{"id":"W4298049763","doi":"10.1609/aaai.v31i1.10592","title":"The Positronic Economist: A Computational System for Analyzing Economic Mechanisms","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Python (programming language); Computer science; Graph; Theoretical computer science; Programming language; Mechanism (biology); Software; Action (physics); Software engineering; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002431654,0.0001424204,0.0002268642,0.00006750972,0.002616603,0.001258577,0.002872271,0.00005879506,0.00006290842],"category_scores_gemma":[0.0009899258,0.00008590773,0.0002032734,0.00008733878,0.0004828331,0.0003166748,0.0002307884,0.000133123,0.0002772652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009897393,"about_ca_system_score_gemma":0.0001307551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002429103,"about_ca_topic_score_gemma":0.00002904269,"domain_scores_codex":[0.9982755,0.00001973712,0.0007506941,0.0004251979,0.0002968062,0.0002321268],"domain_scores_gemma":[0.9967964,0.0007512377,0.001281266,0.0005059895,0.0005999141,0.00006520278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007527063,0.00001508508,0.0000565046,0.000004799314,0.00001529823,1.488996e-8,0.0001144551,0.0009973884,0.001328335,0.967933,0.0001500905,0.02930974],"study_design_scores_gemma":[0.00002625081,0.00004557703,0.0001656655,0.00003283367,0.00001407201,0.000002455967,0.001503919,0.08444124,0.06880269,0.8443345,0.0005362694,0.00009456667],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3670632,0.00004867235,0.5464488,0.04020223,0.003140363,0.002836864,0.0001995836,0.0001472886,0.03991292],"genre_scores_gemma":[0.9980654,0.000004763609,0.0007627643,0.000041586,0.000112666,0.00009507991,5.266849e-7,0.000008913836,0.0009082902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6310022,"threshold_uncertainty_score":0.9997782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1659210741853946,"score_gpt":0.3910233253355,"score_spread":0.2251022511501055,"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."}}