{"id":"W2119621836","doi":"10.2139/ssrn.2133302","title":"The Logic of Backward Induction","year":2012,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","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":[0.01215645,0.00005664503,0.00009183786,0.00006040344,0.0003701878,0.00005291945,0.0005459327,0.00003612248,0.00008855409],"category_scores_gemma":[0.0006453203,0.0000291413,0.00008841996,0.0003603573,0.0001104749,0.0002434528,0.00003477707,0.0007036683,0.0002830434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001059078,"about_ca_system_score_gemma":0.0003316941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003003781,"about_ca_topic_score_gemma":0.00002791744,"domain_scores_codex":[0.9979022,0.0002210498,0.0003405479,0.00008050942,0.0004606951,0.0009949536],"domain_scores_gemma":[0.998782,0.000476498,0.0002743421,0.0002619683,0.0001461272,0.00005910257],"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":[0.00001317293,0.00002616515,0.001196857,1.153566e-7,0.00001359812,1.761076e-8,0.0001115881,0.000009935792,0.0008753287,0.913693,0.0002914822,0.08376871],"study_design_scores_gemma":[0.00007762149,0.00005163814,0.002902389,0.000001085102,0.000007449881,0.0001368548,0.00369805,0.000006494143,0.0005425081,0.9692274,0.02331163,0.00003689181],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9534585,0.003663267,0.03042972,0.003999799,0.0005043238,0.0001014023,0.000001075054,0.0000127211,0.007829116],"genre_scores_gemma":[0.9958257,0.0006002117,0.00005453874,0.00003719864,0.0002901613,0.000002885756,1.785727e-7,0.000003880345,0.003185236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08373182,"threshold_uncertainty_score":0.4213207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07901242858771615,"score_gpt":0.3779603423382767,"score_spread":0.2989479137505605,"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."}}