Innovation and Population 253
interested in seeing what effect individual psychological dispositions—especially disposi-
tions to learn and imitate—have on the cultural makeup of human populations:
A population-based theory of cultural change tells us how the details of individual psychology affect
what kinds of skills, beliefs, and values that individuals acquire. (Richerson and Boyd 2005: 8)
In showing that this kind of thinking bears fruit, one does not thereby show that it is
worthwhile to take an evolutionary stance on individual cultural items (tools, ideas, or
whatever) and the populations they form, of the type endorsed by memeticists. However,
although we can now see that artifact fi tnesses, understood as long-run growth rates, may
illuminate rather little by themselves, we should not assume that an artifact’s long-run
growth rate is merely a simple function of how attractive it is to typical users, any more
than an organic type’s long-run growth rate is a simple function of its suitability to its
local environment (Lewens 2006: ch. 7). Take the case of a particular recording of a song
on a CD. What facts might make recordings of this song more likely to spread than others?
In part, of course, we can point to facts that make the song catchy—psychological facts
that make an individual who has the CD more likely to play it, and facts that make indi-
viduals who hear it more likely to buy a copy for themselves. But a song could score
comparatively poorly on these characteristics and still spread faster than its competitors
simply because it is ubiquitous. If a record company ensures that a melody is played
through all available radio and TV networks, then even a recording that is comparatively
uncatchy will quickly be purchased by millions. We cannot infer from the swift spread of
a CD through a population that the song the CD has on it has features that make it likely
to hop from mind to mind. The song may not be especially contagious or catchy at all;
the song’s producers may just be powerful enough to make it ubiquitous, hence more likely
to be purchased than far catchier but more poorly funded competitors. The moral of this
example is that if we choose to build a cultural evolutionary model that assigns fi tnesses
to technologies themselves, one will need to include population-level factors in addition
to facts about typical individual psychology, among the determinants of artifact fi tnesses.
For some students of technology change, that may be a signifi cant lesson in itself.
14.9 The Needham Question
We have already considered the fairly intuitive positive effects of large population size on
technical innovation. Population thinking also prompts us to consider the less intuitive
possibility that small population size might also have positive effects on innovation. Sewall
Wright famously argued that drift can foster adaptation. Drift is more likely to occur in
small populations than large ones. This means that in a small population, whichever variant
is better suited to the local environment is more likely to be eliminated than it is if in a
large population. Wright’s language of “fi tness landscapes” allows us to articulate the