{"id":"W4299693331","doi":"10.48550/arxiv.1310.3781","title":"An Agent-based Model of the Cognitive Mechanisms Underlying the Origins\\n of Creative Cultural Evolution","year":2013,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia, Okanagan Campus","funders":"","keywords":"Chaining; Novelty; Forward chaining; Recall; Imitation; Computer science; Convergence (economics); Set (abstract data type); Cognition; Swarm behaviour; Backward chaining; Artificial intelligence; Cognitive science; Cognitive psychology; Psychology; Inference; Social psychology; Developmental psychology","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":[0.0005931446,0.0004138207,0.0004138919,0.0005020064,0.0006419004,0.002062206,0.001540329,0.001401338,0.005638322],"category_scores_gemma":[0.002401659,0.0003066024,0.0006392648,0.0004765291,0.001240669,0.002355003,0.0007841046,0.001012405,0.0005838377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062068,"about_ca_system_score_gemma":0.001128122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007316341,"about_ca_topic_score_gemma":0.005416779,"domain_scores_codex":[0.9997965,0.00008956331,0.000009738026,0.00005040022,0.00003037272,0.00002349687],"domain_scores_gemma":[0.9993221,0.0003709201,0.0000857229,0.00007911893,0.00006528411,0.0000768311],"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":[0.00007705293,0.00008492644,0.002149451,0.00007102585,0.0000895976,0.0002326415,0.0005162708,0.6445147,0.00209216,0.3377361,0.001322148,0.01111397],"study_design_scores_gemma":[0.00003400887,0.00002878991,0.0003831143,0.00001030565,0.00001671644,0.0000449393,0.00005559486,0.9034185,0.0001895821,0.09409765,0.001707609,0.00001301905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3367252,0.0007170277,0.5865847,0.004745256,0.0001975827,0.0001534808,0.0005948817,0.0004589988,0.06982289],"genre_scores_gemma":[0.9254776,0.0003884296,0.06078494,0.0001823071,0.00003745169,0.0002090953,0.0001598344,0.0000386766,0.0127218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007316341,"threshold_uncertainty_score":0.01886207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1364629718878613,"score_gpt":0.2509092902291871,"score_spread":0.1144463183413258,"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."}}