{"id":"W2116453336","doi":"10.1016/j.jtbi.2009.05.029","title":"The evolution of social learning rules: Payoff-biased and frequency-dependent biased transmission","year":2009,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":184,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Conformist; Social learning; Stochastic game; Population; Natural selection; Learning rule; Selection (genetic algorithm); Mathematical economics; Computer science; Artificial intelligence; Economics; Sociology; Law","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":[0.002637313,0.00007206817,0.0001774933,0.00005597904,0.0006644974,0.00002078833,0.0001818766,0.0001588999,0.0001558252],"category_scores_gemma":[0.0007597522,0.00004476787,0.00009230404,0.0001032196,0.001292862,0.0000952968,0.000009105124,0.0002829654,0.000002715306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000954288,"about_ca_system_score_gemma":0.0001830098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001750121,"about_ca_topic_score_gemma":0.000007555155,"domain_scores_codex":[0.9977337,0.001422322,0.0003763238,0.00008738825,0.0001913556,0.0001889368],"domain_scores_gemma":[0.9988689,0.0005735786,0.0002346956,0.00004553831,0.0001990604,0.00007821095],"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":[0.0002777517,0.00004513764,0.0003161312,0.000001709731,0.00001311235,0.000001530518,0.001707096,0.000009076865,0.01992295,0.9598156,0.0000200009,0.01786991],"study_design_scores_gemma":[0.0005179095,0.0009481802,0.003586038,0.00002721257,0.00004215958,0.00001475465,0.003436706,0.0001467429,0.0005888427,0.9890487,0.001556058,0.00008675631],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588583,0.00153524,0.01630821,0.01327831,0.0002294937,0.0001286563,0.000003809567,0.00001749833,0.009640536],"genre_scores_gemma":[0.999087,0.0002835186,0.0002414417,0.00006734407,0.0002477945,5.743817e-7,0.00000149853,0.000002745302,0.00006811139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04022873,"threshold_uncertainty_score":0.5110844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008663112882890227,"score_gpt":0.2884936203283744,"score_spread":0.2798305074454842,"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."}}