{"id":"W2762430938","doi":"10.1002/aic.15995","title":"A dynamic game theoretic framework for process plant competitive upgrade and production planning","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Process Optimization and Integration","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; McMaster University","keywords":"Upgrade; Production (economics); Oligopoly; Nash equilibrium; Competition (biology); Sequential game; Time horizon; Cournot competition; Game theory; Strategy; Process (computing); Economics; Competitor analysis; Computer science; Mathematical optimization; Mathematical economics; Microeconomics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001481333,0.00009012615,0.00009864521,0.00005327166,0.0002922604,0.0002252968,0.00010745,0.00006448438,0.00001393474],"category_scores_gemma":[0.0003436145,0.00007569557,0.0000215528,0.00002470115,0.00004802332,0.0003463949,0.00000729662,0.0002573158,0.000001445626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003045301,"about_ca_system_score_gemma":0.0000180133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.602363e-7,"about_ca_topic_score_gemma":0.000001451097,"domain_scores_codex":[0.9995603,0.00000828327,0.0001317309,0.00008899906,0.00008164257,0.0001290374],"domain_scores_gemma":[0.9996465,0.00002868545,0.00009957833,0.00009058894,0.00008277196,0.000051862],"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":[0.001024226,0.0002946758,0.01373129,0.002641148,0.0009284003,0.00006124654,0.04886977,0.5660552,0.009116793,0.2822052,0.006742707,0.06832929],"study_design_scores_gemma":[0.000603582,0.0001253876,0.00473777,0.0008761972,0.00005771445,0.0004007603,0.001550089,0.8966492,0.004789071,0.08883118,0.001056053,0.0003229499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2282068,0.0009430276,0.7662486,0.001589765,0.0009998289,0.0003074771,0.00001654807,0.0001363008,0.001551569],"genre_scores_gemma":[0.9909176,0.0004122735,0.008393013,0.00003774146,0.0001585888,0.00001563408,0.000007863237,0.0000184063,0.0000389121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7627108,"threshold_uncertainty_score":0.3086776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285639950054761,"score_gpt":0.2889729381107131,"score_spread":0.2761165386101655,"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."}}