{"id":"W2962856402","doi":"10.1093/ej/uez043","title":"Learning While Experimenting","year":2019,"lang":"en","type":"article","venue":"The Economic Journal","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Pessimism; State (computer science); Computer science; Epistemology","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":[0.003644891,0.0007461118,0.0008164825,0.0003121991,0.0005456172,0.001397648,0.001082237,0.001364663,0.009324278],"category_scores_gemma":[0.02085857,0.0003184355,0.0004530915,0.0003227013,0.001802807,0.002456541,0.001292512,0.001784443,0.001261503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009426955,"about_ca_system_score_gemma":0.0009164381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001154116,"about_ca_topic_score_gemma":0.001341276,"domain_scores_codex":[0.9980343,0.0009776907,0.0001004383,0.000432703,0.0002280459,0.0002268436],"domain_scores_gemma":[0.9827137,0.01265571,0.001210444,0.001890409,0.0006412792,0.0008885001],"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":[0.003013148,0.001833907,0.02652689,0.000490352,0.0002874267,0.000883745,0.001186189,0.478899,0.01955174,0.2247494,0.006303999,0.2362742],"study_design_scores_gemma":[0.0002836573,0.001114701,0.003353344,0.00009665444,0.00008945318,0.0001787357,0.0002560204,0.6985929,0.006943323,0.2821578,0.006865972,0.00006744698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5184383,0.000435482,0.4250239,0.00483814,0.0001648854,0.0004707951,0.0003430694,0.000965856,0.04931949],"genre_scores_gemma":[0.9548755,0.0001416701,0.03928692,0.0003943584,0.00004864888,0.0002173807,0.0001637146,0.00003793146,0.004833865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009324278,"threshold_uncertainty_score":0.03119284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09921364006409483,"score_gpt":0.4137674019392119,"score_spread":0.3145537618751171,"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."}}