Skip to main content
Kent Academic Repository

Stability of evolving multiagent systems

De Wilde, Philippe, Briscoe, G. (2011) Stability of evolving multiagent systems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 41 (4). pp. 1149-1157. ISSN 1083-4419. (doi:10.1109/TSMCB.2011.2110642) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:93341)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:


A multiagent system is a distributed system where the agents or nodes perform complex functions that cannot be written down in analytic form. Multiagent systems are highly connected, and the information they contain is mostly stored in the connections. When agents update their state, they take into account the state of the other agents, and they have access to those states via the connections. There is also external user-generated input into the multiagent system. As so much information is stored in the connections, agents are often memory less. This memory-less property, together with the randomness of the external input, has allowed us to model multiagent systems using Markov chains. In this paper, we look at multiagent systems that evolve, i.e., the number of agents varies according to the fitness of the individual agents. We extend our Markov chain model and define stability. This is the start of a methodology to control multiagent systems. We then build upon this to construct an entropy-based definition for the degree of instability (entropy of the limit probabilities), which we used to perform a stability analysis. We then investigated the stability of evolving agent populations through simulation and show that the results are consistent with the original definition of stability in nonevolving multiagent systems, proposed by Chli and De Wilde. This paper forms the theoretical basis for the construction of digital business ecosystems, and applications have been reported elsewhere.

Item Type: Article
DOI/Identification number: 10.1109/TSMCB.2011.2110642
Uncontrolled keywords: Agent, entropy, evolution, population, stability
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Philippe De Wilde
Date Deposited: 03 Jan 2023 15:34 UTC
Last Modified: 04 Jan 2023 14:36 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.