{"id":"W4243537860","doi":"10.3982/qe1491","title":"Saddle cycles: Solving rational expectations models featuring limit cycles (or chaos) using perturbation methods","year":2021,"lang":"en","type":"article","venue":"Quantitative Economics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Limit cycle; Perturbation (astronomy); Saddle; Nonlinear system; Business cycle; Limit (mathematics); Applied mathematics; Convergence (economics); Computer science; Statistical physics; Mathematics; Control theory (sociology); Mathematical optimization; Economics; Physics; Mathematical analysis; Keynesian economics; Macroeconomics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006279268,0.0003194127,0.0006660107,0.0003514449,0.0004481448,0.0003000612,0.000226284,0.0001704844,0.0007693652],"category_scores_gemma":[0.0004875934,0.0003964402,0.0002332157,0.0002169236,0.00009747555,0.00179142,0.00009804319,0.0002275237,0.0003240298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004413102,"about_ca_system_score_gemma":0.0001473646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001786174,"about_ca_topic_score_gemma":0.0001470891,"domain_scores_codex":[0.9975626,0.00009654133,0.001060723,0.0007521092,0.0000262094,0.0005018131],"domain_scores_gemma":[0.9981126,0.0006175655,0.0006366661,0.0004105772,0.00006508907,0.0001575202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006701425,0.0001114729,0.001785422,0.00004027232,0.0002597362,0.000005437205,0.007274094,0.4296848,0.0002305767,0.5596163,0.0002187014,0.0007061216],"study_design_scores_gemma":[0.0005468312,0.00005070533,0.001720845,0.00002670949,0.00002035407,0.00002677972,0.00339407,0.8613706,0.001043911,0.1303888,0.0009037033,0.0005066678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7522153,0.002798695,0.233843,0.0007419047,0.0007681744,0.0002442337,0.0004330001,0.00006540866,0.008890235],"genre_scores_gemma":[0.6947677,0.0006436491,0.301959,0.0004181362,0.0002910853,0.00003600505,0.0001799412,0.00007234687,0.001632174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4316858,"threshold_uncertainty_score":0.9998487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3376727762080827,"score_gpt":0.3590650159585593,"score_spread":0.02139223975047666,"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."}}