{"id":"W4399480296","doi":"10.1016/j.trb.2024.102980","title":"Combining time dependency and behavioral game: A Deep Markov Cognitive Hierarchy Model for human-like discretionary lane changing modeling","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Traffic control and management","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Dependency (UML); Hierarchy; Markov chain; Computer science; Markov model; Cognition; Artificial intelligence; Cognitive psychology; Psychology; Machine learning; Economics; Neuroscience","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":[0.002427709,0.00018042,0.0002691426,0.0003342519,0.0002000326,0.0000834875,0.0001063464,0.0001241717,0.0001062662],"category_scores_gemma":[0.00004684679,0.0001633509,0.00009964171,0.0002530114,0.00008859114,0.0001674331,0.00001701333,0.000440646,0.000009676315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003713723,"about_ca_system_score_gemma":0.00002020332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001384634,"about_ca_topic_score_gemma":0.00006974876,"domain_scores_codex":[0.9980953,0.0002415617,0.0003186332,0.0004127261,0.0003787958,0.0005529898],"domain_scores_gemma":[0.9987601,0.0008975066,0.00001286436,0.00009518937,0.00009567194,0.0001386511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004931074,0.0001929089,0.0001524913,0.001070274,0.0002937856,0.0002551746,0.00865557,0.7994449,0.00542497,0.02631788,0.0002154941,0.1574835],"study_design_scores_gemma":[0.000559537,0.0001448342,0.0004638048,0.0001205704,0.00007802143,0.000001966471,0.000438767,0.9887848,0.0000203478,0.009054516,0.0001385052,0.0001943661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2615503,0.0008946765,0.7360673,0.0001089691,0.00007687771,0.0006577965,0.00008577538,0.0003944502,0.0001638644],"genre_scores_gemma":[0.976781,0.0001884811,0.02146771,0.00001800362,0.00007899853,0.0006255143,0.0004600876,0.00003933518,0.0003409103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7152307,"threshold_uncertainty_score":0.6661255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2731552465910297,"score_gpt":0.4403721658021479,"score_spread":0.1672169192111183,"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."}}