{"id":"W2137224661","doi":"10.1109/isit.2011.6034066","title":"Mixed strategy Nash equilibrium in two-user resource allocation games","year":2011,"lang":"en","type":"article","venue":"","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Nash equilibrium; Computer science; Uniqueness; Resource allocation; Channel (broadcasting); Game theory; Selection (genetic algorithm); Strategy; Resource (disambiguation); Equilibrium selection; Mathematical optimization; Resource management (computing); Mathematical economics; Best response; Repeated game; Mathematics; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002775821,0.001055509,0.001402345,0.0006894532,0.0006396658,0.001892812,0.001084651,0.001330572,0.001286573],"category_scores_gemma":[0.005345634,0.0004593878,0.0006035204,0.0008049069,0.002120906,0.001763683,0.001351568,0.0008281699,0.000200804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0013187,"about_ca_system_score_gemma":0.001195873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002003478,"about_ca_topic_score_gemma":0.001766181,"domain_scores_codex":[0.9964696,0.002365765,0.0001071571,0.0002799474,0.0004608214,0.0003167329],"domain_scores_gemma":[0.9974858,0.001890914,0.0002263158,0.00006915155,0.0001926079,0.0001351962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003145291,0.00013979,0.001126632,0.0002013155,0.0001497506,0.0005754267,0.0004055364,0.491716,0.003443124,0.486509,0.0009060437,0.01451277],"study_design_scores_gemma":[0.00006476917,0.00009275534,0.0001807797,0.00001553583,0.00001589815,0.00009187707,0.00006805919,0.8526213,0.0006097417,0.1454652,0.0007501985,0.00002386099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09682649,0.000332187,0.8892657,0.0004655356,0.00004011756,0.0001330769,0.00006623742,0.0000740861,0.01279651],"genre_scores_gemma":[0.9267555,0.0002475501,0.0688341,0.0001151867,0.00002750809,0.0002811251,0.00003682646,0.00001571486,0.003686552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002775821,"threshold_uncertainty_score":0.01468009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1988330265690854,"score_gpt":0.3803723356507712,"score_spread":0.1815393090816858,"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."}}