{"id":"W2027407290","doi":"10.1016/j.jedc.2010.04.007","title":"Learning by doing vs. learning from others in a principal-agent model","year":2010,"lang":"en","type":"article","venue":"Journal of Economic Dynamics and Control","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Principal (computer security); Economics; Mathematical economics; Microeconomics; Computer science","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":[0.003923198,0.001169386,0.002604387,0.0007466346,0.001041717,0.003439378,0.002573777,0.004367812,0.007965486],"category_scores_gemma":[0.01093049,0.0009070068,0.001151247,0.0007639173,0.002336357,0.005551546,0.001638634,0.002177553,0.001072337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009287153,"about_ca_system_score_gemma":0.001179102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004608843,"about_ca_topic_score_gemma":0.002641221,"domain_scores_codex":[0.9986246,0.0008063153,0.00006254371,0.0002135706,0.0001091793,0.0001839339],"domain_scores_gemma":[0.9879231,0.008870049,0.001362157,0.0005196045,0.000351469,0.0009737394],"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":[0.000819504,0.0007098221,0.00471107,0.0002298381,0.000254463,0.000533897,0.0006904045,0.5989948,0.001189559,0.3757373,0.001834428,0.01429503],"study_design_scores_gemma":[0.0003585964,0.0001881241,0.0008095829,0.00001439315,0.00007365317,0.00006123901,0.000107593,0.8652394,0.0001120907,0.1325714,0.0004263164,0.0000375887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7138003,0.001010224,0.246462,0.008504326,0.0002239566,0.0002113214,0.0004533563,0.0002522139,0.02908232],"genre_scores_gemma":[0.9744422,0.0005737279,0.01024538,0.0002038158,0.0001519605,0.0001023627,0.0000697354,0.00003576959,0.01417497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007965486,"threshold_uncertainty_score":0.02664715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009613576773334917,"score_gpt":0.2706396551383488,"score_spread":0.2610260783650138,"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."}}