Optimisation in ‘Self-modelling’ Complex Adaptive Systems

Watson, Richard A., Buckley, C. L. and Mills, Rob (2011) Optimisation in ‘Self-modelling’ Complex Adaptive Systems. Complexity, 16, (5), 17-26.


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When a dynamical system with multiple point attractors is released from an arbitrary initial condition it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimises these constraints by this method is unlikely, or may take many attempts. Here we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimise total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ‘recalling’ low energy states that have been previously visited but ‘predicting’ their location by generalising over local attractor states that have already been visited. This ‘self-modelling’ framework, i.e. a system that augments its behaviour with an associative memory of its own attractors, helps us better-understand the conditions under which a simple locally-mediated mechanism of self-organisation can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph colouring and distributed task allocation problems.

Item Type: Article
Related URLs:
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > EEE
Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
ePrint ID: 271051
Accepted Date and Publication Date:
23 May 2011Published
Date Deposited: 10 May 2010 15:04
Last Modified: 31 Mar 2016 14:18
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/271051

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