{"id":"W2038136293","doi":"10.1109/tem.2011.2169417","title":"Optimization-Based Methods for Improving the Accuracy and Outcome of Learning in Electronic Procurement Negotiations","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Engineering Management","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Negotiation; Outcome (game theory); Procurement; Computer science; Knowledge management; Function (biology); Artificial intelligence; Empirical research; Management science; Machine learning; Operations research; Engineering; Business; Microeconomics; Mathematics; Marketing","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":[0.008382867,0.001087885,0.001585976,0.0009205043,0.0006232959,0.001603257,0.002057925,0.001736963,0.001718517],"category_scores_gemma":[0.02466496,0.0005731879,0.0006402443,0.0009593263,0.002272876,0.00254056,0.002589213,0.00224489,0.0003356718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830795,"about_ca_system_score_gemma":0.001473088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00321324,"about_ca_topic_score_gemma":0.00154561,"domain_scores_codex":[0.9973047,0.001592489,0.0001438084,0.0002924054,0.0004751077,0.0001914617],"domain_scores_gemma":[0.9848089,0.01249439,0.001146281,0.0005541692,0.0008213664,0.0001749477],"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":[0.0000726181,0.00004788429,0.000372743,0.00004639361,0.00001950071,0.00001604464,0.00006059079,0.9589806,0.0003878415,0.01263118,0.0001302976,0.0272343],"study_design_scores_gemma":[0.00001060241,0.00002120916,0.00004488143,0.000004970175,0.000003415391,0.000003569716,0.000006137908,0.9944799,0.0002822403,0.005050734,0.00008783336,0.00000454475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02308626,0.0003318951,0.9738186,0.000276651,0.00002898599,0.00007640031,0.00001284388,0.0001625919,0.002205642],"genre_scores_gemma":[0.7415743,0.0004231911,0.2548758,0.0001301398,0.00005572146,0.0002631573,0.00003952483,0.0001035857,0.002534623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008382867,"threshold_uncertainty_score":0.0443334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06603120270397125,"score_gpt":0.3564499428409321,"score_spread":0.2904187401369608,"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."}}