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Complexity and Chaos Analysis of a Predator-Prey Ecosystem Simulation

Authors:
Yasaman Majdabadi Farahani
Abbas Golestani
Robin Gras

Keywords: agent-based ecosystem; chaos analysis; complexity analysis; Markov chain

Abstract:
We investigated the complexity level of an agent-based predator/prey ecosystem simulation. The variations of the times series associated to this ecosystem simulation are the result of complex simulation mechanisms. For the purpose of understanding how close our system is to random or chaotic processes, we compare these data with data generated by a Markov chain as a simple process. The parameters of the corresponding Markov matrix are learned from the data generated by our simulation. Then we used the Markov chain to generate data similar to those of the simulation. We show that the Markov chain for all three orders, which we tested, generated prey and predator time series that are more random than their counterpart in the original simulation. Also, we used the largest Lyapunov exponent to determine the chaotic behavior of the simulation. We discuss the largest Lyapunov exponent values for population time series of both prey and predator agents, which indicates chaotic behavior in our agent-based ecosystem simulation.

Pages: 52 to 59

Copyright: Copyright (c) IARIA, 2010

Publication date: November 21, 2010

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-108-3

Location: Lisbon, Portugal

Dates: from November 21, 2010 to November 26, 2010