{"id":"W2278457050","doi":"10.1007/s40995-017-0206-0","title":"Multi-period Multi-criteria (MPMC) Valuation of American Options Based on Entropy Optimization Principles","year":2017,"lang":"en","type":"article","venue":"Iranian Journal of Science and Technology Transactions A Science","topic":"Capital Investment and Risk Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Monte Carlo method; Valuation (finance); Valuation of options; Entropy (arrow of time); Principle of maximum entropy; Mathematical optimization; Computer science; Monte Carlo methods for option pricing; Popularity; Probability distribution; Mathematics; Econometrics; Statistics; Economics; Finance","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":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001670154,0.0001027247,0.0002899004,0.002164182,0.00144011,0.000228298,0.0008057629,0.00004724077,0.00004065403],"category_scores_gemma":[0.0003946439,0.00009440838,0.00007362839,0.001675264,0.007847155,0.001198258,0.00002020189,0.0001585307,0.000004395085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122045,"about_ca_system_score_gemma":0.0002345348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004304549,"about_ca_topic_score_gemma":0.00002666655,"domain_scores_codex":[0.998738,0.000008690197,0.0005173467,0.0003140876,0.000190798,0.0002310687],"domain_scores_gemma":[0.9980753,0.00001372868,0.0009585072,0.0003865105,0.0004575366,0.0001084317],"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":[0.0001947836,0.003955631,0.109849,0.00007605433,0.000196388,0.00004082851,0.00558072,0.2060734,0.2889814,0.3417646,0.00001204527,0.04327511],"study_design_scores_gemma":[0.000979725,0.0004722943,0.05439431,0.00004611507,0.00003363138,0.00002773307,0.0008573923,0.9365571,0.005236058,0.001119394,0.0001041839,0.0001721102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.629469,0.0004076728,0.3656484,0.003790793,0.0003519742,0.0001512749,0.00003059422,0.00001931234,0.0001310566],"genre_scores_gemma":[0.906612,0.0004393414,0.09286843,0.00002798193,0.000008891463,0.000003690669,2.35913e-7,0.000004374991,0.00003503918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7304837,"threshold_uncertainty_score":0.9998599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05914903793499939,"score_gpt":0.2873493212281539,"score_spread":0.2282002832931545,"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."}}