{"id":"W4399408372","doi":"10.48550/arxiv.2406.01898","title":"Solving Models of Economic Dynamics with Ridgeless Kernel Regressions","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Inductive bias; Dynamics (music); Computer science; Artificial intelligence; Machine learning; Economics; Multi-task learning; Psychology; Task (project management); Management","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001323116,0.0005285522,0.0009277689,0.000441128,0.0002643684,0.001111475,0.001161519,0.001500577,0.0008369791],"category_scores_gemma":[0.004870915,0.0007050672,0.0008224075,0.0003710003,0.001068944,0.001357462,0.001636047,0.001477991,0.0001768055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006057664,"about_ca_system_score_gemma":0.001165788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004684837,"about_ca_topic_score_gemma":0.0034987,"domain_scores_codex":[0.9995353,0.0002381693,0.00002305172,0.00008401852,0.00007356431,0.00004599771],"domain_scores_gemma":[0.9984096,0.001101925,0.0001952912,0.0001154831,0.0001092981,0.00006848845],"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":[0.00001865279,0.00002668876,0.0005001856,0.00003373895,0.00002896228,0.00004063938,0.00003915377,0.9655229,0.0008467362,0.02739435,0.0002191462,0.005328849],"study_design_scores_gemma":[0.00000171337,0.000002622802,0.00001725115,9.192303e-7,8.059142e-7,0.000001936631,0.00000213933,0.9961866,0.0000585954,0.00366473,0.0000614518,0.000001379815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03554937,0.0001603353,0.9627329,0.0002537244,0.0000179119,0.00001485498,0.00003002469,0.000134479,0.001106421],"genre_scores_gemma":[0.7892608,0.0003070457,0.2054662,0.0001093671,0.00007157614,0.00009794501,0.0001442198,0.0001177755,0.004425053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004684837,"threshold_uncertainty_score":0.009315133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07468314720593862,"score_gpt":0.1728343770960513,"score_spread":0.09815122989011273,"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."}}