{"id":"W2463032555","doi":"","title":"A characterization of voting power for discrete weight distributions","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Voting; Shapley value; Power index; Mathematical economics; Power (physics); Value (mathematics); Weighted voting; Mathematical optimization; Game theory; Mathematics; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000514103,0.0001286182,0.0002468209,0.0001678282,0.00007024599,0.00005094553,0.0002782217,0.00006723766,0.0009688074],"category_scores_gemma":[0.0007327356,0.000110173,0.000132562,0.00009494438,0.0001080787,0.0001918036,0.00003807433,0.00006151639,0.0004140793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007271142,"about_ca_system_score_gemma":0.00002063133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001784712,"about_ca_topic_score_gemma":0.000007436377,"domain_scores_codex":[0.9985378,0.00002126013,0.0008568454,0.0003245139,0.00006835184,0.0001911959],"domain_scores_gemma":[0.9988352,0.0001588201,0.0005554012,0.0001775433,0.0002215578,0.00005149126],"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":[0.0000568053,0.00005507406,0.0004843091,0.000006848497,0.00002844204,4.469553e-7,0.0001392772,0.000003705047,0.05692821,0.9362942,0.000009582681,0.005993109],"study_design_scores_gemma":[0.0001445643,0.0002539196,0.00315974,0.0004301265,0.000006421632,0.00000438595,0.0001074628,0.009359915,0.308027,0.6734083,0.004703792,0.0003943813],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1829567,0.000005186029,0.8068032,0.003040616,0.001190253,0.0002257185,0.001134195,0.00003269461,0.004611472],"genre_scores_gemma":[0.9987535,0.00001277148,0.0002775246,0.00005118835,0.0001521709,0.00003128692,0.00005033449,0.00001281346,0.0006584262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8157968,"threshold_uncertainty_score":0.9999444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029634677157848,"score_gpt":0.2876688554881278,"score_spread":0.184705387772343,"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."}}