{"id":"W4226036023","doi":"10.1109/tac.2022.3142121","title":"Quadratic signaling with prior mismatch at an encoder and decoder: equilibria, continuity, and robustness properties","year":2022,"lang":"en","type":"article","venue":"Bilkent University Institutional Repository (Bilkent University)","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Stackelberg competition; Affine transformation; Mathematical optimization; Robustness (evolution); Computer science; Encoder; Gaussian; Probabilistic logic; Prior probability; Observability; Nash equilibrium; Mathematics; Mathematical economics; Applied mathematics; Artificial intelligence; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0001583496,0.0002610565,0.0002674207,0.0004038585,0.001936035,0.00007742752,0.0005015004,0.00009278381,0.00003173912],"category_scores_gemma":[0.000006172713,0.0003026575,0.00005598829,0.000370356,0.0005182497,0.0008847581,0.0008292268,0.0003178401,0.000001173675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073295,"about_ca_system_score_gemma":0.0001987029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001603095,"about_ca_topic_score_gemma":0.0001850269,"domain_scores_codex":[0.9984782,0.0002238736,0.000184026,0.0004281639,0.0004168059,0.0002689495],"domain_scores_gemma":[0.999084,0.00006258774,0.0001001813,0.0004106039,0.0001274586,0.0002151928],"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":[0.003760835,0.002004309,0.08035706,0.001361844,0.001402308,0.004646102,0.01716977,0.5920967,0.1820889,0.1114494,0.0009695629,0.002693171],"study_design_scores_gemma":[0.01457759,0.002049669,0.01967918,0.001077351,0.001413305,0.004356289,0.04989138,0.436662,0.0500792,0.0002373771,0.413633,0.0063437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910147,0.0003116098,0.003916781,0.00009231162,0.00009842103,0.0004334716,0.00003200225,0.0005079638,0.003592793],"genre_scores_gemma":[0.9963653,0.0001423236,0.001818423,0.00001644946,0.00001990637,0.000002361653,0.00003640318,0.00002112686,0.001577682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4126634,"threshold_uncertainty_score":0.9999425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01307233171328697,"score_gpt":0.1685586840183305,"score_spread":0.1554863523050436,"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."}}