{"id":"W2912328562","doi":"10.5555/3320516.3320595","title":"A symmetric formalism for discrete event simulation with agents","year":2018,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"DEVS; Formalism (music); Initialization; Dataflow; Computer science; Theoretical computer science; Discrete event simulation; Distributed computing; Modeling and simulation; Algorithm; Parallel computing; Programming language; Simulation","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":[0.004035263,0.0008190274,0.0007934115,0.0009912971,0.0009546694,0.002751797,0.002199595,0.001153822,0.004760547],"category_scores_gemma":[0.004079188,0.0006918322,0.002205823,0.001283239,0.002251771,0.003979602,0.002388923,0.003089802,0.001708736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001462952,"about_ca_system_score_gemma":0.003017347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002665812,"about_ca_topic_score_gemma":0.003075659,"domain_scores_codex":[0.9978825,0.000805021,0.0003388239,0.0002507499,0.0005923068,0.0001305504],"domain_scores_gemma":[0.9979485,0.0008487585,0.0001760594,0.000577509,0.0003305313,0.0001187822],"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":[0.000006378541,0.000007141026,0.00006322228,0.00002975311,0.000006067906,0.00005055858,0.0001061514,0.007883756,0.0004728084,0.9855759,0.0007637432,0.005034613],"study_design_scores_gemma":[0.00003125537,0.00003474978,0.00005051224,0.00006375998,0.00002182643,0.0001445951,0.00006573189,0.1234255,0.001905376,0.7964172,0.07781496,0.00002457904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007036459,0.00007522894,0.9935414,0.0002098618,0.00005772444,0.00006255271,0.0001201362,0.0002140479,0.005015492],"genre_scores_gemma":[0.07423113,0.0005757008,0.9164936,0.0003647602,0.0001387467,0.0005531714,0.0005608952,0.0002042275,0.006877802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004760547,"threshold_uncertainty_score":0.02134079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2016763612490777,"score_gpt":0.4719978926340083,"score_spread":0.2703215313849305,"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."}}