Although the work made so far allowed us to distinguish two ontologically distinct notions of drift, it should be stressed that this distinction would be extremely difficult or even impossible to demonstrate empirically. This is related to the problem of the multiple possible causal interpretations associated with any statistical measure. In front of a distribution of reproductive outputs associated with a particular type, knowing whether this distribution should be attributed to ontologically indeterministic events, as in our example of foraging organisms, or to differences in intrinsic-variables or extrinsic properties (e.g., difference in brain structures due to difference in environmental states) is generally untraceable. Because of those empirical limitations, there is thus no doubt that heuristically considering fitness as a propensity is useful. But as I have already noted, non-trivial propensity interpretations of probabilities are generally unsatisfactory in deterministic set-ups and one should there have good theoretical reasons to interpret fitness as if it was a propensity in deterministic systems. This is precisely the project of Strevens (2011) in which he gives us nice examples of deterministic systems (wheels of fortune and tosses of coins) in which probabilities of events can have a “propensity-like look and feel”. Strevens calls these probabilities microconstant probabilities.
For Strevens’ account to be applicable, a system should exhibit ‘microconstancy’. Strevens’ account is very subtle and explaining the subtleties of his account would greatly exceed the scope of this thesis. Yet, it is fair to say that microconstancy is the key to his account. For more on Strevens’s account of probabilities see Strevens (2003, 2011). Roughly speaking, microconstancy is the property of a system to produce a given outcome with the same proportions of initial conditions within any small but not too small neighbourhood of initial conditions. This results in a ball stopping with the same frequency on black or red slots however
the ball is launched on a classical roulette wheel and how many turns the wheel is making. If the system exhibits microconstancy, as the roulette wheel does, then it behaves as if it was exhibiting propensities provided that one does not know with precision the initial conditions.
To apply Strevens’ account to ecological systems and fitness to be considered as a propensity-like property, that is, that an ecological system exhibits microconstancy over the distribution of initial conditions, one prerequisite is that each entity of the population (or at least each type as a whole) has in principle access to all the states of the environment. This is because if this prerequisite is not met, some outcomes could never be possible (in principle) with at least some entities (or types) and thus not any small neighbourhood of initial conditions would produce a given outcome with the same proportions of initial conditions. It would be equivalent in the case of the roulette wheel to some slots never being reachable for the ball.
A biological population is usually defined as a set of entities within a common selective environment, that is, undergoing the same set of ecological selection pressures (Brandon 1990, 46). This means that the assumption demanded by Strevens’ account is a reasonable one. However, cases in which this assumption will be violated should generally be expected. Colyvan (2005) provides several cases in which we know that the condition of microconstancy is violated for biological systems. For instance, if we suppose a population of hares with different types of predators in different health conditions. Unless the average health of the predators are at equilibrium then we cannot consider the system to exhibit microconstancy over time. That would be equivalent to a roulette wheel changing the relative surface of black and red slots over time.
Another problem will arise if the entities of the population are in a patchy environment and different entities for reasons independent from their biology have access to different patches
of the environment. One response to this specific problem could be that if, independently from their intrinsic-invariable properties, the different entities of a set of entities have access to different states of the environment and this leads to differences in reproductive outputs, it might be justified to consider this set of entities not as one but two populations. But this solution will not always be possible. Furthermore, without knowing a priori whether the entities (or types) have in principle access only to certain states of the environment, it will be hard to know whether microconstancy is a reasonable assumption for a given set of entities. As a result one will always be at risk of having missed a hidden variable or made a measurement error that would render inaccurate the approximation fitness as a propensity-like property.
Leaving asides the different problems Streven’s account leads to, which would require much more work to be done, if one is willing to apply Strevens’ account to fitness, deterministic drift should further be separated into microconstant drift and non-microconstant drift. Microconstant drift would represent a departure from an expectation-like fitness function due to a small sample size of the population and non-microconstant drift would represent the level of error coming out of the assumption of microconstancy. Whether one should distinguish microconstant from non- microconstant drift is debatable at that point.