{"id":"W2772399711","doi":"10.1002/bdm.2070","title":"Maximizing Scales Do Not Reliably Predict Maximizing Behavior in Decisions from Experience","year":2017,"lang":"en","type":"article","venue":"Journal of Behavioral Decision Making","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Foundation of Sri Lanka; National Science Foundation","keywords":"Maximization; Stochastic game; Utility maximization; Sampling (signal processing); Psychology; Cognitive psychology; Computer science; Econometrics; Social psychology; Microeconomics; Economics; Mathematical economics","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":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.00679457,0.0007055794,0.001865354,0.00216089,0.001412508,0.005093642,0.007011964,0.0005605382,0.0008351784],"category_scores_gemma":[0.01268012,0.0005566853,0.001095833,0.0007464067,0.0004982959,0.004417356,0.001830212,0.001474321,0.0002650776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005047213,"about_ca_system_score_gemma":0.0002864812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000209494,"about_ca_topic_score_gemma":0.0004181807,"domain_scores_codex":[0.9871762,0.0003285318,0.005482286,0.001428752,0.004609837,0.0009744418],"domain_scores_gemma":[0.9837966,0.00535998,0.005394894,0.00346779,0.001382253,0.000598483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007426606,0.0004445481,0.1968724,7.151162e-7,0.000007161875,0.001643072,0.0007321888,0.0004022816,0.001937158,0.0000264302,0.0007871262,0.7964043],"study_design_scores_gemma":[0.003464183,0.0006373414,0.9123949,0.002427193,0.0002050202,0.0006899352,0.00505195,0.00142919,0.0008376503,0.06753544,0.004163835,0.001163375],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866331,0.0003798744,0.00679341,0.0001911637,0.005163692,0.0003609624,0.0001044992,0.00004066863,0.0003326544],"genre_scores_gemma":[0.9149145,0.0001505649,0.08424159,0.0001219341,0.0004078764,0.00002159108,0.000001815034,0.00008048302,0.00005968042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7952409,"threshold_uncertainty_score":0.9998875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1993645323040618,"score_gpt":0.4524464993842472,"score_spread":0.2530819670801854,"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."}}