Animals and Animats: Why Not Both Iguanas?

In her target article, Webb contrasts two kinds of models, which she calls animal and animat models, and argues that the latter are unfairly held to less strict standards of scientific relevance than the former, particularly in regards to being subjected to empirical refutation. In order to illustrate her position, she draws upon both her own work on cricket phonotaxis (Reeve & Webb, 2003; Webb, 1995) and our work on the evolution and analysis of model brain–body–environment systems (for a review see Beer, 2008), focusing specifically on our studies of categorical perception (Beer, 2003a). We applaud Webb for engaging the general issue of model interpretation in adaptive behavior and artificial life, since it is far too common for work in these communities to be at best unclear and at worst intentionally ambiguous about their intended scientific relevance. We also completely agree that any scientific research must ultimately be judged by the degree to which it illuminates the actual phenomenon of interest and that, to the extent that animats are scientifically relevant, they are indeed models. However, we could not disagree more strongly with Webb’s overly restrictive conception of the kind of models they must be. Any model can be characterized by its answers to several key questions. What is the model’s target? How does the model relate to its target? What purpose is the model intended to serve? How should the model’s success be evaluated? We will call these questions the fundamental modeling questions. As we understand it, Webb’s central argument turns on her insistence that these questions be answered in a particular way, which is grounded in a specific modeling methodology that we will call datadriven modeling. What she fails to recognize, however, is that there are many other kinds of modeling methodologies that offer different but equally valid answers to these questions. In particular, our own work is grounded in the tradition of theory-driven modeling, which has its roots in physics. In this commentary, we attempt to briefly characterize both data-driven and theory-driven modeling and to contrast the sorts of answers they give to the fundamental modeling questions.