Abstract
In this paper we develop an analysis of dispositions in terms of causal Bayes nets. In particular, we analyze dispositions as generic cause–effect structures that increase the probability of the manifestation when the stimulus is brought about by intervention in certain circumstances. We then highlight several advantages of our analysis and how it can handle problems arising for classical analyses of dispositions such as masks, mimickers, and finks.
Highlights
In this paper we develop an analysis of dispositions on the basis of causal Bayes nets.1 Causal modeling techniques such as causal Bayes nets have already been applied to various philosophical problems
The paper is structured as follows: In Sect. 2 we introduce dispositions and the problems arising for classical dispositional theories due to masks, mimickers, and finks
In this paper we developed an analysis of dispositions in terms of causal Bayes nets
Summary
In this paper we develop an analysis of dispositions on the basis of causal Bayes nets. Causal modeling techniques such as causal Bayes nets have already been applied to various philosophical problems (see, e.g., Gebharter 2017a, c; Hitchcock 2016; Meek and Glymour 1994; Schaffer 2016). The causal Bayes net framework can be used to study such behavior of dispositions in different causal environments on empirical grounds Because of this flexibility, our analysis can provide novel solutions to philosophical problems posed by masks, mimickers, and finks which, one way or another, plague all other accounts of dispositions currently on the market.. Even though the plate is not disposed to break when struck, it does break when struck due to the presence of the hater of styrofoam Examples like this are intended to show that conditional analyses can fail because a disposition’s manifestation can be mimicked: The presence of the mimicker makes the conditional corresponding to a disposition true though the object lacks the disposition.
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