{"id":"W2170069376","doi":"10.1109/ccece.2007.348","title":"A Python-Based MPI Framework for Exploring an Adaptive Fuzzy-Agent Approach to Simulating Large-Scale Non-Cooperative Games","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Python (programming language); Scalability; A priori and a posteriori; Message Passing Interface; Fuzzy logic; Scale (ratio); Construct (python library); Theoretical computer science; Message passing; Distributed computing; Artificial intelligence; Programming language","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":[0.001107675,0.0006633372,0.0006471583,0.0005235069,0.001331042,0.001083312,0.003237948,0.0009159649,0.009634161],"category_scores_gemma":[0.002617124,0.0005483413,0.001159414,0.0007104466,0.0009748121,0.001736172,0.002055504,0.001978482,0.001930628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008862743,"about_ca_system_score_gemma":0.001756296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006233911,"about_ca_topic_score_gemma":0.005040706,"domain_scores_codex":[0.9996918,0.00008371086,0.00002494283,0.00004006708,0.0001183344,0.00004120402],"domain_scores_gemma":[0.9992524,0.0003288665,0.00004323456,0.000177645,0.0001090746,0.0000887714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003718814,0.0004186233,0.005641924,0.0005691979,0.000259886,0.0009374907,0.001215073,0.4539432,0.01688058,0.346462,0.03401562,0.1392846],"study_design_scores_gemma":[0.00008442993,0.00003199731,0.0003351705,0.00002506093,0.00002367244,0.0001045591,0.00003599895,0.9051707,0.004446909,0.05584242,0.03386534,0.00003372757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005826938,0.00004048895,0.9656512,0.0002637312,0.00005369288,0.0001394221,0.000294986,0.02150707,0.00622238],"genre_scores_gemma":[0.145681,0.0002158828,0.8426019,0.0002164772,0.00004275892,0.001148013,0.0007907645,0.003502946,0.00580032],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009634161,"threshold_uncertainty_score":0.03222948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08046424969930821,"score_gpt":0.3470104393579255,"score_spread":0.2665461896586173,"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."}}