Abstract:
It is argued that traditional models of urban development are characterised by an
aggregate mechanistic description of statistical units. Furthermore, important aspects of
transportation are not included in these models, but urban development can be regarded
as a combined process of land use change, transportation system and lifestyles. New 4
developments in evolutionary theory provide a new paradigm for a microsimulation
approach on the level of individuals, which accounts for diversity, learning and change
in the population o f the modelled system.
In this thesis a framework for agent-based simulations will be presented for which this
new evolutionary theory provides the theoretical background. The essence of the
approach builds on the mutual interdependencies between all system elements, in this
case inhabitants and their environment. This principle is extended to change in the
interactions of the system over time, leading to an adaptive system that mutually
specifies all its elements over time.
On this framework an adaptive agent-based model for the use in urban simulations is
built. The agents are equipped with a set of intrinsic needs, the satisfaction of which is
expressed through a set of corresponding budgets. The budget state is fed into a Fuzzy
Logic rule base for decision making. As opposed to many existing approaches to
microsimulation, the agents are designed to change their behavioural rules during run
time according to experience. Different adaptation strategies are tested and compete
with each other.
The results of the model vindicate the conceptual framework. The essence of the
underlying theory - mutual specification based on satisficing as opposed to
optimisation - leads to a cognitive approach to the simulation of socio-natural systems.
Microsimulation based on adaptive agents can help integrate many aspects of urban
models, which are conventionally treated by separate models and can help clarify the
implications of change for the inhabitants o f an urban system.
