{"id":"W2058693307","doi":"10.1016/j.omega.2004.07.007","title":"Matrix games with fuzzy goals and fuzzy payoffs","year":2004,"lang":"en","type":"article","venue":"Omega","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":150,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Defuzzification; Fuzzy logic; Fuzzy associative matrix; Fuzzy number; Mathematics; Mathematical optimization; Fuzzy set operations; Fuzzy classification; Matrix (chemical analysis); Fuzzy mathematics; Dual (grammatical number); Computer science; Fuzzy set; Artificial intelligence","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":[0.001663621,0.0002378212,0.0004253482,0.000408768,0.0001954311,0.0008268965,0.0006860136,0.0001021836,0.000276973],"category_scores_gemma":[0.001324537,0.0001496856,0.00007758577,0.0008925805,0.0001910843,0.0005450801,0.0002615557,0.0001654303,0.0008017487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004817358,"about_ca_system_score_gemma":0.00009138987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000481302,"about_ca_topic_score_gemma":0.0000760686,"domain_scores_codex":[0.9964756,0.0001138839,0.0006163956,0.0007602162,0.001627551,0.0004063965],"domain_scores_gemma":[0.9975401,0.0008327597,0.0002348433,0.0009057721,0.0002492464,0.0002373091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001257625,0.0006606068,0.1405084,0.00006741867,0.0001791348,0.001381125,0.008514835,0.00332726,0.03184831,0.07715429,0.04381738,0.6912836],"study_design_scores_gemma":[0.007298762,0.0005864692,0.1946813,0.0002983259,0.00005557916,0.0005449395,0.002883243,0.0003662422,0.002413175,0.4157561,0.3737357,0.001380098],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753516,0.0007194921,0.003664746,0.001794043,0.0003741459,0.0002636405,0.00002251151,0.0001015924,0.01770817],"genre_scores_gemma":[0.9734218,0.00002720765,0.02157487,0.0004194652,0.0001303094,0.00001281696,0.000001526174,0.00002650681,0.004385517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6899035,"threshold_uncertainty_score":0.9999762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07725751484461374,"score_gpt":0.3920464369452687,"score_spread":0.314788922100655,"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."}}