{"id":"W2044315104","doi":"10.1109/cec.2009.4983314","title":"An evolutionary random search algorithm for double auction markets","year":2009,"lang":"en","type":"article","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Double auction; Computer science; Population; Evolutionary algorithm; Genetic algorithm; Mathematical optimization; Algorithm; Artificial intelligence; Machine learning; Mathematics; Microeconomics; Common value auction; Economics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002451508,0.00008237857,0.0001307628,0.0001576182,0.0004802968,0.000128003,0.0003724914,0.00006425842,0.001690905],"category_scores_gemma":[0.00008021817,0.00006125533,0.00009960601,0.0005028605,0.00006565066,0.0005740285,0.00001485047,0.00007023442,0.000372779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002898644,"about_ca_system_score_gemma":0.0000471075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005783678,"about_ca_topic_score_gemma":0.000001060286,"domain_scores_codex":[0.9984621,0.0001194651,0.0003278122,0.0003857723,0.0005167958,0.0001880128],"domain_scores_gemma":[0.9985759,0.0004319668,0.00006371103,0.0004562345,0.0003485018,0.0001237191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004976632,0.0002185929,0.00003953153,4.787203e-7,0.00000612086,3.183642e-7,0.00009765467,0.0006393381,0.0007825749,0.06685941,0.03297444,0.8978839],"study_design_scores_gemma":[0.004004038,0.0002528671,0.01025541,0.000002727776,0.00001241787,0.0000340018,0.001183814,0.2298991,0.004775284,0.6113477,0.1379848,0.0002478321],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008084479,0.00002825938,0.9776109,0.002329374,0.0001872197,0.0004992366,0.00001766724,0.0001148087,0.01112802],"genre_scores_gemma":[0.8744105,0.000008693545,0.08839265,0.0005185879,0.0005236016,0.0001135393,0.00003914259,0.000008548011,0.03598479],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8976361,"threshold_uncertainty_score":0.9992217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08819380243659138,"score_gpt":0.4244308049558272,"score_spread":0.3362370025192359,"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."}}