{"id":"W785130179","doi":"10.1609/aaai.v28i1.8893","title":"Robust Winners and Winner Determination Policies under Candidate Uncertainty","year":2014,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación","keywords":"Unavailability; Voting; Computer science; Computation; Condorcet method; Majority rule; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00825788,0.0007697039,0.002453726,0.0009814135,0.001182097,0.003508416,0.002773569,0.001809957,0.005282045],"category_scores_gemma":[0.041563,0.0006320182,0.001029804,0.001329814,0.001960424,0.004791236,0.002432478,0.002388602,0.000592889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00182272,"about_ca_system_score_gemma":0.002099281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002597637,"about_ca_topic_score_gemma":0.001907553,"domain_scores_codex":[0.9939322,0.002312411,0.0003420288,0.001315581,0.0009806997,0.00111703],"domain_scores_gemma":[0.9722586,0.01989387,0.002744005,0.002489039,0.001497315,0.001117146],"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":[0.0007170491,0.0001979751,0.004033338,0.0001681757,0.0001522389,0.0002976126,0.0003535656,0.7332762,0.00235071,0.2000595,0.003002553,0.05539113],"study_design_scores_gemma":[0.00006306031,0.0001086997,0.0005570418,0.00001739658,0.00002855683,0.00008425094,0.0001164813,0.8558506,0.001250598,0.1410328,0.0008663168,0.00002420935],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.17376,0.0004416669,0.817453,0.001299063,0.00007353081,0.0002748688,0.000351595,0.0004184073,0.005927929],"genre_scores_gemma":[0.9033542,0.0001680773,0.09300212,0.0001355224,0.00005634949,0.0001566245,0.0002184074,0.0000825993,0.002826198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00825788,"threshold_uncertainty_score":0.04367238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0977726672521633,"score_gpt":0.2617441265676452,"score_spread":0.1639714593154819,"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."}}