256 Tim Lewens
blending inheritance to permit cumulative change (Richerson and Boyd (2005): 88–90).
Questions of this sort are perhaps especially easy to access from the perspective of popu-
lational evolutionary models.
In a similar vein, population thinking prompts Kitcher to test whether a group of scien-
tists who pursue truth in a disinterested manner are in fact instantiating an optimal strategy
for attaining the truth. One of his formal models suggests that a group of scientists who
care only about getting at the truth may be less effi cient at generating new knowledge than
a group of scientists who also care about taking the credit for making a new discovery
(Kitcher 1993: 308–314). Roughly speaking, this is because those who care about getting
to the truth will tend to behave in a uniform fashion: If received wisdom suggests that a
particular prominent scientist’s views are along the right lines, then they will all borrow
those views. If the prominent scientist’s views are not well-regarded, then they will all
ignore them. Kitcher’s “sullied scientists,” who also care about their own reputations, have
more of an incentive to pursue unfancied, or unfashionable, avenues of research, which
may lead to their being seen to make an important discovery that goes against the grain
of the community. But this also means that a community of more egotistical scientists will
tend to pursue diverse avenues of research—it will not put all its eggs in one basket. And
this, in turn, can increase the chances, from the community perspective, of making impor-
tant breakthroughs.
Kitcher’s model is recognizably “populational.” Like Boyd and Richerson, he seeks to
show how the properties of a population—in this case, a scientifi c community—depend
in counterintuitive ways on the properties of the entities that make it up—in this case,
scientists. This is another instance of the form of population thinking that I have been
referring to as “aggregative thinking.” At a stretch, one might also describe Kitcher’s
populational models as “evolutionary,” simply because they try to explain the unfolding
behavior over time of a group of interacting entities. But Kitcher’s models rarely have any
obvious analogue to natural selection, reproduction, replication, or drift. No such concepts
feature in his populational attempts to understand the epistemic fortunes of communities
of sullied and pure scientists. This suggests that “population thinking,” understood as
“aggregative thinking,” is an important part of the evolutionary biologist’s toolbox, but it
is not a distinctively biological, or even a distinctively evolutionary, way of thinking. Even
so, the application of these formal, populational modeling techniques may be among the
most promising ways in which styles of thinking familiar to evolutionary biologists will
shed light on the domain of technical innovation.^1
Note
- This chapter was fi rst presented at the KLI Workshop on the Comparative Philosophy of Technical Artifacts
and Biological Organisms in September 2006, and a much earlier ancestor of it was presented in Delft. I am
grateful to both audiences for comments and criticism, and especially to Peter Kroes and Ulrich Krohs.