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Iguana Modeling Is Not the Only Game in Town

We agree with Webb’s point that discussions of the importance and novelty of synthesis in ALife and AB have sometimes obscured the fact that these are fundamentally model-building enterprises. It is true that standard practice in ALife and AB is to construct a program or a robot, and on the surface this can look different to the theories-and-experiments approach of normal science. The picture is further complicated by the fact that many researchers have engineering goals as well as biological ones. However, to the extent that ALife and AB are going to teach us anything about biology, it is important to recognize that they will do so not by becoming independent of laboratory and field work but by giving us new ways to understand empirical data. Webb is right to remind us all of how contributions to biology are made. It follows that we applaud Webb’s attack on the ontological mumbo jumbo of the strong ALife position. “It’s alive!” is the Frankensteinian claim of the strong ALifer. Like Webb, we cannot agree. It is not alive, it is a formal model instantiated as a computer program, and the map should not be mistaken for the territory. ALife’s ability to ask counter-factual questions (e.g., what would it be like if there were more than two sexes?) no more licenses talk of a new, alternate reality than does the ability of a mundane traffic simulation to ask, what would London be like with more motorways? Webb is right to point out that some ALife and AB authors have used the life-as-it-couldbe position to dodge their responsibility to make reference to real biology. We further agree that Webb offers the right rebuttal for those who want to use ALife creations as source models, in the same sense as using a pump as a model for the function of the heart. (Webb suggests this interpretation of Langton’s, 1989, “alternative” biology position.) An ALife simulation is not an independent real-world object but an artifact that has been constructed with strong influences from biological theory. It is therefore already a theoretical model and we should not be surprised when it turns out to resemble the real-world target system in some way. Thus we find much to agree with in Webb’s article. However, we are uncomfortable with the narrowness of a view on modeling that dismisses Beer’s (2003) work on minimally cognitive agents. Frankly, we are suspicious of an analysis that places the overcomplex, over-optimistic multiscale modeling effort of the Psikharpax project (Meyer et al., 2005) in the “good” column while placing Beer’s simplicity, clear reasoning, and careful analysis in the “bad” column. We see Beer as part of a long tradition of useful, simple, and abstract models in the cognitive and biological sciences. So where do we part company with Webb in her view of legitimate modeling practices? Webb argues that model builders must have a target system in mind, they must develop a hypothesis about how that target system works, and they are then justified in building a model if the hypothesis is complex enough to be non-obvious in its implications. In other words, the model’s purpose is to spell out the consequences of the various assumptions in a complicated theory. We think that is not a bad description of what modeling is (although see Di Paolo, Noble, & Bullock, 2000, for a more complete account) but we disagree with Webb on the range of epistemological payoffs