{"id":"W2137004940","doi":"","title":"Solving Combinatorial Auctions Using Stochastic Local Search","year":2000,"lang":"en","type":"article","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":233,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Combinatorial auction; Computer science; Common value auction; Mathematical optimization; Local search (optimization); Combinatorial optimization; Quality (philosophy); Theoretical computer science; Mathematical economics; Artificial intelligence; Mathematics; Algorithm","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":[0.001437847,0.0006966637,0.001206032,0.0005351502,0.000424101,0.001173275,0.00142132,0.0009668538,0.00195057],"category_scores_gemma":[0.004474289,0.0004596765,0.000780463,0.0008374446,0.0009258281,0.001366088,0.00131512,0.001172515,0.0004451978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000909457,"about_ca_system_score_gemma":0.001583183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00291585,"about_ca_topic_score_gemma":0.003288173,"domain_scores_codex":[0.9990494,0.0005489375,0.00004210703,0.0001103215,0.0001759768,0.00007321849],"domain_scores_gemma":[0.997951,0.001456237,0.0001868408,0.0001471288,0.0001781554,0.00008052824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000592539,0.00005902492,0.0003649927,0.00008618514,0.00005384725,0.00004193804,0.00003338169,0.9374656,0.001149905,0.03714812,0.001626881,0.02191095],"study_design_scores_gemma":[0.00001322474,0.00001399725,0.00001787529,0.000002125487,0.000003405347,0.000005921351,0.000004490619,0.9919497,0.0001341376,0.007637799,0.0002149656,0.000002370236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02742079,0.0003213094,0.9677012,0.0002472056,0.00003392521,0.00005929207,0.00004184309,0.0005984194,0.003575922],"genre_scores_gemma":[0.6545497,0.0004985091,0.3396682,0.000244785,0.00008997894,0.0003821879,0.0002599952,0.0001848341,0.004121841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00291585,"threshold_uncertainty_score":0.007604122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1345803613420226,"score_gpt":0.4166453570574597,"score_spread":0.2820649957154371,"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."}}