{"id":"W3176913318","doi":"10.1609/aaai.v35i6.16680","title":"Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Regret; Common value auction; Bidding; Nash equilibrium; Best response; Mathematical economics; Convergence (economics); Equilibrium selection; Computer science; Mathematical optimization; Sequence (biology); Iterated function; Mathematics; Repeated game; Economics; Game theory; Machine learning; Microeconomics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001735815,0.0001607589,0.0004714982,0.0005718183,0.0001833161,0.0001125335,0.001139821,0.00009672936,0.002186403],"category_scores_gemma":[0.00451543,0.0001198115,0.0002835252,0.00763499,0.000466158,0.0002654683,0.0002153648,0.0002549244,0.0001766624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003885532,"about_ca_system_score_gemma":0.0001308084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007540927,"about_ca_topic_score_gemma":0.00008417846,"domain_scores_codex":[0.9969813,0.00006463708,0.00127278,0.0006018341,0.0008507266,0.0002287337],"domain_scores_gemma":[0.995463,0.0004794421,0.000796367,0.0004827231,0.002702248,0.00007621455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008628195,0.0003438338,0.006983592,0.00001215939,0.0001410947,8.438621e-7,0.001494817,0.002561555,0.1100069,0.8440984,0.0002237019,0.03404691],"study_design_scores_gemma":[0.00002887859,0.00005217572,0.004622906,0.00009465596,0.0001344787,0.000003205631,0.005477208,0.1159098,0.638669,0.2344689,0.0003434638,0.0001953509],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9564962,0.00003561554,0.01842654,0.003058811,0.0006799619,0.0003669514,0.00007757809,0.00004890831,0.02080939],"genre_scores_gemma":[0.9972218,0.00004088932,0.000825391,0.00006657062,0.00003296178,0.00002345274,0.000002332601,0.000006769156,0.001779799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6096295,"threshold_uncertainty_score":0.9987257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2145083290659143,"score_gpt":0.4085916857618047,"score_spread":0.1940833566958904,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}