{"id":"W2936326179","doi":"10.3819/ccbr.2019.140006","title":"The Case for a Heuristic Approach to Account for Suboptimal Choice","year":2019,"lang":"en","type":"article","venue":"Comparative Cognition & Behavior Reviews","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Margin (machine learning); Space (punctuation); Heuristic; Line (geometry); Psychology; White (mutation); Selection (genetic algorithm); Font; Arithmetic; Statistics; Mathematics; Artificial intelligence; Computer science; Machine learning; Biology; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003852383,0.000332816,0.0009243854,0.0001719792,0.0006713523,0.0008318565,0.0008373649,0.00009005115,0.0001463621],"category_scores_gemma":[0.002211402,0.000205356,0.0005197551,0.000502452,0.0001066504,0.0003565329,0.0001344845,0.0001725144,0.002116827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009147082,"about_ca_system_score_gemma":0.0000727245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008843438,"about_ca_topic_score_gemma":0.00006760387,"domain_scores_codex":[0.9965598,0.0002633942,0.001440867,0.0009227902,0.0003796457,0.000433504],"domain_scores_gemma":[0.9928722,0.004176421,0.0006643352,0.000884285,0.001193115,0.0002095864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003403201,0.0007339772,0.0009016048,0.00004239836,0.00001832187,0.00001095111,0.0006328849,0.00009212695,0.0005343889,0.0011639,0.1024541,0.893075],"study_design_scores_gemma":[0.001140622,0.0004827354,0.001350837,0.00008637618,0.0002644444,0.0001878496,0.0011323,0.003067983,0.0001263712,0.003862663,0.9877899,0.0005078915],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9365296,0.001675947,0.04555037,0.0001883676,0.001280398,0.01197549,0.0005194213,0.00004928177,0.002231161],"genre_scores_gemma":[0.9780433,0.00006987349,0.01257533,0.0004923261,0.000204069,0.00673521,0.00009406251,0.00003234932,0.001753496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8925672,"threshold_uncertainty_score":0.9986601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4844919538919981,"score_gpt":0.509163919179003,"score_spread":0.02467196528700494,"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."}}