{"id":"W3121817667","doi":"","title":"Learning by Doing vs. Learning from Others in a Principal-Agent Model","year":2007,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Social learning; Stochastic game; Artificial intelligence; Principal (computer security); Proactive learning; Machine learning; Computer science; Convergence (economics); Reinforcement learning; Moral hazard; Active learning (machine learning); Microeconomics; Robot learning; Knowledge management; Economics; Incentive","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":[0.00331609,0.001011407,0.001541269,0.0004182812,0.000628027,0.002334249,0.002442899,0.002758909,0.004984719],"category_scores_gemma":[0.00758662,0.0004865058,0.001110643,0.0005943289,0.002641717,0.003628747,0.001631587,0.002404158,0.0007010758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145295,"about_ca_system_score_gemma":0.001170346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002695015,"about_ca_topic_score_gemma":0.001500673,"domain_scores_codex":[0.99773,0.001383028,0.00007726302,0.0003225119,0.0002719443,0.0002152882],"domain_scores_gemma":[0.9953382,0.002812156,0.000673948,0.0005347796,0.000267226,0.0003737155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001379704,0.0001567512,0.001062678,0.00009539977,0.00006457669,0.0002165852,0.000315598,0.4745732,0.0009379198,0.5113351,0.0007392053,0.01036499],"study_design_scores_gemma":[0.00008254639,0.00008779612,0.0001628831,0.00001183257,0.00001742223,0.00003278428,0.00003185738,0.8578648,0.0001891787,0.140589,0.0009115302,0.00001829616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1390772,0.0005613777,0.8393239,0.003341367,0.000104331,0.0001332826,0.000156313,0.0001790887,0.01712321],"genre_scores_gemma":[0.9250979,0.000509782,0.06375255,0.0002222962,0.0001028572,0.0002010492,0.00004958912,0.00003611723,0.01002776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004984719,"threshold_uncertainty_score":0.01753736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06594586622558594,"score_gpt":0.3855538787927779,"score_spread":0.319608012567192,"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."}}