{"id":"W1568552330","doi":"10.1007/978-3-642-15246-7_47","title":"An Adaptive Bidding Strategy for Combinatorial Auction-Based Resource Allocation in Dynamic Markets","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bidding; Combinatorial auction; Computer science; Profit (economics); Common value auction; Operations research; Ebidding; Resource allocation; Resource (disambiguation); Mathematical optimization; Margin (machine learning); Adaptive strategies; Microeconomics; Economics; Machine learning; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004179571,0.000321826,0.000386526,0.0009624576,0.0004545806,0.0004358739,0.001765917,0.0004369709,0.0001069688],"category_scores_gemma":[0.0005640952,0.0002882906,0.0001090136,0.0008686685,0.0008376756,0.0004382395,0.0001138896,0.0007691039,0.0000257971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002525034,"about_ca_system_score_gemma":0.0005604629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000117909,"about_ca_topic_score_gemma":0.000269853,"domain_scores_codex":[0.9963567,0.000114359,0.0007275799,0.00138797,0.001042466,0.0003708989],"domain_scores_gemma":[0.9952506,0.002616859,0.0004819444,0.001052217,0.0004601401,0.0001382013],"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":[0.0002054559,0.00009966045,0.00002917199,0.000007844711,0.000004643963,0.00000364479,0.0003049149,0.3490042,0.000972582,0.07241751,0.00001828527,0.5769321],"study_design_scores_gemma":[0.0002897703,0.0001438258,0.0002108566,0.00004602084,0.000003973294,0.000003871219,0.00000324346,0.4690245,0.0006390832,0.5277302,0.001667533,0.0002371226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001771704,0.00002927633,0.9943939,0.0004770666,0.001532335,0.0007321333,0.00002760045,0.00005687082,0.0009790951],"genre_scores_gemma":[0.959821,0.000001716793,0.03911005,0.0002600864,0.0004218479,0.00005732591,0.00002865766,0.00002784379,0.0002714066],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9580494,"threshold_uncertainty_score":0.9999569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04461076059919705,"score_gpt":0.3420991646893079,"score_spread":0.2974884040901108,"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."}}