{"id":"W4387514870","doi":"10.1007/s00355-026-01668-4","title":"On the Ordinal Bayesian Incentive Compatibility of Vote Share and Veto Share Rules","year":2023,"lang":"en","type":"preprint","venue":"Social Choice and Welfare","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Veto; Incentive compatibility; Compatibility (geochemistry); Incentive; Bayesian probability; Econometrics; Computer science; Business; Microeconomics; Economics; Artificial intelligence; Political science; Law; Engineering; Politics","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":[0.02996249,0.001067749,0.003705015,0.002974105,0.00239458,0.009398893,0.003821233,0.00629773,0.01651504],"category_scores_gemma":[0.1415342,0.002305058,0.002110912,0.002739748,0.008878103,0.01920645,0.004451978,0.007114157,0.001186403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002951691,"about_ca_system_score_gemma":0.003195051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001870015,"about_ca_topic_score_gemma":0.00110538,"domain_scores_codex":[0.974848,0.01673453,0.001347601,0.002539768,0.002903199,0.001626878],"domain_scores_gemma":[0.8279982,0.1459283,0.01057362,0.007016662,0.005263844,0.003219418],"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":[0.0001692241,0.0001025106,0.0008943562,0.00005898489,0.00003500255,0.00004287032,0.0002221218,0.005964254,0.0003145412,0.9873214,0.0006063553,0.004268452],"study_design_scores_gemma":[0.00008678122,0.00004268331,0.0008768443,0.0000303118,0.00001963637,0.00004678363,0.00006402833,0.03638195,0.0001555962,0.9617816,0.0004881469,0.00002565563],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3053856,0.0007954235,0.611737,0.008233434,0.000203354,0.0004369471,0.001026051,0.0001649838,0.0720172],"genre_scores_gemma":[0.9478907,0.0005285455,0.04123243,0.0006187067,0.0004424365,0.0004935814,0.000402622,0.0001053682,0.008285743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02996249,"threshold_uncertainty_score":0.1584587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06776049486945887,"score_gpt":0.2707366268089776,"score_spread":0.2029761319395188,"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."}}