Emergent associative memory as a local organising principle for global adaptation in adaptive networks

Abstract

Complex adaptive systems composed of self-interested agents can in some circumstances self-organise into structures that enhance global adaptation or efficiency. However, the general conditions for such an outcome are poorly understood. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, generalisation and optimisation, are well-understood. While such global functions within a single agent or organism may arise from mechanisms (e.g., Hebbian learning) that were selected for this purpose, agents in a multi-agent system have no obvious reason to produce such global behaviours when acting from individual interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we use an adaptive network model in which agents can modify their behaviours (states) but also their interactions with other agents (network topology). We show that when self-interested agents can modify how they are affected by other agents then, in adapting these inter- agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. When the agents adapt their behaviours relatively quickly, and their relationships with other agents relatively slowly, we find that the overall network dynamics are modified to find better adapted states more reliably. This separation in timescales causes the state dynamics to spend most of their time at attractors. Thus, the network develops an associative memory that amplifies a subset of its own attractor states. This self-organised modification to the network dynamics enhances its ability to resolve conflicts between agents. Moreover, we show that the system is not merely ‘recalling’ high quality states that have been previously visited, but ‘predicting’ their location by generalising over local attractor states that have already been visited. Thus, globally adaptive behaviours can emerge from self-organising adaptive networks that follow organisational principles familiar in connectionist models of organismic learning.

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ORCID for Richard A. Watson: orcid.org/0000-0002-2521-8255

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