Systems Dynamics or Agent-Based Modelling for Immune Simulation?

Grazziela P Figueredo,Peer-Olaf Siebers,Uwe Aickelin

doi:10.2139/ssrn.2829280

Grazziela P Figueredo, Peer-Olaf Siebers + Show 1 more

Open Access

https://doi.org/10.2139/ssrn.2829280

Copy DOI

Journal: SSRN	Publication Date: Jan 1, 2011
Citations: 3	License type: cc-by

Affiliation: University of Nottingham, University of Melbourne

Abstract

In immune system simulation there are two competing simulation approaches: System Dynamics Simulation (SDS) and Agent-Based Simulation (ABS). In the literature there is little guidance on how to choose the best approach for a specific immune problem. Our overall research aim is to develop a framework that helps researchers with this choice. In this paper we investigate if it is possible to easily convert simulation models between approaches. With no explicit guidelines available from the literature we develop and test our own set of guidelines for converting SDS models into ABS models in a non-spacial scenario. We also define guidelines to convert ABS into SDS considering a non-spatial and a spatial scenario. After running some experiments with the developed models we found that in all cases there are significant differences between the results produced by the different simulation methods.

Highlights

Simulation presents paradigms that allow us to build models for various problem domains
We validate our Agent-Based Simulation (ABS) model by comparing its outputs to the outputs produced by the System Dynamics Simulation (SDS) model
We plotted the results for the first 60 days, where the simulations reach a steady-state.The outputs produced by the SDS model are the same as the mathematical model and different from the results produced by the ABS model

Summary

Introduction

Simulation presents paradigms that allow us to build models for various problem domains. Some of the important simulation approaches are System Dynamics Simulation (SDS) and Agent-Based Simulation (ABS). SDS is a continuous simulation approach that uses stocks, flows and feedback loops as concepts to study the behaviour of complex systems [1]. The models in SDS consist of a set of differential equations that are solved for a certain time interval [2]. ABS, on the other hand, is a modelling technique that employs autonomous agents that interact with each other. The agents’ behaviour is described by rules that determines how they learn, interact with each other and adapt. The overall system behaviour is given by the agents individual dynamics as well as their interactions.

Objectives

Results

Conclusion