{"id":"W1874925508","doi":"10.48550/arxiv.1206.5255","title":"Minimax regret based elicitation of generalized additive utilities","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Regret; Preference elicitation; Minimax; Computer science; Product (mathematics); Preference; Mathematical optimization; Artificial intelligence; Theoretical computer science; Mathematics; Machine learning; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002538362,0.0004566109,0.0009259629,0.001361352,0.0001586991,0.0001691658,0.001970018,0.0004865051,0.003433462],"category_scores_gemma":[0.003232938,0.0004419844,0.0005865482,0.001204138,0.0004124021,0.0005218491,0.001186382,0.000461111,0.0003360896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000202962,"about_ca_system_score_gemma":0.0003308482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002618029,"about_ca_topic_score_gemma":0.00007807706,"domain_scores_codex":[0.9952752,0.001075165,0.0009943113,0.001380434,0.0008037693,0.0004711091],"domain_scores_gemma":[0.9914144,0.003405999,0.001440954,0.001983757,0.001504379,0.0002504581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007470373,0.00227452,0.06914976,0.0006540817,0.001234739,0.0009513781,0.01554025,0.3893383,0.006305298,0.3361353,0.07487145,0.09607449],"study_design_scores_gemma":[0.003510467,0.0001286027,0.03091137,0.000526206,0.0003688305,0.000005085169,0.004678797,0.7480828,0.004008484,0.1852128,0.02098084,0.001585723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8568286,0.0001234931,0.1329614,0.00007715406,0.001405153,0.0005139733,0.0007455686,0.0000939229,0.007250773],"genre_scores_gemma":[0.9909248,0.00004814957,0.005515437,0.0001265961,0.0001427217,0.000002507386,0.00009701637,0.00003356296,0.003109191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3587444,"threshold_uncertainty_score":0.9998032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4219303607012453,"score_gpt":0.3236458706056908,"score_spread":0.09828449009555446,"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."}}