{"id":"W2165729504","doi":"10.1609/aimag.v33i4.2432","title":"TRUSTS: Scheduling Randomized Patrols for Fare Inspection in Transit Systems Using Game Theory","year":2012,"lang":"en","type":"article","venue":"AI Magazine","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Multidisciplinary University Research Initiative; National Science Foundation","keywords":"Stackelberg competition; Revenue; Operations research; Computer science; Scheduling (production processes); Novelty; Transit (satellite); Payment; Computer security; Transport engineering; Engineering; Public transport; Business; Economics; Operations management; Microeconomics; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002750958,0.001273033,0.001573055,0.0006591251,0.001081088,0.00120931,0.001925789,0.001220716,0.003595686],"category_scores_gemma":[0.01004654,0.0007706875,0.0009092751,0.0006068629,0.001208356,0.002152711,0.001388348,0.00140295,0.0003823167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002539752,"about_ca_system_score_gemma":0.004765092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02617523,"about_ca_topic_score_gemma":0.0265383,"domain_scores_codex":[0.9984872,0.0006156163,0.00006623796,0.0002912295,0.0002037714,0.000335877],"domain_scores_gemma":[0.9934406,0.004530455,0.0005063147,0.0004319327,0.0004158949,0.0006748341],"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":[0.0004559511,0.0002469037,0.001764104,0.00006375465,0.00005039394,0.00005462334,0.0001034778,0.9624694,0.001219995,0.006702165,0.001712969,0.02515637],"study_design_scores_gemma":[0.00004572932,0.00005928207,0.0001046119,0.000001824302,0.000005599654,0.00000514661,0.00001957572,0.9968486,0.0001630667,0.002552004,0.0001903568,0.000004156083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2559052,0.0003354269,0.7327793,0.0009085364,0.0001851858,0.000678372,0.0003158146,0.002910005,0.005982206],"genre_scores_gemma":[0.8736532,0.00007872005,0.1241849,0.0001262399,0.00003175822,0.0001965532,0.0001630402,0.0001389395,0.001426738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02617523,"threshold_uncertainty_score":0.05204576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415599737522137,"score_gpt":0.2565609762643894,"score_spread":0.242404978889168,"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."}}