{"id":"W2898540848","doi":"10.3386/w23725","title":"Learning to Live in a Liquidity Trap","year":2017,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Liquidity trap; Economics; Market liquidity; Zero lower bound; Pessimism; Trap (plumbing); Deflation; Crossover; Monetary economics; Keynesian economics; Mathematical economics; Econometrics; Monetary policy; Liquidity risk; Computer science; Physics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001393878,0.0002262839,0.000320909,0.00021314,0.0004995,0.001318166,0.0003864786,0.001048801,0.00280643],"category_scores_gemma":[0.008528567,0.0001509449,0.0003339508,0.000149071,0.001376936,0.001249072,0.001084149,0.001267233,0.0002627614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005040896,"about_ca_system_score_gemma":0.0006612366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008525318,"about_ca_topic_score_gemma":0.0008018188,"domain_scores_codex":[0.9995345,0.0002269141,0.00001797646,0.00007440357,0.00005713431,0.00008908518],"domain_scores_gemma":[0.9972058,0.001570638,0.0005836241,0.0002244517,0.0001655769,0.0002498683],"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":[0.0005304309,0.0002652179,0.01954687,0.0001399001,0.0002218801,0.001156199,0.000953931,0.3377212,0.0179328,0.5585889,0.005926401,0.05701627],"study_design_scores_gemma":[0.00008940081,0.0002130643,0.002088568,0.00002025535,0.00002178494,0.0001978673,0.0002818359,0.5798491,0.002187314,0.4130142,0.002009045,0.00002750053],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7425626,0.0001529387,0.233087,0.005347671,0.00008488809,0.0000399641,0.00009950371,0.0002597083,0.01836579],"genre_scores_gemma":[0.9918046,0.00004859089,0.006402301,0.0001788895,0.00001647964,0.00001875426,0.00001965029,0.00001044331,0.001500358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00280643,"threshold_uncertainty_score":0.009388447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3839133100545639,"score_gpt":0.4611704691017848,"score_spread":0.07725715904722086,"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."}}