Individual and global adaptation in networks


Watson, Richard (2012) Individual and global adaptation in networks. In, Wróbel, Borys (ed.) Artificial Life XIII: Proceedings of the Thirteenth International Conference on the Synthesis and Simulation of Living Systems. EVONETS Workshop at ALife XIII: 13th International Conference on Artificial Life , MIT Press.

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Description/Abstract

The structure of complex biological and socio-economic networks affects the selective pressures or behavioural incentives of components in that network, and reflexively, the evolution/behaviour of individuals in those networks changes the structure of such networks over time. Such ‘adaptive networks’ underlie how gene-regulation networks evolve, how ecological networks self-organise, and how networks of strategic agents co-create social organisations. Although such domains are different in the details, they can each be characterised as networks of self-interested agents where agents alter network connections in the direction that increases their individual utility. Recent work shows that such dynamics are equivalent to associative learning, well-understood in the context of neural networks. Associative learning in neural substrates is the result of mandated learning rules (e.g. Hebbian learning), but in networks of autonomous agents ‘associative induction’ occurs as a result of local individual incentives to alter connections. Using results from a number of recent studies, here we review the theoretical principles that can be transferred between disciplines as a result of this isomorphism, and the implications for the organisation of genetic, social and ecological networks.

Item Type: Book Section
ISBNs: 0262310503 (electronic)
9780262310505 (electronic)


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Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
Faculty of Physical Sciences and Engineering > Electronics and Computer Science > EEE
ePrint ID: 342174
Date Deposited: 14 Aug 2012 16:23
Last Modified: 27 Mar 2014 20:24
Publisher: MIT Press
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/342174

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