{"id":"W2000091880","doi":"10.1016/j.jebo.2011.01.021","title":"A behavioral model for mechanism design: Individual evolutionary learning","year":2011,"lang":"en","type":"article","venue":"Journal of Economic Behavior & Organization","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"California Institute of Technology; National Science Foundation","keywords":"Convergence (economics); Nash equilibrium; Mechanism (biology); Computer science; Fictitious play; Range (aeronautics); Sequence (biology); Space (punctuation); Mathematical economics; Artificial intelligence; Mathematics; Economics; Physics; Biology","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":[0.005704341,0.001037774,0.001501999,0.001043147,0.0008819343,0.002548936,0.003797752,0.003868193,0.01175607],"category_scores_gemma":[0.01743645,0.0006615644,0.001427704,0.0009299346,0.002904105,0.00434543,0.001585628,0.00292845,0.001384958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001898266,"about_ca_system_score_gemma":0.00202955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001659547,"about_ca_topic_score_gemma":0.001364085,"domain_scores_codex":[0.9974623,0.001704336,0.00008083466,0.0003438133,0.0002428133,0.0001659323],"domain_scores_gemma":[0.9927483,0.005452133,0.0004080703,0.0006687806,0.0004298683,0.0002928794],"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":[0.00003686431,0.00008680974,0.0007361833,0.00008325259,0.00008954497,0.00007213429,0.0001357759,0.1023373,0.0004039799,0.8845105,0.001493226,0.01001452],"study_design_scores_gemma":[0.00007546689,0.00004109392,0.0001947563,0.00001599281,0.00003701482,0.00006763951,0.00002427982,0.2972457,0.00008165569,0.7011464,0.001050409,0.00001956849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01295131,0.0001820684,0.9749085,0.002779917,0.00008653913,0.0001075012,0.000134654,0.0001153913,0.008734073],"genre_scores_gemma":[0.6474045,0.000824345,0.3357266,0.001086385,0.000290985,0.001086201,0.0001912245,0.00008911434,0.01330069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01175607,"threshold_uncertainty_score":0.03932792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1557588111844823,"score_gpt":0.3420563759362246,"score_spread":0.1862975647517423,"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."}}