{"id":"W2168252242","doi":"10.1098/rspb.2010.0857","title":"Learning in a game context: strategy choice by some keeps learning from evolving in others","year":2010,"lang":"en","type":"article","venue":"Proceedings of the Royal Society B Biological Sciences","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Context (archaeology); Population; Social learning; Evolutionarily stable strategy; Fixation (population genetics); Evolutionary game theory; Mechanism (biology); Game theory; Microeconomics; Biology; Computer science; Economics; Knowledge management; Demography; Sociology","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":[0.001698091,0.0004431247,0.0006691403,0.0004180155,0.001266282,0.002946617,0.001016894,0.001765845,0.003407646],"category_scores_gemma":[0.008956271,0.0003243264,0.0005618958,0.0002922361,0.003875306,0.004797586,0.002388442,0.001765329,0.0004626942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007695316,"about_ca_system_score_gemma":0.0007653112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001372029,"about_ca_topic_score_gemma":0.002014663,"domain_scores_codex":[0.9985067,0.0007649644,0.00005299944,0.0003484901,0.0001659639,0.0001608363],"domain_scores_gemma":[0.9967214,0.001214238,0.0006361362,0.0006129934,0.0002105572,0.0006046587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005767373,0.0004915049,0.05875376,0.0004049696,0.0005678971,0.001641149,0.007011754,0.06772524,0.03913473,0.7339795,0.002725641,0.08698702],"study_design_scores_gemma":[0.0001299914,0.0009309497,0.02687541,0.0001141731,0.000270272,0.001779427,0.003594688,0.2258034,0.004678112,0.7150843,0.02053926,0.0002000002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.801809,0.0004971624,0.1557775,0.003415727,0.00009329324,0.0001118912,0.0000836457,0.0001003536,0.03811137],"genre_scores_gemma":[0.9822792,0.0001369065,0.01490358,0.0002776018,0.00002619789,0.00005171588,0.00003039553,0.00002223458,0.002272295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003407646,"threshold_uncertainty_score":0.01139975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02153318186670228,"score_gpt":0.2716391250099466,"score_spread":0.2501059431432443,"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."}}