It is a platitude among decision theorists that agents should choose their actions so as to maximize expected value. But exactly how to define expected value is contentious. Evidential decision theory (henceforth EDT), causal decision theory (henceforth CDT), and a theory proposed by Ralph Wedgwood that this essay will call benchmark theory (BT) all advise agents to maximize different types of expected value. Consequently, their verdicts sometimes conflict. In certain famous cases of conflict—medical Newcomb problems—CDT and BT seem to get (...) things right, while EDT seems to get things wrong. In other cases of conflict, including some recent examples suggested by Andy Egan, EDT and BT seem to get things right, while CDT seems to get things wrong. In still other cases, EDT and CDT seems to get things right, while BT gets things wrong. It's no accident, this essay claims, that all three decision theories are subject to counterexamples. Decision rules can be reinterpreted as voting rules, where the voters are the agent's possible future selves. The problematic examples have the structure of voting paradoxes. Just as voting paradoxes show that no voting rule can do everything we want, decision-theoretic paradoxes show that no decision rule can do everything we want. Luckily, the so-called “tickle defense” establishes that EDT, CDT, and BT will do everything we want in a wide range of situations. Most decision situations, this essay argues, are analogues of voting situations in which the voters unanimously adopt the same set of preferences. In such situations, all plausible voting rules and all plausible decision rules agree. (shrink)

"[This book] proposes new foundations for the Bayesian principle of rational action, and goes on to develop a new logic of desirability and probabtility."—Frederic Schick, _Journal of Philosophy_.

This paper looks at a dispute decision theory about how best to characterize expected utility maximization and express the logic of rational choice. Where A1, … , An are actions open to some particular agent, and S1, … , Sn are mutually exclusive states of the world such that the agent knows at least one of which obtains, does the logic of rational choice require an agent to consider the conditional probability of choice Ai given that some state Si obtains, (...) Prob(Ai/Si). Or, is the logic of choice better represented by considering the probability of the counterfactual if Ai then Si, Prob(Ai ⟥-> Si). Causal decision theory, developed by Allan Gibbard, William Harper, and David Lewis defend the counterfactual analysis; whereas, Richard Jeffrey and others defend the conditional probability analysis, evidential decision theory. I argue that the problems posed by cases of decision instability favor evidential decision theory. (shrink)

Newcomb's problem and similar cases show the need to incorporate causal distinctions into the theory of rational decision; the usual noncausal decision theory, though simpler, does not always give the right answers. I give my own version of causal decision theory, compare it with versions offered by several other authors, and suggest that the versions have more in common than meets the eye.

I argue that standard decision theories, namely causal decision theory and evidential decision theory, both are unsatisfactory. I devise a new decision theory, from which, under certain conditions, standard game theory can be derived.

Practical deliberation often involves conditional judgements about what will happen if certain alternatives are pursued. It is widely assumed that the conditionals useful in deliberation are counterfactual or subjunctive conditionals. Against this, I argue that the conditionals of deliberation are indicatives. Key to the argument is an account of the relation between ‘straightforward’ future-directed conditionals like ‘If the house is not painted, it will soon look quite shabby’ and ‘ “were”ed-up’ FDCs like ‘If the house were not to be painted, (...) it would soon look quite shabby’: an account on which both of these types of FDCs are grouped with the indicatives for semantic treatment and on which, while conditionals of both types are properly used in means/ends deliberations, those of the ‘were’ed-up variety are especially well suited for that purpose. (shrink)

Many philosophers (myself included) have been converted to causal decision theory by something like the following line of argument: Evidential decision theory endorses irrational courses of action in a range of examples, and endorses “an irrational policy of managing the news”. These are fatal problems for evidential decision theory. Causal decision theory delivers the right results in the troublesome examples, and does not endorse this kind of irrational news-managing. So we should give up evidential decision theory, and be causal decision (...) theorists instead. Unfortunately, causal decision theory has its own family of problematic examples for which it endorses irrational courses of action, and its own irrational policy that it is committed to endorsing. These are, I think, fatal problems for causal decision theory. I wish that I had another theory to offer in its place. (shrink)

Brian Skyrms constructs a theory of "dynamic deliberation" and uses it to investigate rational decision-making in cases of strategic interaction. This illuminating book will be of great interest to all those in many disciplines who use decision theory and game theory to study human behavior and thought. Skyrms begins by discussing the Bayesian theory of individual rational decision and the classical theory of games, which at first glance seem antithetical in the criteria used for determining action. In his effort to (...) show how methods for dealing with information feedback can be productively combined, the author skillfully leads us through the mazes of equilibrium selection, the Nash equilibria for normal and extensive forms, structural stability, causal decision theory, dynamic probability, the revision of beliefs, and, finally, good habits for decision. The author provides many clarifying illustrations and a handy appendix called "Deliberational Dynamics on Your Personal Computer." His powerful model has important implications for understanding the rational origins of convention and the social contract, the logic of nuclear deterrence, the theory of good habits, and the varied strategies of political and economic behavior. (shrink)

This volume is devoted to Lewis's work in ethics and social philosophy. Topics covered include the logic of obligation and permission; decision theory and its relation to the idea that beliefs might play the motivating role of desires; a subjectivist analysis of value; dilemmas in virtue ethics; the problem of evil; problems about self-prediction; social coordination, linguistic and otherwise; alleged duties to rescue distant strangers; toleration as a tacit treaty; nuclear warfare; and punishment. This collection, and the two preceding volumes, (...) will disseminate more widely the work of an eminent and influential contemporary philosopher. (shrink)

This is about the rights and wrongs of bringing people into existence. In a nutshell: sometimes what matters is not what would have happened to you, but what would have happened to the person who would have been in your position, even if that person never actually exists.

Practical deliberation often involves conditional judgements about what will (likely) happen if certain alternatives are pursued. It is widely assumed that the conditionals useful in deliberation are counterfactual or subjunctive conditionals. Against this, I argue that the conditionals of deliberation are indicatives. Key to the argument is an account of the relation between 'straightforward' future-directed conditionals like ' If the house is not painted, it will soon look quite shabby' and * "w e r e ' ' e d F (...) D C s like ' If the house were not to be painted, it would soon look quite shabby': an account on which both of these types of FDCs are grouped with the indicatives for semantic treatment and on which, while conditionals of both types are properly used in means/ends deliberations, those of the ' were'ed-up variety are especially well suited for that purpose. (shrink)

J. Howard Sobel has long been recognized as an important figure in philosophical discussions of rational decision. He has done much to help formulate the concept of causal decision theory. In this volume of essays Sobel explores the Bayesian idea that rational actions maximize expected values, where an action's expected value is a weighted average of its agent's values for its possible total outcomes. Newcomb's Problem and The Prisoner's Dilemma are discussed, and Allais-type puzzles are viewed from the perspective of (...) causal world Bayesianism. The author establishes principles for distinguishing options in decision problems, and studies ways in which perfectly rational causal maximizers can be capable of resolute choices. Sobel also views critically Gauthier's revisionist ideas about maximizing rationality. This collection will be a desideratum for anyone working in the field of rational choice theory, whether in philosophy, economics, political science, psychology or statistics. Howard Sobel's work in decision theory is certainly among the most important, interesting and challenging that is being done by philosophers. (shrink)

In some decision problems adoption of an option furnishes evidence about the option's consequences. Rational decisions take account of that evidence, although it makes an option's adoption changes the option's expected utility.

Causal decision theory produces decision instability in cases such as Death in Damascus where a decision itself provides evidence concerning the utility of options. Several authors have proposed ways of handling this instability. William Harper (1985 and 1986) advances one of the most elegant proposals. He recommends maximizing causal expected utility among the options that are causally ratifiable. Unfortunately, Harper's proposal imposes certain restrictions; for instance, the restriction that mixed strategies are freely available. To obtain a completely general method of (...) handling decision instability, I step outside the confines of pure causal decision theory. I introduce a new kind of backtracking expected utility and propose maximizing it among the options that are causally ratifiable. In other words, I propose a hierarchical maximization of (1) conditional causal expected utility and (2) the new backtracking expected utility. I support this proposal with some intuitive considerations concerning the distinction between optimality and conditional optimality. And I prove that the proposal yields a solution in every finite decision problem. (shrink)

We use “I’ll be glad I did it” reasoning all the time. For example, last night I was trying to decide whether to work on this paper or go out to a movie. I realized that if I worked on the paper, then today I would be glad I did it. Whereas, if I went out to the movie, today I would regret it. This enabled me to see that I should work on the paper rather than going out to (...) a movie. This looks like excellent reasoning: Paper argument: 1. If I work on my paper, I’ll be glad I did it. 2. Therefore, I should work on my paper. When we’re having trouble making a big life decision, we often try to picture what will happen each way we might choose, and imagine how we’ll feel in that outcome. When choosing between two jobs, we might use this reasoning. Suppose you are choosing between two jobs and you know quite a lot about what the two jobs will be like. In one, you will make a lot of money but have to work eighty-hour weeks and see little of your family. In the other, you will make considerably less money—though enough to support yourself and your family. You’ll have much more time for your family. The money is attractive. But overall, you realize you’ll be glad to have the time with your family if you take the second job—you’ll be glad you made that choice. It seems this is a good way of realizing that you should take the second job. (shrink)