{"id":"W2241295572","doi":"10.1177/1059712315590484","title":"Matching without learning","year":2015,"lang":"en","type":"article","venue":"Adaptive Behavior","topic":"Behavioral and Psychological Studies","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Matching law; Reinforcement learning; Reinforcement; Matching (statistics); Computer science; Variance (accounting); Monte Carlo method; Dependency (UML); Process (computing); Artificial intelligence; Function (biology); Machine learning; Statistics; Mathematics; Psychology; Social psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001181436,0.0003340827,0.0006975862,0.0004081896,0.0004918241,0.0009569816,0.001088056,0.0008901097,0.008599028],"category_scores_gemma":[0.01050472,0.0002212282,0.0007577759,0.0004501165,0.001350799,0.001832994,0.0009662598,0.0008112434,0.00122792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008437125,"about_ca_system_score_gemma":0.0009518321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001686714,"about_ca_topic_score_gemma":0.001065362,"domain_scores_codex":[0.9985934,0.0002734125,0.00006156156,0.00055505,0.000335572,0.0001810515],"domain_scores_gemma":[0.9970028,0.0008274745,0.0005020753,0.001196557,0.0002594583,0.000211737],"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":[0.0006765209,0.000527158,0.01989987,0.0004221701,0.0002993164,0.0006216399,0.0004095616,0.15443,0.03958942,0.5490599,0.004090454,0.229974],"study_design_scores_gemma":[0.0001139094,0.0007634387,0.01458926,0.00003373506,0.00009233975,0.0009309252,0.0001108762,0.4947279,0.01508976,0.4609787,0.01249323,0.00007581506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5042102,0.0003272486,0.4408489,0.0005920018,0.0002221681,0.000353324,0.0004925592,0.0008077442,0.05214582],"genre_scores_gemma":[0.9582694,0.00009308986,0.02941223,0.0002085352,0.00003897408,0.0001268329,0.0001900947,0.00008200459,0.0115788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008599028,"threshold_uncertainty_score":0.02876657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3963156135312348,"score_gpt":0.3972464181947243,"score_spread":0.0009308046634894929,"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."}}