{"id":"W4394724573","doi":"10.1038/s41467-024-47380-8","title":"Dynamics of collective cooperation under personalised strategy updates","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Institute of Allergy and Infectious Diseases; National Heart, Lung, and Blood Institute; National Key Research and Development Program of China; National Institute on Aging; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Beijing Nova Program; National Institutes of Health; National Natural Science Foundation of China","keywords":"Computer science; Homogeneous; Property (philosophy); Population; Heterogeneous network; Variety (cybernetics); Distributed computing; Data science; Management science; Artificial intelligence; Statistical physics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004748595,0.00006452118,0.00007927135,0.00008593228,0.0006958742,0.0000861353,0.0004180182,0.000200027,0.0002023908],"category_scores_gemma":[0.0001311383,0.00006418193,0.00004554586,0.0007423638,0.0003772689,0.0003182032,0.0000469229,0.0004197314,0.00002170942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002802408,"about_ca_system_score_gemma":0.0007441518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001486372,"about_ca_topic_score_gemma":0.01278784,"domain_scores_codex":[0.9990457,0.0004332561,0.0001449821,0.0001158041,0.0001610934,0.00009914264],"domain_scores_gemma":[0.9988351,0.0004044429,0.00003425692,0.000356174,0.0003359404,0.00003402624],"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":[0.000007522494,0.00004800049,0.00008099294,0.000004426361,0.00002688699,1.552859e-7,0.003251675,0.0002518481,0.0002722703,0.9939156,0.001477759,0.0006628791],"study_design_scores_gemma":[0.0008320261,0.0003272709,0.009076188,0.0003628647,0.0003342417,0.00001192855,0.07420347,0.2524897,0.0008630578,0.5077217,0.1527064,0.001071023],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1219882,0.05135883,0.01133975,0.1403853,0.001283343,0.001690519,0.0005457075,0.000859884,0.6705484],"genre_scores_gemma":[0.9941516,0.0007662932,0.0005232345,0.0001360473,0.00005974028,0.00002582275,0.0003111692,0.000007684313,0.004018429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8721634,"threshold_uncertainty_score":0.7135913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02626268961602337,"score_gpt":0.3490753171151401,"score_spread":0.3228126274991167,"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."}}