{"id":"W1982840974","doi":"10.1145/1978721.1978724","title":"Linear solvers for nonlinear games","year":2011,"lang":"en","type":"article","venue":"ACM SIGecom Exchanges","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Constructive; Reduction (mathematics); USable; Computer science; Nash equilibrium; Mathematical optimization; Theoretical computer science; Mathematics; Mathematical economics","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":["insufficient_payload"],"category_scores_codex":[0.001356476,0.0001401679,0.0002236137,0.0001467913,0.0001979279,0.00005157321,0.001435819,0.00008373835,0.001937663],"category_scores_gemma":[0.002293995,0.0001059493,0.0001441165,0.0003293329,0.0001342309,0.000186349,0.0002267966,0.00008405069,0.001282952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009951918,"about_ca_system_score_gemma":0.00002729844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001526444,"about_ca_topic_score_gemma":0.0000541717,"domain_scores_codex":[0.9985604,0.00006688658,0.0003266736,0.0004229624,0.0003308266,0.0002922225],"domain_scores_gemma":[0.9966881,0.001632735,0.0001545381,0.001185023,0.0002183804,0.0001212023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000514326,0.001006803,0.009658119,0.00004786073,0.0001677446,0.00001213644,0.03096518,0.00002955219,0.006597873,0.09072759,0.2356966,0.6245762],"study_design_scores_gemma":[0.0004478909,0.0001980903,0.002902472,0.00000943632,0.0000267673,0.000004329282,0.00285302,0.000628647,0.02057211,0.3545655,0.6175039,0.0002878773],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8936001,0.000771262,0.04941048,0.01083661,0.001628503,0.00194532,0.0005195759,0.000537118,0.04075103],"genre_scores_gemma":[0.8867905,0.00006686922,0.09694988,0.001317993,0.0006340576,0.000307787,0.00002701413,0.0000396263,0.01386628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6242883,"threshold_uncertainty_score":0.9994947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.341295558988655,"score_gpt":0.4151936907719584,"score_spread":0.07389813178330334,"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."}}