{"id":"W2952084589","doi":"10.1016/j.ejor.2019.05.036","title":"The return function: A new computable perspective on Bayesian–Nash equilibria","year":2019,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nash equilibrium; Bayesian probability; Mathematical economics; Computer science; Outcome (game theory); Function (biology); Mathematical optimization; Bayesian game; Convergence (economics); Best response; Perspective (graphical); Mathematics; Economics; Game theory; Artificial intelligence; Repeated game","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005526255,0.001612112,0.001912519,0.003593722,0.001263743,0.008210948,0.003311148,0.004014905,0.01036888],"category_scores_gemma":[0.02548714,0.0008208949,0.001697579,0.003131363,0.00584861,0.01953108,0.003565315,0.005204159,0.001326661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003132318,"about_ca_system_score_gemma":0.001807627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001837979,"about_ca_topic_score_gemma":0.0008444649,"domain_scores_codex":[0.9966954,0.001806293,0.0001657614,0.0003596235,0.0007476797,0.0002252544],"domain_scores_gemma":[0.9919732,0.005509337,0.0005608655,0.0008417196,0.0007789482,0.0003359714],"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":[0.00000412146,0.000004902623,0.00003111105,0.00001099823,0.00000301397,0.00001089216,0.00003419051,0.004113196,0.00004586699,0.9931858,0.0003399078,0.00221604],"study_design_scores_gemma":[0.000003962701,0.000004697044,0.00001933285,0.00001439835,0.000004164348,0.00001725003,0.00001560555,0.02690958,0.0000574133,0.9714603,0.001487033,0.00000620988],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01001727,0.000686908,0.954231,0.003572647,0.0001578614,0.00003491907,0.0001010074,0.0001143333,0.0310841],"genre_scores_gemma":[0.6293809,0.003322663,0.3337395,0.001106592,0.001330357,0.0003745054,0.0002200869,0.0004655962,0.03005975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01036888,"threshold_uncertainty_score":0.0346874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2020059177741892,"score_gpt":0.4604277054761807,"score_spread":0.2584217877019915,"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."}}