{"id":"W2111589951","doi":"10.2200/s00108ed1v01y200802aim003","title":"Essentials of Game Theory: A Concise Multidisciplinary Introduction","year":2008,"lang":"en","type":"article","venue":"Synthesis lectures on artificial intelligence and machine learning","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":381,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Game theory; Field (mathematics); Multidisciplinary approach; Computer science; Notation; Data science; Epistemology; Management science; Sociology; Social science; Mathematical economics; Mathematics; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002511317,0.0001876535,0.0003719929,0.0003055862,0.000498533,0.00006178091,0.0003538796,0.00008768167,0.001244254],"category_scores_gemma":[0.008073512,0.0001369803,0.0001286514,0.0005336141,0.0005299609,0.0001305505,0.00009772011,0.0003222787,0.0002864331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000117505,"about_ca_system_score_gemma":0.0000323911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003085602,"about_ca_topic_score_gemma":0.00002036685,"domain_scores_codex":[0.997305,0.0007637698,0.0006581473,0.0005328646,0.0005145208,0.0002256577],"domain_scores_gemma":[0.9951774,0.003829542,0.0003327509,0.0004075639,0.0001615437,0.0000911272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008692273,0.0003174527,0.00106181,0.00001299338,0.00005126821,0.00001051257,0.005530635,0.03379378,0.0710014,0.2285934,0.0001668248,0.6585907],"study_design_scores_gemma":[0.00002911664,0.0002008825,0.001153676,0.0000241902,0.00003411889,0.00003766123,0.001536277,0.007816319,0.5940843,0.3920095,0.002836174,0.0002377835],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8720406,0.0002593399,0.1227535,0.002615804,0.0001859178,0.0002676281,0.0000122553,0.00008352003,0.001781458],"genre_scores_gemma":[0.9987366,0.0001048646,0.0003198003,0.00005874716,0.0002369131,0.00002700691,0.000003290628,0.00001542519,0.0004973838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6583529,"threshold_uncertainty_score":0.9996687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1253534313265015,"score_gpt":0.3695890453793341,"score_spread":0.2442356140528325,"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."}}