Hallier, M. and Hartmann, C. (2016) Constructing Markov State Models of Reduced Complexity from AgentBased Simulation Data. Social Simulation Conference 2016 . (Submitted)

PDF
640kB 
Abstract
Agentbased models usually are very complex so that models of re duced complexity are needed, not only to see the wood for the trees but also to allow the application of advanced analytic methods. We show how to construct socalled Markov state models that approximate the origi nal Markov process by a Markov chain on a small finite state space and represent well the longest time scales of the original model. More specif ically, a Markov state model is defined as a Markov chain whose state space consists of sets of population states near which the sample paths of the original Markov process reside for a long time and whose transition rates between these macrostates are given by the aggregate statistics of jumps between those sets of population states. An advantage of this ap proach in the context of complex models with large state spaces is that the macrostates as well as transition probabilities can be estimated on the basis of simulated shortterm trajectory data.
Item Type:  Article 

Subjects:  Mathematical and Computer Sciences > Operational Research > Operational Research not elsewhere classified 
Divisions:  Department of Mathematics and Computer Science > Institute of Mathematics > Cellular Mechanics Group Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group 
ID Code:  1899 
Deposited By:  Carsten Hartmann 
Deposited On:  14 Apr 2016 07:20 
Last Modified:  03 Mar 2017 14:42 
Repository Staff Only: item control page