{"id":"W1976617111","doi":"10.1016/j.dss.2014.06.006","title":"Filtering trust opinions through reinforcement learning","year":2014,"lang":"en","type":"article","venue":"Decision Support Systems","topic":"Access Control and Trust","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Research Foundation","keywords":"Reinforcement learning; Trustworthiness; Computer science; Outcome (game theory); Accountability; Key (lock); Order (exchange); Intervention (counseling); Mechanism (biology); Cognitive psychology; Computer security; Artificial intelligence; Psychology; Business; Microeconomics; Political science","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":[0.005569175,0.0006526343,0.001328757,0.0008538443,0.0005184356,0.001573652,0.001060969,0.001566549,0.001615759],"category_scores_gemma":[0.04287701,0.0004072366,0.0005545127,0.0005187812,0.001259372,0.002620389,0.001087354,0.001969738,0.0002771475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502997,"about_ca_system_score_gemma":0.001093719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006597961,"about_ca_topic_score_gemma":0.005174519,"domain_scores_codex":[0.997007,0.001621485,0.0001605206,0.000475371,0.0004928423,0.0002428101],"domain_scores_gemma":[0.9500749,0.04108998,0.002725049,0.001906801,0.003389166,0.000814181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001196629,0.0009015934,0.03004083,0.0002048858,0.0004592834,0.0002904991,0.0006417233,0.7377445,0.004396363,0.04988765,0.003679273,0.1705568],"study_design_scores_gemma":[0.00003350873,0.00005648312,0.0005919737,0.000006202099,0.00002148442,0.00001641714,0.00001827152,0.9865255,0.0003698791,0.01222628,0.0001244904,0.000009584084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2262623,0.0002874994,0.768721,0.001322304,0.0001206389,0.0001061542,0.00006989851,0.0004205903,0.002689505],"genre_scores_gemma":[0.9847973,0.0000473375,0.01412189,0.00009804078,0.00003679465,0.00003660125,0.00002815438,0.0000110794,0.0008228181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006597961,"threshold_uncertainty_score":0.02945298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03777623411525292,"score_gpt":0.3354713864031502,"score_spread":0.2976951522878972,"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."}}