{"id":"W2086702508","doi":"10.1007/s00291-005-0026-z","title":"Semi-Markov information model for revenue management and dynamic pricing","year":2006,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Revenue management; Yield management; Revenue; Dynamic pricing; Computer science; Variable (mathematics); Markov decision process; Markov chain; Operations research; Business; Yield (engineering); Microeconomics; Markov process; Marketing; Economics; Finance; Mathematics","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":[0.005561126,0.002103178,0.004983504,0.002000117,0.00146803,0.004672484,0.005237477,0.00522938,0.01892775],"category_scores_gemma":[0.01646662,0.002333664,0.002439266,0.003050598,0.003302755,0.009806622,0.002381186,0.005633253,0.002472784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004995648,"about_ca_system_score_gemma":0.003471288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0188615,"about_ca_topic_score_gemma":0.0124606,"domain_scores_codex":[0.9971281,0.001144378,0.0001399728,0.000501478,0.0005216372,0.0005644251],"domain_scores_gemma":[0.9837194,0.01311121,0.0009371272,0.0006894628,0.001072956,0.000469902],"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":[0.0001585056,0.0001121673,0.0003858789,0.0001091533,0.00007776608,0.0001877718,0.0001056763,0.5373976,0.0003271522,0.4489309,0.004562119,0.007645326],"study_design_scores_gemma":[0.00001890617,0.00001167879,0.00006491864,0.00001083729,0.00001399354,0.00001835352,0.000009383355,0.894091,0.0000473179,0.1052528,0.0004430932,0.00001775947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02408949,0.001246864,0.9594421,0.00256996,0.0002430414,0.0001126736,0.001497079,0.000542891,0.01025585],"genre_scores_gemma":[0.8653962,0.00283119,0.06990115,0.0008763957,0.0006623478,0.0005691517,0.002166112,0.0003386935,0.05725883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01892775,"threshold_uncertainty_score":0.06331962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008316954873898075,"score_gpt":0.2042421732937018,"score_spread":0.1959252184198037,"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."}}