A substantial body of empirical evidence shows that individuals overweight extreme events and act in conflict with the expected utility theory. These findings were the primary motivation behind the development of a rank-dependent utility theory for choice under uncertainty. The purpose of this paper is to demonstrate that some simple empirical rules of thumb for choice under uncertainty are consistent with the rank-dependent utility theory.

We propose a class [I,S] of loss functions for modeling the imprecise preferences of the decision maker in Bayesian Decision Theory. This class is built upon two extreme loss functions I and S which reflect the limited information about the loss function. We give an approximation of the set of Bayes actions for every loss function in [I,S] and every prior in a mixture class; if the decision space is a subset of R, we obtain the exact set.

Andy Egan's Smoking Lesion and Psycho Button cases are supposed to be counterexamples to Causal Decision Theory. This paper argues that they are not: more precisely, it argues that if CDT makes the right call in Newcomb's problem then it makes the right call in Egan cases too.

Sophisticated ‘tickle’-style defences of Evidential Decision Theory take your motivational state to screen off your act from any state that is causally independent of it, thus ensuring that EDT and CDT converge. That leads to unacceptable instability in cases in which the correct action is obvious. We need a more liberal conception of what the agent controls. It follows that an ordinary deliberator should sometimes consider the past and not only the future to be subject to her present choice.

Models in decision theory and game theory assume that preferences are determinate: for any pair of possible outcomes, a and b, an agent either prefers a to b, prefers b to a, or is indifferent as between a and b. Preferences are also assumed to be stable: provided the agent is fully informed, trivial situational influences will not shift the order of her preferences. Research by behavioral economists suggests, however, that economic and hedonic preferences are to some degree indeterminate and (...) unstable, which in turn suggests that other sorts of preferences may suffer the same problem. Even fully informed agents do not always determinately prefer a to b, prefer b to a, or feel indifferent as between a and b. Seemingly trivial situational influences rearrange the order of their preferences. One could respond that decision theory and game theory are not meant to describe actual behavior, and that they instead adumbrate an ideal of rationality from which human action diverges in various ways. When the divergences are small and systematic, they help us identify the heuristics that conspire to help people approximate rationality. One such heuristic, dubbed the Wilde heuristic, is explored. However, the divergences documented by behavioral economists threaten to be too large to handle through idealization. The Rum Tum Tugger Model, in which indifference is intransitive, is spelled out as one promising way for decision and game theory to retrench. Preferences may be locally unstable and indeterminate, but when the differences between options are sufficiently large, they approximate stability and determinacy. (shrink)

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.

Orthodox decision theory gives no advice to agents who hold two goods to be incomparable in value, because such agents will have negatively intransitive preferences. According to standard treatments, such agents are irrational, despite widespread evidence of incomparable goods in ordinary life. Prospectism is a recent proposal, due to Caspar Hare, to extend standard decision theory so as to cope with incomparability in general, and negatively intransitive preferences in particular. In this paper, we argue that prospectism is inadequate, on three (...) grounds. First, prospectism conflates decision scenarios that, intuitively, rational agents may permissibly treat as different. Second, prospectism leads to violations of a principle of rationality closely related to dominance. Finally, we suggest that what little intuitive appeal prospectism has can be diagnosed as arising from a psychological heuristic that has no normative status. (shrink)

Decision theory is a theory of rationality, but the concept of rationality has several different dimensions. Making decision theory more realistic with respect to one dimension may well have the result of making it less realistic in another dimension. This paper illustrates this tension in the context of sequential choice. Trying to make decision theory more realistic by accommodating resoluteness and commitment brings the normative assessment dimension of rationality into conflict with the action-guiding dimension. In the case of resolute choice (...) the conflict comes because of a clash of perspectives. The perspective from which resolute choice seems normatively compelling is not the perspective from which it can serve the purpose of guiding action. (shrink)

This paper argues that we need to look beyond Bayesian decision theory for an answer to the general problem of making rational decisions under uncertainty. The view that Bayesian decision theory is only genuinely valid in a small world was asserted very firmly by Leonard Savage [18] when laying down the principles of the theory in his path-breaking Foundations of Statistics. He makes the distinction between small and large worlds in a folksy way by quoting the proverbs ”Look before you (...) leap” and ”Cross that bridge when you come to it”. You are in a small world if it is feasible always to look before you leap. You are in a large world if there are some bridges that you cannot cross before you come to them. As Savage comments, when proverbs conflict, it is proverbially true that there is some truth in both—that they apply in different contexts. He then argues that some decision situations are best modeled in terms of a small world, but others are not. He explicitly rejects the idea that all worlds can be treated as small as both ”ridiculous” and ”preposterous”. The first half of his book is then devoted to a very successful development of the set of ideas now known as Bayesian decision theory for use in small worlds. The second half of the book is an attempt to develop a quite different set of ideas for use in large worlds, but this part of the book is usually said to be a failure by those who are aware of its existence.2 Frank Knight [15] draws a similar distinction between making decision under risk or uncertainty.3 The pioneering work of Gilboa and Schmeidler [7] on making.. (shrink)

In this paper I discuss some of the mathematics behind an often quoted existence theorem from Richard Jeffrey's The Logic of Decision (Jeffrey 1990) in order to pose several new questions about the meaning and value of that mathematics for decision theory.

Decision theory is concerned with how agents should act when the consequences of their actions are uncertain. The central principle of contemporary decision theory is that the rational choice is the choice that maximizes subjective expected utility. This entry explains what this means, and discusses the philosophical motivations and consequences of the theory. The entry will consider some of the main problems and paradoxes that decision theory faces, and some of responses that can be given. Finally the entry will briefly (...) consider how decision theory applies to choices involving more than one agent. (shrink)

This paper provides new foundations for Bayesian Decision Theory based on a representation theorem for preferences defined on a set of prospects containing both factual and conditional possibilities. This use of a rich set of prospects not only provides a framework within which the main theoretical claims of Savage, Ramsey, Jeffrey and others can be stated and compared, but also allows for the postulation of an extended Bayesian model of rational belief and desire from which they can be derived as (...) special cases. The main theorem of the paper establishes the existence of a such a Bayesian representation of preferences over conditional prospects, i.e. the existence of a pair of real-valued functions respectively measuring the agent’s degrees of belief and desire and which satisfy the postulated rationality conditions on partial belief and desire. The representation of partial belief is shown to be unique and that of partial desire, unique up to a linear transformation. (shrink)

Conditional attitudes are not the attitudes an agent is disposed to acquire in event of learning that a condition holds. Rather they are the components of agent's current attitudes that derive from the consideration they give to the possibility that the condition is true. Jeffrey's decision theory can be extended to include quantitative representation of the strength of these components. A conditional desirability measure for degrees of conditional desire is proposed and shown to imply that an agent's degrees of conditional (...) belief are conditional probabilities. Rational conditional preference is axiomatised and by application of Bolker's representation theorem for rational preferences it is shown that conditional preference rankings determine the existence of probability and desirability measures that agree with them. It is then proven that every conditional desirability function agrees with an agent's conditional preferences and, under certain assumptions, every desirability function agreeing with an agent's conditional preferences is a conditional desirability function agreeing with her unconditional preferences. (shrink)

This paper investigates the role of conditionals in hypothetical reasoning and rational decision making. Its main result is a proof of a representation theorem for preferences defined on sets of sentences (and, in particular, conditional sentences), where an agent’s preference for one sentence over another is understood to be a preference for receiving the news conveyed by the former. The theorem shows that a rational preference ordering of conditional sentences determines probability and desirability representations of the agent’s degrees of belief (...) and desire that satisfy, in the case of non-conditional sentences, the axioms of Jeffrey’s decision theory and, in the case of conditional sentences, Adams’ expression for the probabilities of conditionals. Furthermore, the probability representation is shown to be unique and the desirability representation unique up to positive linear transformation. (shrink)

It is sometimes argued that the fact that possession of perfect knowledge about the future is impossible, means that it is impossible for decisions to be rational. This reasoning is fallacious. If rationality is given a new interpretation, then decisions can be considered rational. A theory of decision that has as its basis Peirce’s theory of abduction can provide a new way of understanding decisions as rational processes. The Peircean theory of decision (i) considers decisions as part of a complete (...) strategy, and (ii) shows that decision making is governed by the same rules as scientific abduction. These rules are neither permissive rules like rules of deductive inference nor predictive like laws of nature, but rather genuine laws of conduct that determine what step should be made, if a given end is to be reached. (shrink)

It is well known that classical, aka ‘sharp’, Bayesian decision theory, which models belief states as single probability functions, faces a number of serious difficulties with respect to its handling of agnosticism. These difficulties have led to the increasing popularity of so-called ‘imprecise’ models of decision-making, which represent belief states as sets of probability functions. In a recent paper, however, Adam Elga has argued in favour of a putative normative principle of sequential choice that he claims to be borne out (...) by the sharp model but not by any promising incarnation of its imprecise counterpart. After first pointing out that Elga has fallen short of establishing that his principle is indeed uniquely borne out by the sharp model, I cast aspersions on its plausibility. I show that a slight weakening of the principle is satisfied by at least one, but interestingly not all, varieties of the imprecise model and point out that Elga has failed to motivate his stronger commitment. (shrink)

In normative decision theory, the weight of an uncertain event in a decision is governed solely by the probability of the event. A large body of empirical research suggests that a single notion of probability does not accurately capture peoples' reactions to uncertainty. As early as the 1920s, Knight made the distinction between cases where probabilities are known and where probabilities are unknown. We distinguish another case –- the unknowable uncertainty –- where the missing information is unavailable to all. We (...) propose that missing information influences the attractiveness of a bet contingent upon an uncertain event, especially when the information is available to someone else. We demonstrate that the unknowable uncertainty –- falls in preference somewhere in between the known and the known uncertainty. (shrink)

There is a surprising disconnect between formal rational choice theory and philosophical work on reasons. The one is silent on the role of reasons in rational choices, the other rarely engages with the formal models of decision problems used by social scientists. To bridge this gap, we propose a new, reason-based theory of rational choice. At its core is an account of preference formation, according to which an agent’s preferences are determined by his or her motivating reasons, together with a (...) ‘weighing relation’ between different combinations of reasons. By explaining how someone’s preferences may vary with changes in his or her motivating reasons, our theory illuminates the relationship between deliberation about reasons and rational choices. Although primarily positive, the theory can also help us think about how those preferences and choices ought to respond to normative reasons. (shrink)

Abstract I analyse the relationship between the Ramsey Test (RT) for the acceptance of indicative conditionals and the so-called problem of decision-instability. In particular, I argue that the situations which allegedly bring about this problem are troublesome just in case the relevant conditionals are evaluated by non-suppositional versions, e.g. causal/evidential, of the test. In contrast, a suppositional RT, by highlighting the metacognitive nature of the evaluation of indicative conditionals, allows an agent to run a simulation of such evaluation, without yet (...) committing her to the acceptance of such conditionals. I conclude that a suppositional interpretation of RT is superior to its nonsuppositional counterparts and by briefly showing that a suppositional RT is compatible with a deliberational decision theory. (shrink)

I develop and defend a version of what I call Disposition-Based Decision Theory (or DBDT). I point out important problems in David Gauthier’s (1985, 1986) formulation of DBDT, and carefully develop a more defensible formulation. I then compare my version of DBDT to the currently most widely accepted decision theory, Causal Decision Theory (CDT). Traditional intuition-based arguments fail to give us any strong reason to prefer either theory over the other, but I propose an alternative strategy for resolving this debate. (...) I argue that we should embrace DBDT because it does better than CDT at the work that we, as a matter of empirical fact, commonly call upon a notion of rationality to do. (shrink)

Many philosophers hold that for various reasons there must be psychological laws governing beliefs and desires. One of the few serious examples that they offer is the _belief-desire law_, which states, roughly, that _ceteris paribus_ people do what they believe will satisfy their desires. This paper argues that, in fact, there is no such law. In particular, decision theory does not support the contention that there is such a law.

This paper is partly a tribute to Richard Jeffrey, partly a reflection on some of his writings, The Logic of Decision in particular. I begin with a brief biography and some fond reminiscences of Dick. I turn to some of the key tenets of his version of Bayesianism. All of these tenets are deployed in my discussion of his response to the St. Petersburg paradox, a notorious problem for decision theory that involves a game of infinite expectation. Prompted by that (...) paradox, I conclude with some suggestions of avenues for future research. (shrink)

We argue that indeterminate probabilities are not only rationally permissible for a Bayesian agent, but they may even be rationally required . Our first argument begins by assuming a version of interpretivism: your mental state is the set of probability and utility functions that rationalize your behavioral dispositions as well as possible. This set may consist of multiple probability functions. Then according to interpretivism, this makes it the case that your credal state is indeterminate. Our second argument begins with our (...) describing a world that plausibly has indeterminate chances. Rationality requires a certain alignment of your credences with corresponding hypotheses about the chances. Thus, if you hypothesize the chances to be indeterminate, your will inherit their indeterminacy in your corresponding credences. Our third argument is motivated by a dilemma. Epistemic rationality requires you to stay open-minded about contingent matters about which your evidence has not definitively legislated. Practical rationality requires you to be able to act decisively at least sometimes. These requirements can conflict with each other-for thanks to your open-mindedness, some of your options may have undefined expected utility, and if you are choosing among them, decision theory has no advice to give you. Such an option is playing Nover and Hájek’s Pasadena Game , and indeed any option for which there is a positive probability of playing the Pasadena Game. You can serve both masters, epistemic rationality and practical rationality, with an indeterminate credence to the prospect of playing the Pasadena game. You serve epistemic rationality by making your upper probability positive-it ensures that you are open-minded. You serve practical rationality by making your lower probability 0-it provides guidance to your decision-making. No sharp credence could do both. (shrink)

This article is an attempt at a systematic account of decision making under greater uncertainty than what traditional, mathematically oriented decision theory can cope with. Four components of great uncertainty are distinguished: (1) the identity of the options is not well determined (uncertainty of demarcation) ; (2) the consequences of at least some option are unknown (uncertainty of consequences); (3) it is not clear whether information obtained from others, such as experts, can be relied on (uncertainty of reliance); and (4) (...) the values relevant for the decision are not determined with sufficient precision (uncertainty of values). Some possible strategy types are proposed for each of these components. Decisions related to environmental issues are used to illustrate the proposals. (shrink)

This text is a non-technical overview of modern decision theory. It is intended for university students with no previous acquaintance with the subject, and was primarily written for the participants of a course on risk analysis at Uppsala University in 1994.

This paper includes a survey of decision theories directed toward exploring the adequacy of alternative approaches for application to game theoretic reasoning, a review of the classic results of von Neumann and Morgenstern and Nash about equilibrium solutions, an account of a recent challenge to the idea that solutions should be equilibria, and, finally, an explicit reconstruction and defense (using the resources of causal decision theory) of the classic indirect argument for equilibrium solutions.

"Frederic Schick, Journal of Philosophy This book uses elementary logical and mathematical means to philosophical end: elucidation of the notions of subjective ...

Richard Jeffrey is beyond dispute one of the most distinguished and influential philosophers working in the field of decision theory and the theory of knowledge. His work is distinctive in showing the interplay of epistemological concerns with probability and utility theory. Not only has he made use of standard probabilistic and decision theoretic tools to clarify concepts of evidential support and informed choice, he has also proposed significant modifications of the standard Bayesian position in order that it provide a better (...) fit with actual human experience. Probability logic is viewed not as a source of judgment but as a framework for explaining the implications of probabilistic judgments and their mutual compatability This collection of essays spans a period of some 35 years and includes what have become some of the classic works in the literature. There is also one completely new piece, while in many instances Jeffrey includes afterthoughts on the older essays. (shrink)

In The Logic of Decision Richard Jeffrey defends a version of expected utility theory that advises agents to choose acts with an eye to securing evidence for thinking that desirable results will ensue. Proponents of "causal" decision theory have argued that Jeffrey's account is inadequate because it fails to properly discriminate the causal features of acts from their merely evidential properties. Jeffrey's approach has also been criticized on the grounds that it makes it impossible to extract a unique probability/utility representation (...) from a sufficiently rich system of preferences (given a zero and unit for measuring utility). The existence of these problems should not blind us to the fact that Jeffrey's system has advantages that no other decision theory can match: it can be underwritten by a particularly compelling representation theorem proved by Ethan Bolker; and it has a property called partition invariance that every reasonable theory of rational choice must possess. I shall argue that the non-uniqueness problem can be finessed, and that it is impossible to adequately formulate causal decision theory, or any other, without using Jeffrey's theory as one's basic analysis of rational desire. (shrink)

This article focuses on both daily forms of weakness of will as discussed in the philosophical debate (usually referred to as akrasia) and psychopathological phenomena as impairments of decision making. We argue that both descriptions of dysfunctional decision making can be organized within a common theoretical framework that divides the decision making process in three different stages: option generation, option selection, and action initiation. We first discuss our theoretical framework (building on existing models of decision-making stages), focusing on option generation (...) as an aspect that has been neglected by previous models. In the main body of this article, we review how both philosophy and neuropsychiatry have provided accounts of dysfunction in each decision-making stage, as well as where these accounts can be integrated. Also, the neural underpinnings of dysfunction in the three different stages are discussed. We conclude by discussing advantages and limitations of our integrative approach. (shrink)

Is Bayesian decision theory a panacea for many of the problems in epistemology and the philosophy of science, or is it philosophical snake-oil? For years a debate had been waged amongst specialists regarding the import and legitimacy of this body of theory. Mark Kaplan had written the first accessible and non-technical book to address this controversy. Introducing a new variant on Bayesian decision theory the author offers a compelling case that, while no panacea, decision theory does in fact have the (...) most profound consequences for the way in which philosophers think about inquiry, criticism and rational belief. The new variant on Bayesian theory is presented in such a way that a non-specialist will be able to understand it. The book also offers new solutions to some classic paradoxes. It focuses on the intuitive motivations of the Bayesian approach to epistemology and addresses the philosophical worries to which it has given rise. (shrink)

Suppose my utilities are representable by a set of utility assignments, each defined for atomic sentences; suppose my beliefs are representable by a set of probability assignments. Then each of my utility assignments together with each of my probability assignments will determine a utility assignment to non-atomic sentences, in a familiar way. This paper is concerned with the question, whether I am committed to all the utility assignments so constructible. Richard Jeffrey (1984) says (in effect) "no", Isaac Levi (1974) says (...) "yes". I argue for "no", and raise in passing a problem for Levi. (shrink)

A case is made for supposing that the total probability accounted for in a decision analysis is less than unity. This is done by constructing a measure on the set of all codes for computable functions in such a way that the measure of every effectively accountable subset is bounded by a number <1. The consistency of these measures with the Savage axioms for rational preference is established. Implications for applied decision theory are outlined.

This book is a major contribution to decision theory, focusing on the question of when it is rational to accept scientific theories.

According to classical decision theory, an agent realises at time t the option with maximum expected utility (determined by his beliefs and desires at t), where the relevant options are possible actions performed at t. I consider an alternative according to which the relevant options are in general plans, complex courses of action extending into the future.

We consider the dispute between causal decision theorists and evidential decision theorists over Newcomb-like problems. We introduce a framework relating causation and directed graphs developed by Spirtes et al. (1993) and evaluate several arguments in this context. We argue that much of the debate between the two camps is misplaced; the disputes turn on the distinction between conditioning on an event E as against conditioning on an event I which is an action to bring about E. We give the essential (...) machinery for calculating the effect of an intervention and consider recent work which extends the basic account given here to the case where causal Knowledge is incomplete. (shrink)

I distinguish between risks in which most people will do badly from those in which few will, though some will do very badly.

This paper contributes to a theory of rational choice for decision-makers with incomplete preferences due to partial ignorance, whose beliefs are representable as sets of acceptable priors. We focus on the limiting case of `Complete Ignorance' which can be viewed as reduced form of the general case of partial ignorance. Rationality is conceptualized in terms of a `Principle of Preference-Basedness', according to which rational choice should be isomorphic to asserted preference. The main result characterizes axiomatically a new choice-rule called `Simultaneous (...) Expected Utility Maximization'. It can be interpreted as agreement in a bargaining game (Kalai-Smorodinsky solution) whose players correspond to the (extremal) `acceptable priors' among which the decision maker has suspended judgment. An essential but non-standard feature of Simultaneous Expected Utility choices is their dependence on the entire choice set. This is justified by the conception of optimality as compromise rather than as superiority in pairwise comparisons. (shrink)

Reasoning under uncertainty, that is, making judgements with only partial knowledge, is a major theme in artificial intelligence. Professor Paris provides here an introduction to the mathematical foundations of the subject. It is suited for readers with some knowledge of undergraduate mathematics but is otherwise self-contained, collecting together the key results on the subject, and formalising within a unified framework the main contemporary approaches and assumptions. The author has concentrated on giving clear mathematical formulations, analyses, justifications and consequences of the (...) main theories about uncertain reasoning, so the book can serve as a textbook for beginners or as a starting point for further basic research into the subject. It will be welcomed by graduate students and research workers in logic, philosophy, and computer science as a textbook for beginners, a starting point for further basic research into the subject, and not least, an account of how mathematics and artificial intelligence can complement and enrich each other. (shrink)

This up-to-date introduction to decision theory offers comprehensive and accessible discussions of decision-making under ignorance and risk, the foundations of utility theory, the debate over subjective and objective probability, Bayesianism, causal decision theory, game theory, and social choice theory. No mathematical skills are assumed, and all concepts and results are explained in non-technical and intuitive as well as more formal ways. There are over 100 exercises with solutions, and a glossary of key terms and concepts. An emphasis on foundational aspects (...) of normative decision theory (rather than descriptive decision theory) makes the book particularly useful for philosophy students, but it will appeal to readers in a range of disciplines including economics, psychology, political science and computer science. • Has over 100 end of chapter review questions and exercises with solutions • Includes a chapter on how to draw a decision matrix • Explains the link between individual decision making, game theory and social choice theory Contents Preface; 1. Introduction; 2. The decision matrix; 3. Decisions under ignorance; 4. Decisions under risk; 5. Utility; 6. The mathematics of probability; 7. The philosophy of probability; 8. Why should we accept the preference axioms; 9. Causal vs. evidential decision theory; 10. Bayesian vs. non-Bayesian decision theory; 11. Game theory I: basic concepts and zero sum games; 12. Game theory II: nonzero sum and co-operative games; 13. Social choice theory; 14. Overview of descriptive decision theory; Appendix A. Glossary; Appendix B. Proof of the von Neumann-Morgenstern theorem; Further reading; Index. (shrink)

Counterexamples are constructed for classical decision theory, turning on the fact that actions must often be chosen in groups rather than individually, i.e., the objects of rational choice are plans. It is argued that there is no way to define optimality for plans that makes the finding of optimal plans the desideratum of rational decision-making. An alternative called “locally global planning” is proposed as a replacement for classical decision theory. Decision-making becomes a non-terminating process without a precise target rather than (...) a terminating search for an optimal solution. (shrink)

The objective of this paper is to construct an implementable theory of rational decision-making for cognitive agents subject to realistic resource constraints. It is argued that decision-making should select actions indirectly by selecting plans that prescribe them. It is also argued that although expected values provide the tool for evaluating plans, plans cannot be compared straightforwardly in terms of their expected values, and the objective of a realistic agent cannot be to find optimal plans. The theory of Locally Global planning (...) is proposed as a realistic alternative to standard "maximizing" theories of rational decision-making. (shrink)

Decision-theoretic planning is normally based on the assumption that plans can be compared by comparing their expected-values, and the objective is to find an optimal plan. This is typically defended by reference to classical decision theory. However, classical decision theory is actually incompatible with this “simple plan-based decision theory”. A defense of plan-based decision theory must begin by showing that classical decision theory is incorrect insofar as the two theories conflict, so this paper begins by raising objections to classical decision (...) theory . First, there is a discussion of the considerations arising out of the Newcomb problem that have given rise to causal decision theory. Next, counterexamples are constructed for classical decision theory turning on the fact that an agent may be unable to perform an action, and may even be unable to try to perform an action. A proposal is made for how to repair classical decision theory in light of these counterexamples. But then turning to the concept of an “alternative” that is presupposed by classical decision theory, it is argued that actions must often be chosen in groups rather than individually, i.e., the objects of rational choice are plans. It is argued that optimality cannot be defined for plans, and even if it could be, it would not be reasonable to require rational agents to find optimal plans. So simple plan-based decision theory must also be rejected. An alternative called “locally global planning” is proposed as a replacement for both classical decision theory and simple plan-based decision theory. (shrink)

This paper investigates decision-theoretic planning in sophisticated autonomous agents operating in environments of real-world complexity. An example might be a planetary rover exploring a largely unknown planet. It is argued th a t existing algorithms for decision-theoretic planning are based on a logically incorrect theory of rational decision making. Plans cannot be evaluated directly in terms of their expected values, because plans can be of different scopes, and they can interact with other previously adopted plans. Furthermore, in the real world, (...) the search for optimal plans is completely intractable. An alternative theory of rational decision making is proposed, called “locally global planning”. (shrink)

Counterexamples are constructed for the theory of rational choice that results from a direct application of classical decision theory to ordinary actions. These counterexamples turn on the fact that an agent may be unable to perform an action, and may even be unable to try to perform an action. An alternative theory of rational choice is proposed that evaluates actions using a more complex measure, and then it is shown that this is equivalent to applying classical decision theory to "conditional (...) policies" rather than ordinary actions. (shrink)

Imagine that you receive information on the choices made by a decision maker (DM) from all subsets of some set X. You know nothing about the context of these choices. You look for an explanation for the DM’s behavior. You would probably look first for a single rationale explaining the behavior. Specifically, you would seek a rationalizing ordering—that is, a linear ordering on X, such that for every choice set A ⊆ X, the DM’s choice from A is the best (...) element in A according to the ordering. You recall that the “Independence of Irrelevant Alternatives” Axiom (IIA)—which requires that the chosen element from a set also be chosen from every subset that contains it—is a necessary and sufficient condition for the existence of such an explanation. (shrink)

Classic analysis of the subject and the development of personal probability; one of the greatest controversies in modern statistcal thought.

This is an important new book about human motivation, about the reasons people have for their actions. What is distinctively new about it is its focus on how people see or understand their situations, options, and prospects. By taking account of people's understandings (along with their beliefs and desires), Professor Schick is able to expand the current theory of decision and action. The author provides a perspective on the topic by outlining its history. He defends his new theory against criticism, (...) considers its formal structure, and shows at length how it resolves many currently debated problems: the problems of conflict and weakness of will, Allais' problem, Kahneman and Tversky's problems, Newcomb's problem, and others. The book will be of special interest to philosophers, psychologists, and economists. (shrink)

Most decisions in life involve ambiguity, where probabilities can not be meaningfully specified, as much as they involve probabilistic uncertainty. In such conditions, the aspiration to utility maximization may be self-deceptive. We propose “robust satisficing” as an alternative to utility maximizing as the normative standard for rational decision making in such circumstances. Instead of seeking to maximize the expected value, or utility, of a decision outcome, robust satisficing aims to maximize the robustness to uncertainty of a satisfactory outcome. That is, (...) robust satisficing asks, “what is a ‘good enough’ outcome,” and then seeks the option that will produce such an outcome under the widest set of circumstances. We explore the conditions under which robust satisficing is a more appropriate norm for decision making than utility maximizing. (shrink)

This paper offers a comparison between two decision rules for use when uncertainty is depicted by a non-trivial, convex2 set of probability functions Γ. This setting for uncertainty is different from the canonical Bayesian decision theory of expected utility, which uses a singleton set, just one probability function to represent a decision maker’s uncertainty. Justifications for using a non-trivial set of probabilities to depict uncertainty date back at least a half century (Good, 1952) and a foreshadowing of that idea can (...) be found even in Keynes’ (1921), where he allows that not all hypotheses may be comparable by qualitative probability – in accord with, e.g., the situation where the respective intervals of probabilities for two events merely overlap with no further (joint) constraints, so that neither of the two events is more, or less, or equally probable compared with the other. (shrink)

The "traditional" view of normative decision theory, as reported (for example) in chapter 2 of Luce and RaiÃa's [1957] classic work, Games and Decisions, proposes a reduction of sequential decisions problems to non-sequential decisions: a reduction of extensive forms to normal forms. Nonetheless, this reduction is not without its critics, both from inside and outside expected utility theory, It islay purpose in this essay to join with those critics by advocating the following thesis.

In Decision Theory as Philosophy, Mark Kaplan reissues a number of perennial questions within decision theory and epistemology, particularly regarding the relevance of decision theory to epistemology and the scope of an epistemology informed by a “modest” Bayesian decision theory. Much of Kaplan’s book represents a challenge to what he calls the “Orthodox” Bayesian theory of decision and evidence. His arguments turn positive in the fourth chapter, in which he argues for the “Assertion View” of belief---an attempted reconciliation of the (...) categorical notion of belief (as distinct from disbelief) with that of confidence, which comes in degrees. Theapproach to epistemology manifest in Decision Theory, while commendable in some respects, suffers fundamentally from its methodological commitment to the primacy of preference principles over and above distinctively epistemic principles. But to express this last misgiving is just to doubt whether decision theory has much of its own to contribute to epistemology. (shrink)

The actions that agents perform in social situations are often influenced by the moral justifications they are able to provide of their behaviour. Boltanski and Thévenot point out that this fact appears to be in tension with the standard models of social explanation which seek to explain behaviour in social situations in terms of self-interested motivations. In this note I consider this tension, and caution against reading too much into it.

It is shown that even if a process of ideal evidential deliberation that paid attention to its own progress would in every case lead to credences that made things probabilistically independent of actions of which they were believed to be causally independent; it would not in every case lead to agreement in the ultimate dictates of evidential and causal decision theories. This point is made by a decision problem in which the action prescribed by causal decision theory is not (as (...) it is in Newcomb's Problem) a dominant action. It is also shown that such non-dominance problems provide decisive objections to Ratificationism. (shrink)

The paper argues that the standard decision theoretic account of strategies and their rationality or optimality is much too narrow, that strategies should rather condition future action to future decision situations (a point of view already developed in my Grundlagen der Entscheidungstheorie, sect. 4.4), that practical deliberation must therefore essentially rely on a relation of superiority and inferiority between possible future decision situations, that all this allows to substantially broaden the theory of practical rationality, that a long list of points (...) attended to in the literature can be subsumed under the broadened perspective (including a novel view on the iterated prisoner's dilemma and on iterated Newcomb's problem, which, however, is revised in Spohn (2003) "Dependency Equilibria and the Causal Structure of Decision and Game Situation"), and that the task to complete and systematize this list indeed forms a fruitful research programme. (shrink)

Ex ante predicted outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. But error rates have error rates. We reapply measurements of uncertainty about the estimation errors of the estimation errors of an estimation treated as branching counterfactuals. Such recursions of epistemic uncertainty have markedly different distributial properties from conventional sampling error, and lead to fatter tails in the projections than in past realizations. Counterfactuals of error rates always lead to fat tails, regardless of (...) the probability distribution used. A mere .01% branching error rate about the STD (itself an error rate), and .01% branching error rate about that error rate, etc. (recursing all the way) results in explosive (and infinite) moments higher than 1. Missing any degree of regress leads to the underestimation of small probabilities and concave payoffs (a standard example of which is Fukushima). The paper states the conditions under which higher order rates of uncertainty (expressed in spreads of counterfactuals) alters the shapes the of final distribution and shows which a priori beliefs about conterfactuals are needed to accept the reliability of conventional probabilistic methods (thin tails or mildly fat tails). (shrink)

The combination of logic and game theory provides a fine-grained perspective on information and interaction dynamics, a Theory of Play. In this paper we lay down the main components of such a theory, drawing on recent advances in the logical dynamics of actions, preferences, and information. We then show how this fine-grained perspective has already shed new light on the long-term dynamics of information exchange, as well as on the much-discussed question of extensive game rationality.

In this paper I explore a version of standard (expected utility) decision theory in which the probability parameter is interpreted as an objective chance believed by agents to obtain and values of this parameter are fixed by indicative conditionals linking possible actions with possible outcomes. After reviewing some recent developments centering on the common-cause counterexamples to the standard approach, I introduce and briefly discuss the key notions in my own approach. (This approach has essentially the same results as the causal (...) approach in common-cause cases.) I then discuss the Rule of Dominance and find, in the context of the present proposal, that it cannot serve as an independent source of action justification. Turning next to Newcomb''s Problem, I argue that the much discussed issue of back-tracking counterfactuals is something of a red herring for decision theory. Once the twin distractions of back-tracking counterfactuals and Dominance Reasoning are set aside the 1-box solution emerges as a natural consequence of the present proposal. It is of interest that this proposal agrees with the causal approach in the standard common-cause examples and the expected-utility approach in the Newcomb case: one can be smart and rich and keep on smoking. (shrink)

Abstract. Suppose that several individuals who have separately assessed prior probability distributions over a set of possible states of the world wish to pool their individual distributions into a single group distribution, while taking into account jointly perceived new evidence. They have the option of (i) first updating their individual priors and then pooling the resulting posteriors or (ii) first pooling their priors and then updating the resulting group prior. If the pooling method that they employ is such that they (...) arrive at the same final distribution in both cases, the method is said to be externally Bayesian, a property first studied by Madansky (1964). We show that a pooling method for discrete distributions is externally Bayesian if and only if it commutes with Jeffrey conditioning, parameterized in terms of certain ratios of new to old odds, as in Wagner (2002), rather than in terms of the posterior probabilities of members of the disjoint family of events on which such conditioning originates. (shrink)

Orthodox Bayesian decision theory requires an agent’s beliefs representable by a real-valued function, ideally a probability function. Many theorists have argued this is too restrictive; it can be perfectly reasonable to have indeterminate degrees of belief. So doxastic states are ideally representable by a set of probability functions. One consequence of this is that the expected value of a gamble will be imprecise. This paper looks at the attempts to extend Bayesian decision theory to deal with such cases, and concludes (...) that all proposals advanced thus far have been incoherent. A more modest, but coherent, alternative is proposed. Keywords: Imprecise probabilities, Arrow’s theorem. (shrink)

Recent proposals that frame norms of action in terms of knowledge have been challenged by Bayesian decision theorists. Bayesians object that these knowledge-based norms conflict with the highly successful and established view that rational action is rooted in degrees of belief. I argue that the knowledge-based and Bayesian pictures are not as incompatible as these objectors have made out. Attending to the mechanisms of practical reasoning exposes space for both knowledge and degrees of belief to play their respective roles.

Theory choice can be approached in at least four ways. One of these calls for the application of decision theory, and this article endorses this approach. But applying standard forms of decision theory imposes an overly demanding standard of numeric information, supposedly satisfied by point-valued utility and probability functions. To ameliorate this difficulty, a version of decision theory that requires merely comparative utilities and plausibilities is proposed. After a brief summary of this alternative, the article illustrates how comparative decision theory (...) affords a rational reconstruction of decisions made by exemplary scientists in two cases of theory choice: Buffon’s law and the luminiferous ether. It also offers a rational reconstruction of two cases of theory diagnosis: Mendeleev’s anomalies and the Pioneer anomaly. (shrink)

Some decisions result in cognitive consequences such as information gained and information lost. The focus of this study, however, is decisions with consequences that are partly or completely noncognitive. These decisions are typically referred to as ‘real-life decisions’. According to a common complaint, the challenges of real-life decision making cannot be met by decision theory. This complaint has at least two principal motives. One is the maximizing objection that to require agents to determine the optimal act under real-world constraints is (...) unrealistic. The other is the precision objection that the numeric requirements for applying decision theory are overly demanding for real-life decisions. Responses to both objections are aired in the History section of this chapter. The maximizing objection is addressed with reference to work by Weirich and Pollock, while the precision objection is countered via a proposal by Kyburg and another by Gärdenfors and Sahlin. However, the Current Research section urges a different response to the precision objection by introducing a comparative version of decision theory. Drawing on Chu and Halpern’s notion of generalized expected utility, this version of decision theory permits many choices to be based on merely comparative plausibilities and utilities. Finally, the Further Research section undertakes an open-ended exploration of three of the assumptions upon which this form of decision theory (and many others) is based: transitivity, independence, and plausibilistic decision rules. (shrink)

The focus of this study is cognitive choice: the selection of one cognitive option (a hypothesis, a theory, or an axiom, for instance) rather than another. The study proposes that cognitive choice should be based on the plausibilities of states posited by rival cognitive options and the utilities of these options' information outcomes. The proposal introduces a form of decision theory that is novel because comparative; it permits many choices among cognitive options to be based on merely comparative plausibilities and (...) utilities. This form of decision theory intersects with recommendations by advocates of decision theory for cognitive choice, on the one hand, and defenders of comparative evaluation of scientific hypotheses and theories, on the other. But it differs from prior decision-theoretic proposals because it requires no more than minimal precision in specifying plausibilities and utilities. And it differs from comparative proposals because none has shown how comparative evaluations can be carried out within a decision-theoretic framework. (shrink)

Addressing such questions is a central challenge in explicating the cognitive role of indeterminacy. But there is little consensus in the literature about even such mundane questions as: what attitude to p is appropriate, when one knows that p is indeterminate'? This paper explores two answers, both built on a 'supervaluational' treatment of indeterminacy. The first is drawn out from David Lewis's discussion of Parfit on what matters in survival, and is a view where the indeterminacy of the identity relation (...) between Alpha and Omega scales the concern Alpha should feel. The second is developed on the model of imprecise credence treatments of indeterminacy, and generates some interesting and suprisingly successful predictions about the forced march sorites. (shrink)