{"id":"W4366456817","doi":"10.48550/arxiv.2304.08497","title":"Agent-Based Modeling and its Tradeoffs: An Introduction & Examples","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Immunization Research Network","keywords":"Computer science; Scalability; Context (archaeology); Population; Representation (politics); Ordinary differential equation; Agent-based model; Artificial intelligence; Distributed computing; Differential equation; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002711653,0.001534401,0.001227029,0.001695833,0.0009426352,0.003311779,0.001564097,0.00258011,0.006304654],"category_scores_gemma":[0.005632292,0.0008911539,0.001174074,0.002860083,0.002055844,0.00467665,0.002506784,0.005158811,0.00247044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001491486,"about_ca_system_score_gemma":0.0007556191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002213391,"about_ca_topic_score_gemma":0.002285048,"domain_scores_codex":[0.998808,0.0004687452,0.00009078166,0.00013344,0.0004450589,0.00005395888],"domain_scores_gemma":[0.9969673,0.00227942,0.0001236854,0.0001736236,0.0003369757,0.0001191035],"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":[0.00002406395,0.0001415224,0.0004194605,0.000776852,0.00004836885,0.0003516668,0.0002961133,0.02469326,0.0004311566,0.8288675,0.03610092,0.1078492],"study_design_scores_gemma":[0.00000821534,0.00005786272,0.0002638288,0.0004893123,0.00002029825,0.0004505737,0.0001047603,0.0412914,0.0002004067,0.6425598,0.3145081,0.0000455398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002851078,0.326621,0.5422794,0.01837173,0.003843254,0.0001565054,0.0002381623,0.0004018565,0.1052371],"genre_scores_gemma":[0.06909651,0.4463885,0.416765,0.004517084,0.008143393,0.0005894665,0.0003502126,0.0004890954,0.0536608],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006304654,"threshold_uncertainty_score":0.02109116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2662075998125162,"score_gpt":0.2579465911074399,"score_spread":0.008261008705076345,"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."}}