{"id":"W4404951340","doi":"10.2139/ssrn.5043048","title":"Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Curse of dimensionality; Curse; Econometrics; Economics; Artificial intelligence; Psychology; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002984898,0.0003021298,0.0008490712,0.0003276938,0.0002587174,0.0001966715,0.000464754,0.0001411783,0.0001241607],"category_scores_gemma":[0.00005946568,0.0002324532,0.0004804203,0.0002426808,0.000130883,0.00008602669,0.000431181,0.003940475,0.00009778149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007981162,"about_ca_system_score_gemma":0.0007188939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001407758,"about_ca_topic_score_gemma":0.002234151,"domain_scores_codex":[0.9972214,0.00007159225,0.001038542,0.0004891911,0.00007317704,0.0011061],"domain_scores_gemma":[0.9978341,0.0001224169,0.00148918,0.0003904755,0.0001061812,0.00005760635],"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":[0.00003936069,0.00002454533,0.001464335,0.00006719094,0.001996388,0.000002106547,0.0009544412,0.01746925,0.000001227429,0.9757175,0.00001208254,0.002251553],"study_design_scores_gemma":[0.0002492905,0.0002724144,0.0002769792,0.0001443884,0.0001752752,0.00009714242,0.006255995,0.02697807,0.000002997987,0.9567583,0.008429978,0.0003591896],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8331314,0.1357219,0.01783425,0.002455559,0.0009547672,0.0004025997,0.00008863203,0.00004555284,0.00936534],"genre_scores_gemma":[0.9877954,0.008315491,0.0002821147,0.00002509884,0.00022839,0.00001325258,0.00001697302,0.00006135934,0.00326197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1546639,"threshold_uncertainty_score":0.9983575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968285987325112,"score_gpt":0.2260897723426841,"score_spread":0.206406912469433,"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."}}