{"id":"W3122400387","doi":"10.3982/te1204","title":"Dynamics of information exchange in endogenous social networks","year":2014,"lang":"en","type":"article","venue":"Theoretical Economics","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Office of the Chief Medical Examiner","funders":"","keywords":"Aggregate (composite); Private information retrieval; Information exchange; Computer science; Action (physics); Social learning; Network formation; Telecommunications network; Microeconomics; Welfare; Mathematical economics; Economics; Computer network; Telecommunications","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":[0.001637317,0.0006180509,0.0008464984,0.001023013,0.001165017,0.002327946,0.001939574,0.002242323,0.005094276],"category_scores_gemma":[0.01134952,0.0004207802,0.0006175896,0.001387123,0.001988736,0.004939849,0.001855396,0.001489075,0.0005404441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002608452,"about_ca_system_score_gemma":0.0009709297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004390269,"about_ca_topic_score_gemma":0.002347847,"domain_scores_codex":[0.9984822,0.0007256689,0.0000550102,0.0002686308,0.0002835456,0.0001850378],"domain_scores_gemma":[0.9932199,0.004322019,0.001286597,0.0004677563,0.000371238,0.0003326331],"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":[0.00004855069,0.00004993437,0.001230166,0.00004905333,0.00003055027,0.0002606229,0.0003983505,0.2696271,0.0009121048,0.7200912,0.001304903,0.005997409],"study_design_scores_gemma":[0.00003021765,0.00002313405,0.0003569262,0.00001550133,0.000008552714,0.00005791835,0.0001039081,0.6914352,0.0001635565,0.3056155,0.002170566,0.00001907643],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3184116,0.0008184762,0.6298442,0.006780338,0.0001056899,0.0001772249,0.0006190784,0.0003357373,0.04290767],"genre_scores_gemma":[0.9706014,0.0005154566,0.02111251,0.0002174308,0.00005589876,0.000184373,0.0001236879,0.00002992068,0.007159332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005094276,"threshold_uncertainty_score":0.01892573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03717421351222842,"score_gpt":0.2874504293326846,"score_spread":0.2502762158204561,"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."}}