{"id":"W2149455953","doi":"10.1109/gamenets.2009.5137414","title":"Bidding efficiently in repeated auctions with entry and observation costs","year":2009,"lang":"en","type":"article","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Common value auction; Bidding; Bayesian game; Computer science; Resource allocation; Microeconomics; Budget constraint; Vickrey–Clarke–Groves auction; Complete information; Resource (disambiguation); Value (mathematics); Operations research; Unique bid auction; Mathematical optimization; Game theory; Repeated game; Auction theory; Economics; Computer network; 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":[],"consensus_categories":[],"category_scores_codex":[0.0006687402,0.0000576751,0.00008272386,0.0001707704,0.0001602143,0.00009878119,0.0001080814,0.00003167356,0.0001709934],"category_scores_gemma":[0.0002427322,0.00003915345,0.00001327587,0.001172801,0.00004836158,0.0002473931,0.0000126224,0.00006675379,0.00005898993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002767449,"about_ca_system_score_gemma":0.00001574473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001400038,"about_ca_topic_score_gemma":0.00004186171,"domain_scores_codex":[0.9990842,0.0000539133,0.0002466898,0.0002586457,0.0002567652,0.00009975598],"domain_scores_gemma":[0.9993443,0.0002112258,0.00007667793,0.0002241298,0.00009432741,0.00004933858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001540694,0.0003920466,0.08077037,0.000001526077,0.000008978965,0.000005971313,0.001193189,0.006710883,0.008976044,0.5886517,0.004425072,0.3087102],"study_design_scores_gemma":[0.0008881469,0.0001597749,0.9033635,0.00003036642,0.000009061888,0.00004375611,0.003494247,0.01308399,0.003231212,0.06160381,0.01384556,0.0002465525],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9487883,0.00001485794,0.0326636,0.002560124,0.00003424058,0.0001460993,0.000001651976,0.00005790637,0.01573316],"genre_scores_gemma":[0.9933702,0.000005022186,0.001623918,0.0003170994,0.00001770585,0.000007372449,0.000002971513,0.000002117193,0.004653629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8225932,"threshold_uncertainty_score":0.1872258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0695017570966685,"score_gpt":0.3613627652144811,"score_spread":0.2918610081178126,"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."}}