{"id":"W2590970332","doi":"10.3982/te1934","title":"Attaining efficiency with imperfect public monitoring and one-sided Markov adverse selection","year":2017,"lang":"en","type":"article","venue":"Theoretical Economics","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Stochastic game; Pairwise comparison; Mathematical economics; Imperfect; Constructive; Private information retrieval; Markov chain; Adverse selection; Folk theorem; Repeated game; Perfect information; Markov process; Computer science; Continuation; Selection (genetic algorithm); Economics; Process (computing); Mathematics; Microeconomics; Game theory; Equilibrium selection; Statistics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002450603,0.0001342025,0.000234769,0.00009514606,0.0008998003,0.000614804,0.0006448348,0.00007424678,0.0003151336],"category_scores_gemma":[0.001923823,0.000104147,0.00004857002,0.00009127326,0.001155264,0.0004719404,0.0001994799,0.0001733577,0.0001374487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004343118,"about_ca_system_score_gemma":0.00007666965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004651979,"about_ca_topic_score_gemma":0.000006489297,"domain_scores_codex":[0.9986711,0.0001011325,0.0003224763,0.0004585365,0.000149885,0.0002968274],"domain_scores_gemma":[0.9976571,0.001104978,0.0002335993,0.0007135461,0.00009731035,0.0001935084],"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":[0.00006525472,0.00002869976,0.01558148,0.000001265456,0.00001106486,4.879548e-7,0.0001442851,0.00006902173,0.0002838005,0.9660931,0.000004304307,0.01771723],"study_design_scores_gemma":[0.0007485701,0.00021083,0.04070802,0.0000249843,0.00003257606,0.00003235417,0.0008659384,0.01144417,0.003461429,0.9418618,0.0002810214,0.0003282968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673172,0.000007540001,0.003364423,0.001785939,0.00009028133,0.0001483282,0.000004138958,0.00004135904,0.0272408],"genre_scores_gemma":[0.9978071,0.00001846081,0.001822202,0.00003520134,0.0001330616,0.00001899914,7.250586e-7,0.00001409514,0.0001501214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03048994,"threshold_uncertainty_score":0.6920627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06835838086256651,"score_gpt":0.3386419985993186,"score_spread":0.2702836177367521,"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."}}