Résumé
Agent-based simulation is an alternative approach to traditional
analytical methods for understanding and capturing dierent types
of complex, dynamic interactive processes. However, the application of
these models is currently not common in the eld of socio-economical
science and many researchers still consider them as intransparent, unreliable
and unsuitable for prediction. One of the main reasons is that these
models are often built on architectures derived from computational concepts,
and hence do not speak to the selected domain's ontologies. Using
Triandis' Theory of Interpersonal Behaviour, we are developing a new
agent architecture for choice model simulation that capable of combining
a diverse number of determinants in human decision-making and
being enhanced by empirical data. It also aims to promote communication
between technical scientists and other disciplines in a collaborative
environment. This paper illustrates an overview of this architecture and
its implementation in creating an agent population for the simulation of
mobility demand in Switzerland.