independently replayed.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Finite perfect-recall extensive games underpin the stated regret decomposition; average self-play strategies yield the equilibrium guarantee in the two-player zero-sum setting. It is not a universal Nash-convergence theorem for arbitrary multiplayer/general-sum learning or unrestricted imperfect-recall abstractions. Blackwell approachability/regret matching are inherited. Source theorem statements read, proofs not independently replayed.", "references": [ { "title": "Regret Minimization in Games with Incomplete Information", "url": "https://proceedings.neurips.cc/paper/2007/file/08d98638c6fcd194a4b1e6992063e944-Paper.pdf", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities." } ], "target": { "record_id": "urn:uuid:2b57f2da-d92b-41cc-a43f-975afeb27973", "version": 1, "content_hash": "sha256:4b1566b29f65b1044e1897f144b11acaf2ef83fb2891a2e326ad86946a951670" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "220000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }