{"id":"W4389841248","doi":"10.5267/j.dsl.2023.11.003","title":"Analyzing the interrelations among investors’ behavioral biases using an integrated DANP method","year":2023,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Loss aversion; Overconfidence effect; Regret; Anchoring; Preference; Econometrics; Herd behavior; Behavioral economics; Prospect theory; Psychology; Risk aversion (psychology); Disposition effect; Risk-seeking; Proxy (statistics); Ambiguity; Economics; Microeconomics; Social psychology; Expected utility hypothesis; Statistics; Computer science; Mathematics; Herding; Financial economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01747868,0.0003425522,0.0004826799,0.00299618,0.001939568,0.003121266,0.004306189,0.0001252906,0.0002560958],"category_scores_gemma":[0.008992063,0.0002099489,0.0002953688,0.01292056,0.001700136,0.003536011,0.0008846008,0.0005301195,0.0005947207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002900183,"about_ca_system_score_gemma":0.0002536463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005159936,"about_ca_topic_score_gemma":0.0002636257,"domain_scores_codex":[0.9931212,0.0005392334,0.001460378,0.001527532,0.00255614,0.0007954833],"domain_scores_gemma":[0.9911683,0.005120176,0.000609859,0.002132378,0.000522645,0.000446615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002380778,0.00009362064,0.06312701,2.896908e-7,0.000005686859,0.0000565564,0.002023096,0.0592434,0.0186921,0.00009306845,0.006997373,0.849644],"study_design_scores_gemma":[0.0005607903,0.000181439,0.1754141,0.000192497,0.00009627814,0.00009908059,0.01635314,0.773052,0.001783709,0.02375145,0.007477459,0.001037969],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8302935,0.000008512039,0.1664956,0.00096917,0.001777593,0.0002114345,0.00002282121,0.000169313,0.00005206662],"genre_scores_gemma":[0.9665573,0.000002878583,0.03231486,0.0008912845,0.0001046267,0.00001295828,0.00001008115,0.00002986049,0.00007615135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.848606,"threshold_uncertainty_score":0.9993598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.397188947689401,"score_gpt":0.5075228894466369,"score_spread":0.1103339417572359,"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."}}