{"id":"W3124684010","doi":"","title":"Structural Analysis of Nonlinear Pricing","year":2014,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Nonlinear pricing; Nonparametric statistics; Estimator; Econometrics; Identification (biology); Quantile; Tariff; Nonlinear system; Inverse demand function; Computer science; Phone; Consumption (sociology); Pricing strategies; Economics; Mathematical optimization; Mathematics; Microeconomics; Statistics; Demand curve","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002196005,0.000307038,0.0009249508,0.002739681,0.0001347742,0.0002805927,0.0007863236,0.000291024,0.0003711611],"category_scores_gemma":[0.0004247443,0.0003224453,0.0004032939,0.0008323218,0.0001824376,0.0002095818,0.001817698,0.00113821,0.000008459861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001848683,"about_ca_system_score_gemma":0.0001246625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001656771,"about_ca_topic_score_gemma":0.001782615,"domain_scores_codex":[0.9974133,0.00007373127,0.0008294899,0.0007554065,0.0003276158,0.0006004539],"domain_scores_gemma":[0.9978796,0.0003855065,0.0004767645,0.0009761263,0.0002526582,0.00002932379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009752833,0.00005543894,0.5911538,0.0006325456,0.0006849832,0.00001274966,0.0001104925,0.03268542,0.0001390714,0.0003012043,0.00001939941,0.3741073],"study_design_scores_gemma":[0.0004736799,0.00000972017,0.41425,0.0002112375,0.0007127372,7.707836e-7,0.0002632226,0.573869,0.00002927694,0.0005326138,0.009041685,0.0006061232],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9435176,0.00004770634,0.000005272509,0.00008889553,0.0003686099,0.0003507712,0.00001916853,0.00003867982,0.05556331],"genre_scores_gemma":[0.9983408,0.0002588032,0.000304552,0.00008418228,0.0004690844,0.00003174421,0.0002020785,0.00004766466,0.0002610386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5411836,"threshold_uncertainty_score":0.9999228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03480504443196926,"score_gpt":0.3078915920994151,"score_spread":0.2730865476674458,"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."}}