"If you can't be with the one you love, love the one you're with": How individual habituation of agent interactions improves global utility


Davies, Adam, Watson, Richard, Mills, Rob, Buckley, C.L. and Noble, Jason (2011) "If you can't be with the one you love, love the one you're with": How individual habituation of agent interactions improves global utility. Artificial Life, 17, (3), Summer Issue, 167-181. (doi:10.1162/artl_a_00030).

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Description/Abstract

Simple distributed strategies that modify the behaviour of selfish individuals in a manner that enhances cooperation or global efficiency have proved difficult to identify. We consider a network of selfish agents who each optimise their individual utilities by coordinating (or anti‐coordinating) with their neighbours, to maximise the pay-offs from randomly weighted pair-wise games. In general, agents will opt for the behaviour that is the best compromise (for them) of the many conflicting constraints created by their neighbours, but the attractors of the system as a whole will not maximise total utility. We then consider agents that act as 'creatures of habit' by increasing their preference to coordinate (anti-coordinate) with whichever neighbours they are coordinated (anti‐coordinated) with at the present moment. These preferences change slowly while the system is repeatedly perturbed such that it settles to many different local attractors. We find that under these conditions, with each perturbation there is a progressively higher chance of the system settling to a configuration with high total utility. Eventually, only one attractor remains, and that attractor is very likely to maximise (or almost maximise) global utility. This counterintuitive result can be understood using theory from computational neuroscience; we show that this simple form of habituation is equivalent to Hebbian learning, and the improved optimisation of global utility that is observed results from well‐known generalisation capabilities of associative memory acting at the network scale. This causes the system of selfish agents, each acting individually but habitually, to collectively identify configurations that maximise total utility.

Item Type: Article
ISSNs: 1064-5462 (print)
1530-9185 (electronic)
Keywords: hebbian learning, hopfield network, associative memory, game theory, self-­rganisation, adaptive networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
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: 272431
Date Deposited: 10 Jun 2011 10:07
Last Modified: 27 Mar 2014 20:18
Further Information:Google Scholar
ISI Citation Count:1
URI: http://eprints.soton.ac.uk/id/eprint/272431

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