Mechanistic models of population-...
Mechanistic models of population-level phenomena John Matthewson ��� Brett Calcott Published online: 7 June 2011 �� Springer Science+Business Media B.V. 2011 Abstract This paper is about mechanisms and models, and how they interact. In part, it is a response to recent discussion in philosophy of biology regarding whether natural selection is a mechanism. We suggest that this debate is indicative of a more general problem that occurs when scientists produce mechanistic models of popu- lations and their behaviour. We can make sense of claims that there are mechanisms that drive population-level phenomena such as macroeconomics, natural selection, ecology, and epidemiology. But talk of mechanisms and mechanistic explanation evokes objects with well-defined and localisable parts which interact in discrete ways, while models of populations include parts and interactions that are neither local nor discrete in any actual populations. This apparent tension can be resolved by carefully distinguishing between the properties of a model and those of the system it represents. To this end, we provide an analysis that recognises the flexible relationship between a mechanistic model and its target system. In turn, this reveals a surprising feature of mechanistic representation and explanation: it can occur even when there is a mismatch between the mechanism of the model and that of its target. Our analysis reframes the debate, providing an alternative way to interpret scien- tists��� ������mechanism-talk������, which initially motivated the issue. We suggest that the relevant question is not whether any population-level phenomenon such as natural selection is a mechanism, but whether it can be usefully modelled as though it were a particular type of mechanism. Keywords Mechanism Model Population Natural selection Economics J. Matthewson (&) B. Calcott Philosophy Program, RSSS and Centre for Macroevolution & Macroecology, Australian National University, Canberra, ACT 0200, Australia e-mail: email@example.com 123 Biol Philos (2011) 26:737���756 DOI 10.1007/s10539-011-9277-z
Introduction Despite the growing body of work on mechanisms (Machamer et al. 2000 Bechtel and Abrahamsen 2005 Glennan 2005 Craver 2006 Woodward 2002), compara- tively little attention has been paid to the relationship between these mechanisms and the way in which they are described or depicted. Stuart Glennan suggests one plausible reason for this: Perhaps because of the realist tendencies of the philosophers involved, most of the literature has focussed on the properties of mechanisms themselves and has not said much about the relationships between mechanisms and their models or theoretical representations (Glennan 2005, 443���444). In fact, it is sometimes difficult to see where the analysis of mechanisms in the world finishes and where (or if) a discussion of their representations begins. For example, in their paper ������Thinking about Mechanisms������, Machamer, Darden and Craver discuss the idea that different fields construct mechanisms differently, according to their purposes (Machamer et al. 2000, p. 13). Given that discovering mechanisms is central to their analysis, we think it unlikely that Machamer, Darden and Craver literally mean that scientists construct these mechanisms. Rather, scientists construct different representations of mechanisms, according to their different purposes. It is important to recognise and address this slippage between talk about representations and talk about what they represent. For example, since claims that appear to be about particular phenomena in the world may actually be about representations, we must be careful to not interpret such claims too literally. We return to this issue in the latter half of the paper, when we examine recent philosophical discussion regarding the status of natural selection as a mechanism. Additionally, any ambiguity between representation and what is represented can cause real problems for philosophers of science, because an important part of our overall project is to understand how scientific representation engages with the world. Collapsing the two will obscure this relationship, especially when the representation in question is a mechanistic model. We know that a model can be explanatory even when the properties of the model and what it represents diverge. Biologists who model infinitely large populations are not suggesting that such infinite populations exist, and chemists who model covalent bonds as springs are not suggesting tiny springs hold atoms together (Weisberg 2004). Nonetheless, such models represent their target systems in the right ways to provide explanatory power: they tell us how populations can evolve, and why organic molecules deform in the way they do. So as well as the possible confusion arising from such ambiguities, neglecting the gap between models and what they represent hides a rich and useful set of model-target relations that can help clarify how scientists investigate the world. This has not been a prominent issue in the literature on mechanisms, probably because the similarities between model and mechanism are usually clear���the parts and organisation of the model typically map directly to parts and organisation of the object being investigated. The problem becomes conspicuous, however, when 738 J. Matthewson, B. Calcott 123
scientists model populations���a common task in both biology and economics. Even though the parts and interactions that drive a population���s behaviour occur at the level of its individual members, a population is often modelled using population- level properties, so a mapping between the parts of the model and those of the object being investigated is less obvious. In what follows, we employ insights from recent philosophical work on modelling to show that we can make sense of this situation, even when there may be a mismatch between the mechanism of the model and the mechanism of the system it represents. What are mechanisms? Recent definitions for mechanisms come from a number of different authors: A mechanism is a structure performing a function in virtue of its component parts, component operations, and their organization. The orchestrated functioning of the mechanism is responsible for one or more phenomena (Bechtel and Abrahamsen 2005, p. 423). Mechanisms are entities and activities organized such that they are productive of regular changes from start or set-up to finish or termination conditions (Machamer et al. 2000, p. 3). A mechanism for a behavior is a complex system that produces that behavior by the interaction of a number of parts, where the interactions between parts can be characterized by direct, invariant, change-relating generalizations (Glennan 2005, p. 445). The definitions differ, but the differences are minor relative to the common ground shared by these authors.1 Here are a list of requirements that we consider to be central to, and relatively uncontroversial within, the mechanist account of science. 1. A mechanism does something of note. It underlies a behaviour (Glennan 2005), performs a function (Bechtel and Abrahamsen 2005), or produces some regularity (Machamer et al. 2000). 2. This behaviour/phenomenon/function is produced in virtue of the mechanism���s component parts or entities, and the processes, interactions, or activities these parts carry out. 3. The components and activities are organised in a particular way in order to produce the behaviour or regularity. 4. Mechanisms can be composed hierarchically (Bechtel and Abrahamsen 2005 Craver 2006). A mechanism may be a component in some larger mechanism, and the components that make up a mechanism may themselves be mechanisms. 1 The internal controversies are of course important in their own right (see (Tabery 2004)), but we do not think they bear on the points made in this paper. Mechanistic models of population 739 123