{"id":"W4414592730","doi":"10.48550/arxiv.2509.09865","title":"Linear fractional relative risk aversion","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Social Sciences and Humanities Research Council of Canada","keywords":"Monopolistic competition; Constant (computer programming); Risk aversion (psychology); Aggregate (composite); Isoelastic utility; Marginal utility; Competition (biology); Measure (data warehouse)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001744245,0.0002810705,0.000423315,0.0005046013,0.000363994,0.0001142371,0.0007843846,0.0005484911,0.001271627],"category_scores_gemma":[0.003728906,0.0002284549,0.0003315498,0.0006945428,0.00009509208,0.0003940911,0.0008698424,0.001214571,0.002810466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001038341,"about_ca_system_score_gemma":0.0003564598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002344848,"about_ca_topic_score_gemma":0.00001991431,"domain_scores_codex":[0.9963901,0.0004119431,0.0008091887,0.0009567199,0.001200326,0.0002317389],"domain_scores_gemma":[0.9958991,0.001396585,0.0009234393,0.001013481,0.0006470243,0.0001203796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005804181,0.0000558168,0.8911849,0.000004051132,0.0000742156,0.000007959942,0.0003861794,0.07746185,0.000005609639,0.0003516906,0.02471143,0.005698275],"study_design_scores_gemma":[0.000476954,0.0000503639,0.6098516,0.000117579,0.0001606043,0.000002812801,0.0002504253,0.04925853,0.0003638859,0.04598298,0.2929718,0.0005124949],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7776093,0.0005575895,0.1575226,0.001566156,0.005821591,0.0005515899,0.0003730822,0.0001746594,0.05582338],"genre_scores_gemma":[0.8958437,0.0055083,0.01173464,0.000477776,0.0009421515,0.00003771581,0.0003125768,0.00002731881,0.08511578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2813333,"threshold_uncertainty_score":0.9996414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191778333748204,"score_gpt":0.3950923346295649,"score_spread":0.2759145012547445,"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."}}