{"id":"W3185935516","doi":"10.1115/1.4051890","title":"Implications of Competitor Representation for Profit-Maximizing Design","year":2021,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Natural Sciences and Engineering Research Council of Canada; Carnegie Mellon University","keywords":"Competitor analysis; Logit; Mixed logit; Profit (economics); Econometrics; Computer science; Logistic regression; Representation (politics); Market share; Mathematical optimization; Mathematics; Economics; Microeconomics; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001245995,0.00006978593,0.0003225871,0.00007870651,0.00004817033,0.00002020673,0.0001303151,0.00006845822,0.0002184132],"category_scores_gemma":[0.0003917224,0.00007756959,0.0001627927,0.00009257843,0.00001719471,0.0001920467,0.00002123815,0.0000786348,0.00002446035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001049142,"about_ca_system_score_gemma":0.0000443775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002400526,"about_ca_topic_score_gemma":3.698541e-7,"domain_scores_codex":[0.9987618,0.00005843197,0.0008794226,0.0001541499,0.00003614838,0.0001100145],"domain_scores_gemma":[0.9985203,0.0003374104,0.0008458493,0.0001582965,0.00007965245,0.00005849597],"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":[0.0004245935,0.0004601138,0.02283229,0.0001088749,0.0004975539,0.000007364291,0.0004420158,0.0362036,0.1124355,0.808675,0.006914823,0.01099822],"study_design_scores_gemma":[0.003387291,0.0009243707,0.04823386,0.0000880677,0.0001054565,0.00008987602,0.0003648733,0.03524873,0.2073631,0.7024384,0.001382628,0.0003734005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01895169,0.0004259998,0.9787903,0.001009913,0.0002748834,0.0002331902,0.00001856512,0.00000370589,0.0002917546],"genre_scores_gemma":[0.7467999,0.0001922625,0.2526262,0.0001058391,0.0001198737,0.00001875898,0.000005852113,0.00001430714,0.0001169324],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7278482,"threshold_uncertainty_score":0.3163196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3213587236906792,"score_gpt":0.2900951325003641,"score_spread":0.03126359119031513,"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."}}