"Causal Reasoning and Backtracking"Abstract:"Causes do not invariably raise the probabilities of their effects, nor are they generally evidence for their effects. Learning about a cause can convey information about one of its effects either via a direct cause-to-effect inference, which will confirm the effect, or via a "backtracking" inference, which can disconfirm the effect by indicating that stronger inhibiting causes will occur. My aim is to find ways of separating the direct, or "front-door," evidence that causes provide for their effects in virtue of being causes from any backtracking evidence they might provide. In this way, I hope to salvage an important part of the idea that causes generally provide evidence for their effects. My argument relies heavily on the theory of causal Bayesian networks developed in J. Pearl's Causality and P. Spirtes, C. Glymour, and R. Scheines's Causation, Prediction and Search. In particular, I will show how to use Pearl's method of "adjustment for direct causes" to define functions that split the evidence that a cause provides for its effect into a "direct" and "backtracking" part (at least in the context of Markovian causal graphs). I will show that my use of this method does not commit me to any problematic "interventionist" metaphysics of causation of the sort that Pearl recommends. Indeed, I shall argue that a proper understanding of the epistemology of causation does not require a commitment to any specific metaphysics of the notion. If time allows I will discuss some of the limitations of the "causal net" approach in the context of my project, and will say something about how these limitations might be overcome. A complete copy of the talk will be on the web on Thursday, May 30 at http://www-personal.umich.edu/~jjoyce/."
"The Axiomatic Foundations of Bayesian Decision Theory."
PhD Dissertation, University of Michigan, 1992.
Abstract in Dissertation Abstracts International (November 1992), 53(5A):1540-A.
Abstract:"Bayesian decision theorists argue that rational agents should always perform acts that maximize subjective expected utility. To justify this claim, they prove representation theorems which are designed to show that any decision maker whose beliefs and desires satisfy reasonable axiomatic constraints will necessarily behave like an expected utility maximizer. The existence of such a representation result is a prerequisite for any adequate account of rational choice because one is only able to determine what a decision theory says about beliefs and desires by looking at the axioms used in the proof of its representation result. I examine a number of versions of decision theory and their representation theorems. Particular attention is paid to so-called causal and evidential decision theories. It is argued that only the latter has an adequate representation which is found in a theorem due to Ethan Bolker which was adapted to the decision theoretic context by R. Jeffrey. I remove the single outstanding problem with Bolker's theorem by reformulating it in a way which yields a unique probability and utility representation. This is possible because, unlike Bolker, I make use of axioms which govern not only preference but comparative probability. I show how this reformulated version of Bolker's result can be further generalized to a representation theorem for a generic theory of conditional expected utility whose basic term is a function which measures the strength of an agent's desires when he supposes that various hypotheses are true. Evidential and causal decision theories are show to be special cases of this generic theory. They differ only in the interpretation they give to the notion of supposition. The evidential account interprets it indicatively, while the causal account views it subjunctively. Finally, I show how my generic representation theorem for conditional decision theory can serve as a foundation for both causal and evidential decision theories. This provides the first fully adequate representation result for causal decision theory, thereby removing its most serious defect."
Review of Edward F. McClennen's Rationality and Dynamic Choice: Foundational Explorations. Philosophical Books (January 1992), 33(1):27-30.
Editor of issue on "Decision Theory" Philosophical Topics, (Spring 1993), 21(1).
(with Allan Gibbard.) "Causal Decision Theory." In Salvador Barberà, Peter J. Hammond, and Christian Seidl, eds., Handbook of Utility Theory, Vol. 1: Principles, pp. 701-740. Dordrecht & Boston: Kluwer, 1998.
"A Non-Pragmatic Vindication of Probabilism." Philosophy of
Science (December 1998), 65(4):575-603.
"The pragmatic character of the Dutch book argument makes it unsuitable as an "epistemic" justification for the fundamental probabilist dogma that rational partial beliefs must conform to the axioms of probability. To secure an appropriately epistemic justification for this conclusion, one must explain what it means for a system of partial beliefs to accurately represent the state of the world and then show that partial beliefs that violate the laws of probability are invariably less accurate than they could be otherwise."
Review of Brian Skyrms' The Evolution of the Social Contract. Philosophical Books (April 1998), 39(2):137-139.
The Foundations of Causal Decision Theory.
Cambridge Studies in Probability, Induction, and Decsion Theory.
Cambridge & New York: Cambridge University Press, 1999.
Bradley, Richard. Economics and Philosophy (October 2001), 17(2):281-288.
Eells, Ellery. British Journal for the Philosophy of Science (December 2000), 51(4):893-900.
Janusz, Mirek. Philosophical Review (April 2001), 110(2):296-300.
Levi, Isaac. Journal of Philosophy (July 2000), 97(7):387-402.
"Why We Still Need the Logic of Decision." Philosophy of Science (2000), 67(3)Supplement:S1-S13.
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