{"id":"W3124927179","doi":"","title":"Choosing Wisely: The Natural Multi-Bidding Mechanism","year":2005,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Law, Economics, and Judicial Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Bidding; Mechanism (biology); Object (grammar); Set (abstract data type); Nash equilibrium; Mechanism design; Computer science; Microeconomics; Aggregate (composite); Operations research; Incentive compatibility; Mathematical economics; Economics; Incentive; Mathematics; Artificial intelligence","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":[0.008066172,0.0007053054,0.001129471,0.0008456028,0.001469535,0.003575079,0.00343805,0.003171235,0.007347357],"category_scores_gemma":[0.01649241,0.0007121338,0.001140554,0.001098739,0.003015649,0.006395941,0.002162688,0.00235551,0.001521627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174112,"about_ca_system_score_gemma":0.001788365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005011865,"about_ca_topic_score_gemma":0.0005771335,"domain_scores_codex":[0.990746,0.005786652,0.0004439675,0.001124347,0.001355061,0.0005440342],"domain_scores_gemma":[0.9916213,0.003532719,0.001292934,0.002017466,0.0008879814,0.0006476099],"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":[0.00007597258,0.00008267203,0.0002699662,0.00006238652,0.00002485206,0.00008881265,0.000157319,0.004389829,0.0009113046,0.9818392,0.001623243,0.0104744],"study_design_scores_gemma":[0.0002387732,0.0001027203,0.0001739734,0.00002177626,0.00001949824,0.0004228134,0.00005992217,0.05803426,0.0005974708,0.9291434,0.01114566,0.00003975259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04952653,0.0002874256,0.8867856,0.003162183,0.0001891894,0.0006503909,0.0002652871,0.0002404242,0.05889283],"genre_scores_gemma":[0.6117821,0.0002845771,0.3671671,0.001132595,0.0001997267,0.001492538,0.000152593,0.0000858356,0.01770296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008066172,"threshold_uncertainty_score":0.04265851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05983336230777291,"score_gpt":0.2943355713292979,"score_spread":0.234502209021525,"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."}}