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Global adaptation in networks of selfish components: emergent associative memory at the system scale

Global adaptation in networks of selfish components: emergent associative memory at the system scale
Global adaptation in networks of selfish components: emergent associative memory at the system scale
In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning.
self-organization, adaptive networks, hebbian learning, multi-agent systems, social networks, emergent computation, games on networks
147-166
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Buckley, C.L.
777ec49b-e6e7-4d52-b130-f7b6bcaf3166
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Buckley, C.L.
777ec49b-e6e7-4d52-b130-f7b6bcaf3166

Watson, Richard A., Mills, Rob and Buckley, C.L. (2011) Global adaptation in networks of selfish components: emergent associative memory at the system scale Artificial Life, 17, (3), Summer Issue, pp. 147-166.

Record type: Article

Abstract

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning.

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More information

e-pub ahead of print date: 13 July 2011
Published date: 2011
Keywords: self-organization, adaptive networks, hebbian learning, multi-agent systems, social networks, emergent computation, games on networks
Organisations: Agents, Interactions & Complexity, EEE

Identifiers

Local EPrints ID: 271443
URI: http://eprints.soton.ac.uk/id/eprint/271443
PURE UUID: 43fc55a0-5358-491f-bf9d-53ae2e808dc3

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Date deposited: 23 Jul 2010 20:46
Last modified: 10 Aug 2017 10:27

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Contributors

Author: Rob Mills
Author: C.L. Buckley

University divisions

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