{"id":"W3001979324","doi":"10.65109/qhud6811","title":"Silly Rules Improve the Capacity of Agents to Learn Stable Enforcement and Compliance Behaviors","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Taboo; Punishment (psychology); Enforcement; Compliance (psychology); Psychology; Foraging; Meaning (existential); Social learning; Social psychology; Context (archaeology); Computer science; Political science; Law; Ecology; Psychotherapist","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":[0.004150991,0.001275398,0.0009792458,0.0009219359,0.001024078,0.00351267,0.001912663,0.002289124,0.0104053],"category_scores_gemma":[0.03444979,0.000634591,0.0008715683,0.0005108207,0.002474978,0.007087268,0.00379938,0.002803053,0.002468782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008852058,"about_ca_system_score_gemma":0.00175882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00182422,"about_ca_topic_score_gemma":0.002165863,"domain_scores_codex":[0.9970424,0.0009581116,0.0002824477,0.0008109924,0.0005869389,0.0003191551],"domain_scores_gemma":[0.9736493,0.01133267,0.00404352,0.007941625,0.001735333,0.001297613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009533838,0.001701084,0.03788415,0.001028169,0.0005810645,0.0005849942,0.002718177,0.2834426,0.02893342,0.2314,0.009592991,0.4011798],"study_design_scores_gemma":[0.0002433921,0.0009533191,0.008200436,0.0001617788,0.0002273391,0.0002723933,0.0006871774,0.4884,0.01061028,0.4751273,0.01500875,0.0001078632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4101384,0.001161643,0.5287687,0.004296849,0.0003299729,0.0003783703,0.0005887551,0.002599962,0.05173738],"genre_scores_gemma":[0.9389088,0.0004534361,0.05454987,0.0004598818,0.00008112645,0.0001819419,0.0003938481,0.0002010045,0.004770216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0104053,"threshold_uncertainty_score":0.03480923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09358527773459241,"score_gpt":0.3385780430863672,"score_spread":0.2449927653517748,"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."}}