{"id":"W4388455350","doi":"10.1007/s10614-023-10493-1","title":"Computing Longitudinal Moments for Heterogeneous Agent Models","year":2023,"lang":"en","type":"article","venue":"Computational Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Monte Carlo method; Computer science; Population; Mathematical optimization; Computation; Method of moments (probability theory); Markov chain Monte Carlo; Function (biology); Applied mathematics; Mathematics; Algorithm; Statistics","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":[0.003216183,0.000798095,0.001640884,0.001467475,0.0008548094,0.002201759,0.001849644,0.001655539,0.003675979],"category_scores_gemma":[0.02673301,0.00132379,0.001303696,0.001188415,0.001011916,0.00288416,0.00218055,0.002079691,0.0005559243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00143678,"about_ca_system_score_gemma":0.001433207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008097717,"about_ca_topic_score_gemma":0.01019386,"domain_scores_codex":[0.9991647,0.0003703721,0.00006699244,0.0001506277,0.0001291632,0.0001182268],"domain_scores_gemma":[0.9813482,0.01558296,0.001016967,0.0008733265,0.0005013504,0.0006772102],"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":[0.000146305,0.00007900996,0.004205644,0.00005568888,0.00009557726,0.0001415816,0.0000729882,0.9571164,0.0003029325,0.02364027,0.001190593,0.01295311],"study_design_scores_gemma":[0.000007508811,0.000004670564,0.0001087512,0.000002713172,0.000004519748,0.000006963031,0.000007258526,0.985297,0.00005020822,0.01442546,0.00008200681,0.000002932341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1522432,0.0007396723,0.8421428,0.001247913,0.0001241813,0.0000553335,0.0006629723,0.001167228,0.001616703],"genre_scores_gemma":[0.886142,0.000520876,0.1093774,0.0001575697,0.0002240365,0.0001330173,0.001288251,0.0001912878,0.001965543],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008097717,"threshold_uncertainty_score":0.01700896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08935507334048438,"score_gpt":0.3399895755705446,"score_spread":0.2506345022300602,"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."}}