{"id":"W1985900815","doi":"10.1287/mnsc.2014.2027","title":"Correcting for Misspecification in Parameter Dynamics to Improve Forecast Accuracy with Adaptively Estimated Models","year":2015,"lang":"en","type":"article","venue":"Management Science","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Queen's University","funders":"University of California, Davis","keywords":"Computer science; Variation (astronomy); Econometrics; Process (computing); Variety (cybernetics); Chebyshev filter; Estimation theory; Estimation; Mathematical optimization; Mathematics; Artificial intelligence; Algorithm; Economics","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":[0.004690855,0.0007937499,0.0009133196,0.000808396,0.0004311619,0.00105775,0.001214909,0.001214685,0.00123122],"category_scores_gemma":[0.03509569,0.0005073621,0.0007416677,0.0008110405,0.0004922433,0.001843434,0.001225428,0.002511308,0.0003300633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005773506,"about_ca_system_score_gemma":0.001379638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007169434,"about_ca_topic_score_gemma":0.006334811,"domain_scores_codex":[0.9984288,0.0008237814,0.0001127483,0.0002602566,0.0002671031,0.0001074114],"domain_scores_gemma":[0.9829354,0.01306481,0.0009129958,0.002058149,0.0009119307,0.0001165614],"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":[0.0001416443,0.00009163373,0.01021645,0.00007366988,0.0001830173,0.0001087678,0.000245926,0.7640713,0.004391692,0.01807005,0.001788624,0.2006171],"study_design_scores_gemma":[0.000008051902,0.00002818415,0.0008817154,0.000008173827,0.00001351263,0.00001951956,0.00001043225,0.9945378,0.0006876427,0.003321922,0.0004733724,0.000009641351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05907531,0.0003837289,0.9381218,0.0005030297,0.00009177737,0.00003440916,0.00004483822,0.0005190629,0.001226107],"genre_scores_gemma":[0.8096927,0.0003092691,0.1881756,0.0001797151,0.00009268548,0.00005748406,0.0001590906,0.00009211526,0.001241292],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007169434,"threshold_uncertainty_score":0.02480793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2883996944256034,"score_gpt":0.4015171285851785,"score_spread":0.1131174341595751,"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."}}