{"id":"W2222340070","doi":"","title":"A Principal-Agent Model of Affect and Cognition: How to Explain Cognitive 'Biases and Fallacies' with a Unified Model","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cognitive dissonance; Simplicity; Cognition; Affect (linguistics); Principal (computer security); Cognitive bias; Psychology; Cognitive psychology; Fallacy; Maximization; Confirmation bias; Social psychology; Epistemology; Computer science; Philosophy","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":[0.006970275,0.001327503,0.001589951,0.001441038,0.0009909421,0.004158295,0.003002264,0.004043745,0.006046625],"category_scores_gemma":[0.01280513,0.0007305451,0.00144269,0.0009959143,0.004713533,0.007245581,0.002147336,0.003806504,0.001639146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001612315,"about_ca_system_score_gemma":0.00187801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002333808,"about_ca_topic_score_gemma":0.001255337,"domain_scores_codex":[0.9974497,0.001258699,0.0001292377,0.0003423961,0.0006219007,0.0001980248],"domain_scores_gemma":[0.9944162,0.003618216,0.0004568842,0.000491964,0.0006863918,0.0003303974],"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":[0.00001550455,0.00001941733,0.0003090116,0.00004494753,0.00003014425,0.00006881016,0.000228699,0.01179948,0.0001204816,0.9825837,0.0007581815,0.004021659],"study_design_scores_gemma":[0.00002455389,0.00001817694,0.0001879654,0.00002376069,0.00001455615,0.00004168615,0.00003536141,0.05227469,0.00003415765,0.9451895,0.002141609,0.00001398022],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01914458,0.002274753,0.8964031,0.01748598,0.0004511149,0.0001739226,0.0002182267,0.0002719529,0.06357647],"genre_scores_gemma":[0.7504642,0.002821639,0.2275341,0.001903902,0.0008281856,0.0008148568,0.0001447775,0.0001034106,0.01538496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006970275,"threshold_uncertainty_score":0.03686273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1084390093061487,"score_gpt":0.3586299427851932,"score_spread":0.2501909334790446,"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."}}