Optimality explanations: shared physical dependency relations

That optimality models do not use causes is not an argument in itself that they are non- causal explanations: one can also deny that they are adequate explanations at all. For instance, Michael Strevens (2008, p.288) argues that optimality models give us minimal causal information: the actual causal history must be within the set of possible causal trajectories, limited by context-specific constraints and trade-offs. But the actual causal information is ‘black-boxed’. This implies that optimality models are at best only partial explanations:

Because a model that secretes some mechanisms in its explanatory framework does not confer what I called […] ‘deep’ of ‘full’ understanding of the phenomenon that constitutes its target, the black-boxing model is limited in its explanatory power. A deep explanation of the ecosystem’s stability must flesh out the model’s black boxes rather than leaving the causal details in the framework. (Strevens, 2008, p. 159)

The problem for Strevens is that the features of optimality models are discretely multiply realizable kinds. There is no single, stable explanation across all populations, even if there is a single high-level structure that is shared among the populations. His conclusion is even more fare-reaching:

The sense in which the black-boxing model explains the stability of a wide range of ecosystems is at best partial, then: the model does not itself explain stability in each such system; it rather provides the schema for the individual, case-by-case explanations. (Strevens, 2008, p. 160)

Strevens’ position contradicts my approach. My analysis starts by considering how these models are used by scientists: as explanations. Optimality models are not temporary explanations or steps towards an explanation. They are the best-suited explanations for the task at hand: providing highly idealized equilibrium explanations, or in my terms: providing answers to resemblance questions.

Woodward’s view is more in line with scientific practice here. His notion of explaining is broader than causal explanations:

An explanation must answer a what-if-things-had-been-different question, or exhibit information about a pattern of dependency. (2003, p. 201)

This means that if I can show that optimality models exhibit information about dependency relations, they are explanations. Woodward uses the criterion of mirroring physical dependency relations to distinguish explanatory derivations from non- explanatory derivations:

The idea is that these derivations trace or mirror the relations of physical dependency that hold between the explanans conditions and the explananda phenomena-relations that would be revealed if, for example, we were to physically intervene to alter the explanans conditions. (2003, p. 201)

If I can show that optimality models mirror such physical dependency relations, they can be classified as explanatory according to Woodward’s view.

In order to do so, I will use Weber, Van Eck and Mennes’ interesting analysis of the epistemic value of biological ascriptions, or functional explanations:

Biological advantage ascriptions are valuable because they provide the means for answering questions of the following form: “What would happen if (due to mutation) in some individuals of species s item i’ would have a different property e’ (while the habitat remains unchanged)?” (201+)

Their conclusion is that functional explanations provide answers to questions of the form:

what would happen if a disturbance occurred. If such a disruption would occur, similar

causal processes and mechanisms would be activated in the individuals that make up the population (or set of populations).

Let me explain this by comparing it, as Weber, Van Eck and Mennes do, with Woodward’s contrastive account (201+).

For Woodward an explanation must show how the explanandum would change if the initial conditions were different. In other words, adequate explanations

… locate their explananda within a space of alternative possibilities and show us how which of these alternatives is realized systematically depends on the conditions cited in the explanans. They do this by enabling us to see how, if these initial conditions had been different or had changed in various ways, various of these alternatives would have been realized instead (2003, p. 191).

In Woodward’s view an explanation must give an answer to the question: what if things had been different? What if we could intervene in the relevant causal factors of the explanandum and change one or more factors, would the explanandum be different or not? In order to give a satisfactory answer to that question, counterfactual dependence must be established.

Functional explanations not only look back or focus on the present, but they are also prospective. They give information about what might happen in the future if the current state of affairs would change. Biological ascriptions formulate reasons to prefer one theoretical possibility above others. In this sense, functional explanations are not causal explanations, since they do not rely on reconstructing causal processes or causal relevance from the past to the present. Instead functional explanations use theoretical possibilities to argue how future states of affairs might be if the current state is disrupted. Optimality models work in a similar way. In order to explain the current state they do not

reconstruct a causal ancestry, but they show that future states may be different if trade- offs are changed due to genetic mutations or environmental factors.

The explanation in optimality models is based on structural relationships between trade-offs and constraints. Optimality explanations tell us what would happen if the relationship between those tradeoffs and constraints changes, e.g. because of a change in the habitat of the population or because of a genetic mutation. By focusing on these structural relationships optimality models can answer forward-looking resemblance questions of the type:

What would happen to phenotypic trait X of a population (or a set of populations) if the current state of affairs (environmental or genetic) would change? What would happen if a disruption occurs that disturbs the current evolutionary equilibrium? An optimality model can answer such questions because it exhibits the dependency relations between context-specific constraints, trade-offs and the state of a population, even if these relations are not causal. The deductive structure of the optimality model mirrors counterfactual physical dependency relations, and so, they qualify as explanations. The explanatory unification that occurs in these models will be called

physical dependency unification.

5.6 Physical dependence unification supervenes on causal