{"id":"W590896683","doi":"10.2139/ssrn.2509425","title":"Near-Optimal Bisection Search for Nonparametric Dynamic Pricing with Inventory Constraint","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Nonparametric statistics; Bisection; Constraint (computer-aided design); Dynamic pricing; Mathematical optimization; Bisection method; Econometrics; Dynamic programming; Economics; Computer science; Mathematics; Mathematical economics; Microeconomics","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":[0.005143004,0.001429457,0.00402633,0.002921829,0.001124543,0.00267948,0.003274646,0.00383465,0.007797709],"category_scores_gemma":[0.02832328,0.002512466,0.001346707,0.002700583,0.002265906,0.004669675,0.003593371,0.002916126,0.0008594021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001778083,"about_ca_system_score_gemma":0.002818119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007286517,"about_ca_topic_score_gemma":0.004947131,"domain_scores_codex":[0.9982153,0.001193555,0.00007569913,0.00018815,0.0001881941,0.0001391046],"domain_scores_gemma":[0.9810854,0.01646379,0.0007169191,0.0005092801,0.0007674771,0.0004570851],"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":[0.0003399951,0.0001389495,0.0005800731,0.0001149502,0.00007218596,0.00004251838,0.00006202306,0.9571026,0.0003008776,0.02296129,0.00114013,0.0171444],"study_design_scores_gemma":[0.00001791491,0.00001738174,0.00003525509,0.000006956068,0.000003822192,0.000005507659,0.000006739526,0.9908056,0.00003973248,0.008974953,0.00008098395,0.000005079276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03791459,0.0005465102,0.956787,0.0004403308,0.00005514869,0.00009745381,0.0001476909,0.0004157589,0.003595464],"genre_scores_gemma":[0.6694736,0.0005025444,0.321237,0.0004048272,0.0001134488,0.0005057117,0.0007046757,0.0004879247,0.006570111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007797709,"threshold_uncertainty_score":0.02719909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118005355169397,"score_gpt":0.2404702439244926,"score_spread":0.2292901903727987,"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."}}