{"id":"W1491755126","doi":"10.1023/a:1011149422763","title":"Lottery Decisions and Probability Weighting Function","year":2001,"lang":"en","type":"article","venue":"Journal of Risk and Uncertainty","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Lottery; Argument (complex analysis); Mathematical economics; Function (biology); Weighting; Preference; Economics; Econometrics; Computer science; 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":[0.01401656,0.0009561227,0.002165366,0.002131083,0.00077858,0.003639521,0.001672062,0.002801401,0.01146719],"category_scores_gemma":[0.06365202,0.0009144982,0.00133643,0.001672055,0.002457648,0.007863002,0.001406814,0.002181179,0.0007995043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001468569,"about_ca_system_score_gemma":0.0009584717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009001241,"about_ca_topic_score_gemma":0.0007435803,"domain_scores_codex":[0.9936656,0.004626682,0.0002705812,0.0004692054,0.0005673797,0.000400467],"domain_scores_gemma":[0.9406396,0.05329736,0.001715754,0.00206338,0.00136817,0.0009157265],"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":[0.0003588267,0.000231535,0.001665275,0.0001560359,0.0001464696,0.0001120228,0.000288148,0.03964362,0.0004531234,0.9171586,0.002074917,0.03771139],"study_design_scores_gemma":[0.0000540179,0.00007318644,0.0006907925,0.00002649619,0.00004666312,0.0001099523,0.00007319789,0.1163077,0.0001239411,0.881695,0.0007659381,0.00003314144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1994302,0.001383002,0.7718954,0.003315753,0.0002138348,0.0002747139,0.0002551449,0.0001818236,0.02305009],"genre_scores_gemma":[0.8851631,0.00109508,0.09963025,0.0004133398,0.0002095112,0.0004380174,0.0002073262,0.00007580975,0.01276754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01401656,"threshold_uncertainty_score":0.0741275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08839251925516983,"score_gpt":0.3476835833279564,"score_spread":0.2592910640727866,"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."}}