For large-scale systems and networks embedded in highly dynamic, volatile, and unpredictable
environments, self-adaptive and self-organising (SASO) algorithms have been proposed as
solutions to the problems introduced by this dynamism, volatility, and unpredictability. In open
systems it cannot be guaranteed that an adaptive mechanism that works well in isolation will
work well — or at all — in combination with others.
In complexity science the emergence of systemic, or macro-level, properties from individual, or
micro-level, interactions is addressed through mathematical modelling and simulation. Intermediate
meso-level structuration has been proposed as a method for controlling the macro-level
system outcomes, through the study of how the application of certain policies, or norms, can
affect adaptation and organisation at various levels of the system.
In this context, this thesis describes the specification and implementation of an adaptive affective
anticipatory agent model for the individual micro level, and a self-organising distributed institutional
consensus algorithm for the group meso level. Situated in an intelligent transportation
system, the agent model represents an adaptive decision-making system for safe driving, and the
consensus algorithm allows the vehicles to self-organise agreement on values necessary for the
maintenance of “platoons” of vehicles travelling down a motorway. Experiments were performed
using each mechanism in isolation to demonstrate its effectiveness.
A computational testbed has been built on a multi-agent simulator to examine the interaction
between the two given adaptation mechanisms. Experiments involving various differing combinations
of the mechanisms are performed, and the effect of these combinations on the macro-level
system properties is measured. Both beneficial and pernicious interactions are observed; the
experimental results are analysed in an attempt to understand these interactions.
The analysis is performed through a formalism which enables the causes for the various interactions
to be understood. The formalism takes into account the methods by which the SASO
mechanisms are composed, at what level of the system they operate, on which parts of the
system they operate, and how they interact with the population of the system. It is suggested
that this formalism could serve as the starting point for an analytic method and experimental
tools for a future systems theory of adaptation.