{"id":"W2737990324","doi":"","title":"Pricing strategies of complementary products in distribution channels: A dynamic approach","year":2015,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Merger and Competition Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Product (mathematics); Business; Dynamic pricing; Channel (broadcasting); Microeconomics; Margin (machine learning); Transfer pricing; Differential game; Industrial organization; Pricing strategies; Differential (mechanical device); Marketing; Economics; Computer science; 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":[0.002050534,0.0007449229,0.001321489,0.00166532,0.0007955414,0.003197201,0.00177638,0.001869496,0.006589422],"category_scores_gemma":[0.008146503,0.0008755474,0.00119753,0.0008279087,0.002473974,0.004091521,0.00164914,0.001312083,0.0003620623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002789637,"about_ca_system_score_gemma":0.001167226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004586038,"about_ca_topic_score_gemma":0.002828488,"domain_scores_codex":[0.9986101,0.0005926894,0.00003127902,0.0001804913,0.0001902928,0.0003950197],"domain_scores_gemma":[0.9939244,0.00424306,0.0007981347,0.0001832058,0.0003582927,0.0004928813],"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":[0.0002888084,0.0002515076,0.004381683,0.0001240491,0.00009602258,0.0005835548,0.00033005,0.8021535,0.002942192,0.1730747,0.001092458,0.01468132],"study_design_scores_gemma":[0.00003085768,0.00007423115,0.0008381546,0.00001071374,0.00002878233,0.0001022433,0.0002170313,0.9549384,0.000331431,0.04251657,0.0008805319,0.00003089652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6985817,0.0006591831,0.2751775,0.0009174973,0.00004934438,0.00021732,0.0001821587,0.00008391609,0.02413142],"genre_scores_gemma":[0.9886056,0.0001647656,0.006844902,0.00003881867,0.00002140525,0.0000450435,0.00002768743,0.00001151807,0.004240222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006589422,"threshold_uncertainty_score":0.02204376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03478521967888057,"score_gpt":0.2203054293694739,"score_spread":0.1855202096905933,"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."}}