{"id":"W3194348169","doi":"10.1017/asb.2021.21","title":"ON COMPLEX ECONOMIC SCENARIO GENERATORS: IS LESS MORE?","year":2021,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; Nvidia","keywords":"Markov chain Monte Carlo; Econometrics; Computer science; Bayesian probability; Generator (circuit theory); Markov chain; Monte Carlo method; Economics; Perspective (graphical); Conservatism; Yield curve; Term (time); Statistics; Mathematics; Interest rate; Finance; Artificial intelligence; Machine learning; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006659742,0.0007090745,0.0009175163,0.001217796,0.0005786319,0.00300849,0.001455582,0.001847839,0.01103405],"category_scores_gemma":[0.03417027,0.0005142145,0.000768122,0.001071066,0.002505936,0.008049806,0.002782539,0.002948101,0.00131812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000833799,"about_ca_system_score_gemma":0.0006830306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009915559,"about_ca_topic_score_gemma":0.0007205057,"domain_scores_codex":[0.9971657,0.001810209,0.00008260101,0.0002427013,0.0005940936,0.000104583],"domain_scores_gemma":[0.9738997,0.02080065,0.001223292,0.002341788,0.001145366,0.0005891202],"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":[0.00006405585,0.00003965127,0.0008575663,0.00006277288,0.00003058728,0.0001830888,0.0001536793,0.1988678,0.000335799,0.7641687,0.002961253,0.03227511],"study_design_scores_gemma":[0.00001036758,0.00002120166,0.0001208101,0.0000454136,0.000005960457,0.00008690732,0.00003417921,0.2860635,0.0002331397,0.7080005,0.0053574,0.00002068171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01950892,0.0006086856,0.965957,0.003973867,0.0002062328,0.00004793915,0.0001871487,0.0002313386,0.00927898],"genre_scores_gemma":[0.7141589,0.002133317,0.2720025,0.001607368,0.0009927294,0.0002474186,0.0007555485,0.0005629553,0.007539284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01103405,"threshold_uncertainty_score":0.03691256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0612820005810959,"score_gpt":0.2411288036641389,"score_spread":0.179846803083043,"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."}}