Paper Info
Title
Calibration of a stochastic agent-based model for re-hospitalization numbers of psychiatric patients
Abstract
Calibration is a vital part of the modeling and simulation process and denotes the determination of parameter values by estimating them from comparison between simulation results with reference data. During recent decades, a lot of algorithms have been developed for this purpose which are able to fit mentioned parameter values generically without information about the modelled system or the simulation. Especially for stochastic simulation models these routines very often require thousands of iterative simulation executions which, in case of large agent-based models, might be too time intensive. In this paper, we illustrate a real-world example for such a problem and present a solution for it based on probability theory. Hereby we not only calibrate the desired parameters, but also find a measure for the quality of the fit as well. By presenting this example we want to motivate modelers to analyze agent-based models to save costly calibration time.
Year
DOI
Venue
2017
10.5555/3242181.3242434
WSC '17: Winter Simulation Conference Las Vegas Nevada December, 2017
Field
DocType
ISSN
Reference data (financial markets),Stochastic simulation,Data modeling,Mathematical optimization,Agent-based model,Computer science,Simulation,Modeling and simulation,Probability theory,Calibration
Conference
0891-7736
ISBN
Citations 
PageRank 
978-1-5386-3427-1
0
0.34
References 
Authors
3
5
Name
Order
Citations
PageRank
Martin Bicher112.07
Urach, C.202.03
Günther Zauner383.90
Claire Rippinger400.68
Nikolas Popper501.35