{"id":"W1985944206","doi":"10.1109/msp.2012.2232356","title":"Social learning and bayesian games in multiagent signal processing: how do local and global decision makers interact?","year":2013,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Stylized fact; Artificial intelligence; Bayesian network; Bayesian probability; Machine learning; Social learning; SIGNAL (programming language); Bayesian inference; Signal processing; Data science; Human–computer interaction; Knowledge management; Digital signal processing","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":[0.005536629,0.0009852519,0.001094607,0.0008897273,0.00137328,0.003923679,0.00135832,0.003514379,0.002117751],"category_scores_gemma":[0.01391566,0.0005354051,0.0008705952,0.001012343,0.005493782,0.007408623,0.002130723,0.003208277,0.0003462632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001486482,"about_ca_system_score_gemma":0.001061829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002466207,"about_ca_topic_score_gemma":0.002028279,"domain_scores_codex":[0.9963013,0.0025205,0.0001017047,0.0003619326,0.0004518796,0.0002627113],"domain_scores_gemma":[0.9914572,0.00681681,0.0007166283,0.0002917666,0.0003375504,0.0003801128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004190914,0.00004908487,0.001189241,0.0001129366,0.00006856068,0.0001433166,0.001004296,0.04049282,0.0006837742,0.937118,0.001861223,0.01723485],"study_design_scores_gemma":[0.00001634424,0.00002748961,0.0004939422,0.00003519816,0.00001692946,0.00004877166,0.0002359021,0.08755184,0.0001635508,0.908232,0.003151716,0.0000263562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05758825,0.006172762,0.8743439,0.02354395,0.0002937738,0.0001301851,0.0001396206,0.00008091317,0.03770658],"genre_scores_gemma":[0.8969592,0.005245875,0.08754587,0.002286213,0.0007775318,0.0003343979,0.00007866179,0.00004701672,0.006725157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005536629,"threshold_uncertainty_score":0.02928084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038071194831622,"score_gpt":0.2562815520812541,"score_spread":0.2459008401329379,"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."}}