{"id":"W4401381294","doi":"10.1145/3677525.3678661","title":"Altruistic Bandit Learning For One-to-Many Matching Markets","year":2024,"lang":"en","type":"article","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Matching (statistics); Computer science; Artificial intelligence; Machine learning; Mathematics; Statistics","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":[0.003704566,0.001105503,0.002100085,0.0005160787,0.0008565583,0.001786254,0.002089421,0.002248612,0.006361303],"category_scores_gemma":[0.01059315,0.0005437396,0.0008745381,0.0006251235,0.001620914,0.002102162,0.001555261,0.002680872,0.0008062407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001607988,"about_ca_system_score_gemma":0.001367031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004442028,"about_ca_topic_score_gemma":0.002956258,"domain_scores_codex":[0.9985256,0.0007077999,0.00007009455,0.0002554461,0.0001741667,0.0002669617],"domain_scores_gemma":[0.9938782,0.00406565,0.0007963966,0.0003821198,0.0004344388,0.0004431926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002604825,0.0001231131,0.00111107,0.00007253121,0.00004836093,0.0001180286,0.0001171981,0.9467582,0.0005600517,0.03607465,0.001179709,0.01357663],"study_design_scores_gemma":[0.00001537509,0.00002955979,0.00009061269,0.000006666795,0.000006643958,0.0000108778,0.000012304,0.9863815,0.0000842579,0.01315541,0.0002013873,0.000005276675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2139225,0.001061656,0.7679339,0.001926372,0.0001366255,0.0001975218,0.0003071269,0.0005364223,0.01397794],"genre_scores_gemma":[0.9590169,0.0003047446,0.03293016,0.0003605514,0.0000516101,0.0002129838,0.0001681615,0.00005275726,0.006902158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006361303,"threshold_uncertainty_score":0.02128071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09496153115245384,"score_gpt":0.4116869797022876,"score_spread":0.3167254485498338,"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."}}