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Optimisation in ‘Self-modelling’ Complex Adaptive Systems

Optimisation in ‘Self-modelling’ Complex Adaptive Systems
Optimisation in ‘Self-modelling’ Complex Adaptive Systems
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.
17-26
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Buckley, C. L.
403be04e-fca5-4f1c-b0c4-d84401f51d51
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Buckley, C. L.
403be04e-fca5-4f1c-b0c4-d84401f51d51
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7

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

Record type: Article

Abstract

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.

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Watson_Buckley_Mills_Complexity_in-press.pdf - Accepted Manuscript
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More information

Published date: 23 May 2011
Organisations: Agents, Interactions & Complexity, EEE

Identifiers

Local EPrints ID: 271051
URI: http://eprints.soton.ac.uk/id/eprint/271051
PURE UUID: 19605443-b486-4e04-a573-46483b36bd02
ORCID for Richard A. Watson: ORCID iD orcid.org/0000-0002-2521-8255

Catalogue record

Date deposited: 10 May 2010 15:04
Last modified: 15 Mar 2024 03:21

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Contributors

Author: Richard A. Watson ORCID iD
Author: C. L. Buckley
Author: Rob Mills

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