{"id":"W2937941984","doi":"10.1017/asb.2019.6","title":"ECONOMIC SCENARIO GENERATOR AND PARAMETER UNCERTAINTY: A BAYESIAN APPROACH","year":2019,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Markov chain Monte Carlo; Econometrics; Bayesian probability; Portfolio; Context (archaeology); Monte Carlo method; Statistics; Inflation (cosmology); Economics; Mathematics; 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":[0.00984596,0.0009119957,0.001512609,0.003081888,0.0009402636,0.003195704,0.002501875,0.002282405,0.003871811],"category_scores_gemma":[0.04347394,0.001177259,0.001208408,0.001944828,0.002933555,0.005771407,0.002297142,0.003026373,0.0003914437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001930388,"about_ca_system_score_gemma":0.001628301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004135991,"about_ca_topic_score_gemma":0.003635755,"domain_scores_codex":[0.9959614,0.002752493,0.0001040759,0.000360539,0.0006509482,0.0001704736],"domain_scores_gemma":[0.9679851,0.02787794,0.001709544,0.0009800674,0.00104354,0.0004038272],"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":[0.00002389545,0.00003289741,0.0009914902,0.00003249777,0.00005767709,0.0001358349,0.0001034824,0.7108194,0.0001437845,0.2773995,0.0004621712,0.00979732],"study_design_scores_gemma":[0.000007347315,0.000009699658,0.0002508426,0.00002337193,0.0000120276,0.00005130947,0.00002861639,0.7234424,0.00006700613,0.2754053,0.0006815983,0.00002056069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01995346,0.0003824926,0.9729637,0.00135685,0.00003163193,0.0000450847,0.0001332147,0.00007648962,0.005057035],"genre_scores_gemma":[0.8021965,0.001302066,0.1917719,0.0003232583,0.0003026329,0.0002426472,0.0003480734,0.0001177666,0.003395112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00984596,"threshold_uncertainty_score":0.05207103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002046483745128,"score_gpt":0.2429728319468361,"score_spread":0.2329523671093848,"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."}}