GPA: A Multiformalism, Multisolution Approach to Efficient Analysis of Large-Scale Population Models

GPA: A Multiformalism, Multisolution Approach to Efficient Analysis of Large-Scale Population Models

Jeremy T. Bradley (Imperial College London, UK), Marcel C. Guenther (Imperial College London, UK), Richard A. Hayden (Imperial College London, UK) and Anton Stefanek (Imperial College London, UK)
Copyright: © 2014 |Pages: 26
DOI: 10.4018/978-1-4666-4659-9.ch008
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Abstract

This chapter discusses the latest trends and developments in performance analysis research of large population models. In particular, it reviews GPA, a state-of-the-art Multiformalism, Multisolution (MFMS) tool that provides a framework for the implementation of various population modelling formalisms and solution methods.
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2 Background

Population models describe interactions between individuals, which are grouped into populations. Individuals can represent a number of different entities or agents such as people, telecommunication equipment or vehicles to name but a few. While the individual behaviour of agents can be described using a small set of rules, the simulation of population models becomes infeasible when looking at the interaction of thousands or millions of individuals. However, when grouping a large number of individuals into populations, it is possible to evaluate the effects of interactions using efficient mean-field analysis techniques rather than simulation.

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