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Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks

Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks
Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks
The natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a ‘self-modelling’ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks.
Hopfield networks, associative learning, dynamical systems, adaptive networks, constraint optimisation, modularity, nearly-decomposable systems, coarse-graining, self-organisation, canalisation.
227-249
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Buckley, C. L.
403be04e-fca5-4f1c-b0c4-d84401f51d51
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Buckley, C. L.
403be04e-fca5-4f1c-b0c4-d84401f51d51

Watson, Richard A., Mills, Rob and Buckley, C. L. (2011) Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks Adaptive Behavior, 19, (4), pp. 227-249.

Record type: Article

Abstract

The natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a ‘self-modelling’ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks.

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More information

Accepted/In Press date: 16 May 2011
Keywords: Hopfield networks, associative learning, dynamical systems, adaptive networks, constraint optimisation, modularity, nearly-decomposable systems, coarse-graining, self-organisation, canalisation.
Organisations: Agents, Interactions & Complexity, EEE

Identifiers

Local EPrints ID: 272116
URI: http://eprints.soton.ac.uk/id/eprint/272116
PURE UUID: 56298183-7f1b-48e4-b9c0-732a493728e6

Catalogue record

Date deposited: 24 Mar 2011 10:37
Last modified: 09 Nov 2017 22:45

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Contributors

Author: Rob Mills
Author: C. L. Buckley

University divisions

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