{"id":"W3121339690","doi":"","title":"Learning Benevolent Leadership in a Heterogenous Agents Economy","year":2008,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Set (abstract data type); Function (biology); Inflation (cosmology); Value (mathematics); Economics; Outcome (game theory); Microeconomics; Private information retrieval; Extension (predicate logic); Key (lock); Mathematical economics; 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":[0.00236617,0.0005179202,0.001764085,0.0005358855,0.001009772,0.002294258,0.001137594,0.002541395,0.006384545],"category_scores_gemma":[0.00756438,0.000377852,0.0005311643,0.0003772872,0.002445221,0.001880438,0.001821832,0.001932141,0.0004435953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009080873,"about_ca_system_score_gemma":0.001156811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002421938,"about_ca_topic_score_gemma":0.002481725,"domain_scores_codex":[0.9993055,0.0003348194,0.00002736089,0.0001058442,0.00009275953,0.0001336604],"domain_scores_gemma":[0.9937511,0.00352167,0.0007178956,0.000258563,0.0002809111,0.001469828],"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":[0.0006748896,0.0005528656,0.007813101,0.0002506568,0.0002372149,0.001561714,0.0008849102,0.3728933,0.002008967,0.5857477,0.006395863,0.02097866],"study_design_scores_gemma":[0.000199631,0.0001851402,0.001463149,0.00001494233,0.00002652404,0.00007247726,0.0002956697,0.6390579,0.0001113496,0.3579114,0.0006378008,0.0000239874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8678058,0.0005014718,0.1029089,0.007379412,0.0001714455,0.00007222671,0.0001253163,0.0001714074,0.02086405],"genre_scores_gemma":[0.9928071,0.0001602672,0.002102662,0.0001295981,0.000056513,0.00002305911,0.00002256887,0.00000785409,0.004690254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006384545,"threshold_uncertainty_score":0.02135843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0726721695748826,"score_gpt":0.2135874163754166,"score_spread":0.140915246800534,"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."}}