{"id":"W2999448625","doi":"10.1016/j.insmatheco.2020.01.002","title":"Fast and efficient nested simulation for large variable annuity portfolios: A surrogate modeling approach","year":2020,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Nested set model; Nested loop join; Computer science; Surrogate model; Portfolio; Population; Mathematical optimization; Algorithm; Mathematics; Data mining; Finance","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":[0.00429658,0.0008693716,0.002152638,0.0009479563,0.0009445767,0.001562353,0.002529426,0.002420126,0.004036663],"category_scores_gemma":[0.01628138,0.001253232,0.001597657,0.0009379855,0.001141871,0.001813207,0.002932509,0.002704394,0.0007203988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009968836,"about_ca_system_score_gemma":0.002211961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006614077,"about_ca_topic_score_gemma":0.005780011,"domain_scores_codex":[0.9985776,0.0007380184,0.00006439888,0.0001229867,0.0003371351,0.0001599292],"domain_scores_gemma":[0.990473,0.006729815,0.0004992401,0.0007268532,0.001018125,0.0005529242],"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":[0.00006491007,0.00004629796,0.0006216883,0.00002645685,0.00002872055,0.00007107579,0.0000372105,0.9722492,0.0004109403,0.01934953,0.0003740646,0.006719964],"study_design_scores_gemma":[0.000004645768,0.000003473598,0.00001526674,0.000001826651,0.00000128693,0.000005061122,0.000001588733,0.9970819,0.00004096923,0.002775945,0.00006660162,0.00000139736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01396771,0.0001171427,0.9838601,0.0001660885,0.00003330926,0.00004320214,0.00006770585,0.0003018017,0.001443017],"genre_scores_gemma":[0.5572381,0.000229597,0.4372746,0.0002210305,0.00008783396,0.0004244838,0.0005105429,0.0003954978,0.003618321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006614077,"threshold_uncertainty_score":0.02272278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04010748013943836,"score_gpt":0.2773103546660203,"score_spread":0.2372028745265819,"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."}}