(DRAFT) Minds and Machines 18, 4: 475-491, 2008
1. Introduction
Computational modeling plays an increasingly important explanatory role in cases where we
investigate systems or problems that exceed our native epistemic capacities. One clear case
where technological enhancement is indispensable involves the study of complex systems.1
However, even in contexts where the number of parameters and interactions that define a problem
is small, simple systems sometimes exhibit non-linear features which computational models can
illustrate and track. In recent decades, computational models have been proposed as a way to
assist us in understanding emergent phenomena.
The core concern of this paper centers on the following question: Assuming that
emergent properties are a genuine feature of the natural world, how might computational models
help us to generate explanatory accounts of those properties? Putatively emergent properties such
as the flocking behavior of birds, (Reynolds 1988) the adaptive features of the immune systems
(Hofmyer et al 2000) and the characteristic patterns of traffic flow (Schreckenberg 1995) have
been given computational models. In what sense (if any) do such models help to explain the
respective emergent features under consideration?