David Lewis has long defended an analysis of counterfactuals in terms of comparative similarity of possible worlds. The purpose of this paper is to reevaluate Lewis’s response to one of the oldest and most familiar objections to this proposal, the future similarity objection.

What is it for an event not to occur? This is an urgent, yet under explored, question for counterfactual analyses of causation quite generally. In this paper I take a lead from Lewis in identifying two different possible standards of non-occurrence that we might adopt and I argue that we need to apply them asymmetrically: one standard for the cause, another for the effect. This is a surprising result. I then offer a contextualist refinement of the Lewis approach in light (...) of initial problems, and discuss how the asymmetry remained hidden until now. I then relate the non-occurrence problem to issues of transitivity and proportionality in causation, before showing that a parallel problem exists for contrastivist and interventionist approaches to causation too. (shrink)

The future similarity objection against David Lewis’s theory of counterfactuals re-emerges once the space-time of possible worlds is adequately represented. Given such a representation, it can be shown that a number of counterfactuals that seem clearly true, such as Kit Fine’s example ‘If Nixon had pressed the button, there would have been a nuclear holocaust’, come out false, even if determinism is assumed. Lewis’s similarity criteria can be modified in different ways to avoid the problem, but some of the modifications (...) have problems of their own. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For illustrative (...) purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different "degrees of contingency". We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For illustrative (...) purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For illustrative (...) purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

A number of recent authors (Galles and Pearl, Found Sci 3 (1):151–182, 1998; Hiddleston, Noûs 39 (4):232–257, 2005; Halpern, J Artif Intell Res 12:317–337, 2000) advocate a causal modeling semantics for counterfactuals. But the precise logical significance of the causal modeling semantics remains murky. Particularly important, yet particularly under-explored, is its relationship to the similarity-based semantics for counterfactuals developed by Lewis (Counterfactuals. Harvard University Press, 1973b). The causal modeling semantics is both an account of the truth conditions of counterfactuals, and (...) an account of which inferences involving counterfactuals are valid. As an account of truth conditions, it is incomplete. While Lewis's similarity semantics lets us evaluate counterfactuals with arbitrarily complex antecedents and consequents, the causal modeling semantics makes it hard to ascertain the truth conditions of all but a highly restricted class of counterfactuals. I explain how to extend the causal modeling language to encompass a wider range of sentences, and provide a sound and complete axiomatization for the extended language. Extending the truth conditions for counterfactuals has serious consequences concerning valid inference. The extended language is unlike any logic of Lewis's: modus ponens is invalid, and classical logical equivalents cannot be freely substituted in the antecedents of conditionals. (shrink)

Big decisions in a person’s life often affect the preferences and standards of a good life which that person’s future self will develop after implementing her decision. This paper argues that in such cases the person might lack any reasons to choose one way rather than the other. Neither preference-based views nor happiness-based views of justified choice offer sufficient help here. The available options are not comparable in the relevant sense and there is no rational choice to make. Thus, ironically, (...) in many of a person’s most important decisions the idea of that person’s good seems to have no application. (shrink)