Theoretical complementarity and incompatibility

What do we learn from such a sprawling comparison of different views? For the purposes of this chapter, I will focus on just two points. First, there are two importantly different clusters of views. Second, given a particular explanatory goal, we should try for a synthetic view that incorporates all three methodological approaches.

One cluster of views emerges that overlaps in explanatory goals, methodology, and permissiveness. The Four Dimensional Inheritance view (#2), the Darwinian Populations View (#3), the Price Equation View (#9) are all permissive, multi-channel views interested in explaining actual evolutionary phenomena, and in understanding

which mechanisms of inheritance have the potential to produce evolutionary outcomes.

Proponents of these views are also interested in developmental phenomena, but understanding evolution is primary. They are not seeking to identify inheritance

mechanisms that always, consistently, produce interesting and important effects. Rather,

they want to identify mechanisms that can produce such outcomes, even if they do so

rarely. This set of views includes both mechanistic and statistical methodologies, but in the context of so much common ground, it is clear that these different methodological approaches are complementary rather than competitive.

Another cluster of views are the role-based views: the Reproducer View (#4), the Limited Extended Inheritance View (#5), the Replicator View (#7), and the Inherited Representations View (#8). Unlike the first cluster, these views are all of restrictive or intermediate stringency, and, with the exception of the Reproducer View, their

explaining a particular set of evolutionary phenomena, one that is much narrower than the first cluster of views. The type of evolutionary outcome that these views are interested in is cumulative selection leading to adaptation, particularly complex, multi-part adaptations like the vertebrate eye.

To the extent that these two clusters of views have valuable and distinct

explanatory goals, there is no reason to worry about the incompatibilities between them, such as different definitions of inheritance, different conceptions of the units of

inheritance, and different levels of stringency. It is only when projects share an

explanatory target and are thus competing with one another that such differences need to be resolved. At the same time, there are substantive questions about the value of different explanatory goals and about the interrelations among different explanatory goals.

In this case, both clusters of views want to explain evolutionary phenomena, but they are interested in different kinds of phenomena. If both views are right, then there is a narrow set of inheritance mechanisms that is relevant to explaining cumulative selection and complex adaptations, and a broader set of mechanisms relevant to explaining

phenomena such as the maintenance of variation and the emergence of novel traits. In the next chapter, I will challenge this idea by showing that the narrower set of mechanisms is not actually sufficient to explain the kinds of phenomena that the second cluster of views is interested in.

Leaving that aside for now, what are we to make of the fact that the mechanistic and statistical views are allied, and that they are not well-integrated with the role-based views? I propose that this state of affairs is not ideal. Whatever our explanatory goal is, the best explanation of our target phenomena will make use of all three methodologies.

The reason is simple: all three methodologies tell us something interesting and different about inheritance. Mechanistic approaches help us to identify actual inheritance

mechanisms in the world and to see interesting differences in how they function. Statistical approaches allow us to understand how mechanisms that may operate

differently from one another or have a different physical bases can produce similar types of evolutionary outcomes. Finally, role-based approaches highlight the features of particular mechanisms that are most relevant to producing different evolutionary outcomes.

The following passage from Shea et al. (2011) highlights one way in which role- based approaches can supplement mechanistic ones:

Jablonka & Lamb (1995, 2005) deliberately employ a mechanistic classification scheme in order to highlight the wide variety of nongenetic effects on the phenotypes of future generations. In their usage, all

transgenerational epigenetic mechanisms are systems of inheritance. Similarly, Bonduriansky & Day (2009) call all nongenetic

transgenerational effects between parents and offspring inheritance. Although, this is a legitimate use of the world in its broad sense of 'things received from a predecessor', only a subset of epigenetic mechanisms forms a system of long-run inheritance in the way the genome is an inheritance system. Focusing on the type of mechanism involved may obscure such questions about the evolutionary significance of such mechanisms (p. 1178).

While I agree with Shea et al. that there are virtues of their role-based approach that the mechanistic approach does not have, I disagree with their implicit claim that the

difference in methodology is dictated by a difference in explanatory goals (the goals of highlighting nongenetic transgenerational effects and the goal of identifying systems of long-run inheritance). The fact that Jablonka & Lamb have different explanatory goals than Shea et al. does not mean incorporating a mechanistic element would not improve

Shea's view, nor that incorporating a role-based element would not improve Jablonka & Lamb's view. In fact, the opposite is true. A mechanistic approach would shed light on the physical basis of the different kinds of evolutionary effects Shea et al. identify. And a role-based approach would help us understand the kinds of features that are essential for producing the nongenetic transgenerational effects that interest Jablonka & Lamb.