{"id":"W2014726216","doi":"10.1016/j.ejor.2005.02.081","title":"Mathematical structure of a bilevel strategic pricing model","year":2007,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Merger and Competition Analysis","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Bilevel optimization; Oligopoly; Context (archaeology); Computer science; Schedule; Revenue; Operations research; Game theory; Microeconomics; Product (mathematics); Service (business); Revenue management; Mathematical optimization; Industrial organization; Mathematical economics; Business; Cournot competition; Economics; Optimization problem; Marketing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095669,0.000779752,0.00138333,0.001279071,0.0007871643,0.003415475,0.00187619,0.002862836,0.006769293],"category_scores_gemma":[0.004645761,0.0006671082,0.0009851918,0.001585819,0.002117182,0.003160544,0.002050539,0.002280831,0.0009628219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00136645,"about_ca_system_score_gemma":0.001786331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002796203,"about_ca_topic_score_gemma":0.001513126,"domain_scores_codex":[0.9994765,0.0001961134,0.00003052245,0.00007334317,0.0001449108,0.00007868341],"domain_scores_gemma":[0.9987389,0.0006302511,0.0001759505,0.00006942528,0.0002532257,0.0001321988],"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":[0.00001260716,0.00003180216,0.0001477219,0.00003732548,0.00001315797,0.00008633429,0.0000765264,0.1041156,0.0005217296,0.891459,0.0007296203,0.002768624],"study_design_scores_gemma":[0.00001305588,0.00001402808,0.00005595339,0.0000107573,0.000008818622,0.00005615942,0.00002492518,0.6368378,0.00008970982,0.3620211,0.0008513837,0.00001631459],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05848368,0.001130168,0.8921019,0.002946225,0.0001860667,0.0000659878,0.0002352234,0.0001788878,0.04467182],"genre_scores_gemma":[0.9013773,0.001461713,0.06395294,0.0004027759,0.0002631043,0.000234871,0.0002401798,0.0001045809,0.03196252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006769293,"threshold_uncertainty_score":0.02264553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1922904043859714,"score_gpt":0.3463435826363318,"score_spread":0.1540531782503604,"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."}}