{"id":"W2123179754","doi":"10.5555/1838206.1838325","title":"Preference elicitation for risky prospects","year":2010,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Regret; Preference elicitation; Cumulative prospect theory; Preference; Expected utility hypothesis; Utility theory; Prospect theory; Minimax; Decision theory; Game theory; Computer science; Subjective expected utility; Economics; Mathematical economics; Microeconomics; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0001651285,0.00006992977,0.00006637922,0.00003419305,0.0000938263,0.0001367593,0.000434432,0.0000518718,0.00001354622],"category_scores_gemma":[0.00007451783,0.00005774223,0.00002697519,0.0001143902,0.00001776028,0.0002980471,0.00004570844,0.0001102935,0.00005244604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004684016,"about_ca_system_score_gemma":0.0000421173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001841428,"about_ca_topic_score_gemma":0.00003390595,"domain_scores_codex":[0.9993727,0.000007228892,0.0001068096,0.0002439931,0.0001039835,0.0001652726],"domain_scores_gemma":[0.9993975,0.00006420587,0.00003466665,0.0003074228,0.0001352298,0.00006099676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001894727,0.00002146179,0.0001369636,0.000005759361,0.000001930631,1.666403e-7,0.000145489,0.00001615632,0.01434251,0.936592,0.0005724602,0.04816318],"study_design_scores_gemma":[0.0002902731,0.0001258191,0.004147982,0.000008113189,0.000003337583,0.00000492579,0.000008498963,0.6309227,0.02742615,0.3348207,0.002013966,0.0002275476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03112649,0.000005260985,0.9605826,0.000573169,0.0003303556,0.0001724764,9.394876e-7,0.000224738,0.006984012],"genre_scores_gemma":[0.7365351,0.000001344393,0.2626281,0.0001770922,0.00004400333,0.00004253438,0.000001092442,0.000003039316,0.000567735],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7054086,"threshold_uncertainty_score":0.235466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04079795695807046,"score_gpt":0.2768015047237196,"score_spread":0.2360035477656492,"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."}}