Online research in philosophy

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.

This book is a unique introductory overview of decision theory. It is completely non-technical, without a single formula in the book. Written in a crisp and clear style it succinctly covers the full range of philosophical issues of rationality and decision theory, including game theory, social choice theory, prisoner's dilemma and much else. The book aims to expand the scope and enrich the foundations of decision theory. By addressing such issues as ambivalence, inner conflict, and the constraints imposed upon us by our attachments to others, Frederic Schick reveals that our thinking is often more subtle than standard theories of rationality allow. Only a theory that respects that subtlety can illumine what is otherwise puzzling. The book contains many examples drawn from history and literature dealing with subjects such as love, war, friendship, and crime.

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.

This essay answers the “Bayesian Challenge,” which is an argument offered by Bayesians that concludes that belief is not relevant to rational action. Patrick Maher and Mark Kaplan argued that this is so because there is no satisfactory way of making sense of how it would matter. The two ways considered so far, acting as if a belief is true and acting as if a belief has a probability over a threshold, do not work. Contrary to Maher and Kaplan, Keith Frankish argued that there is a way to make sense of how belief matters by introducing a dual process theory of mind in which decisions are made at the conscious level using premising policies . I argue that Bayesian decision theory alone shows that it is sometimes rational to base decisions on beliefs; we do not need a dual process theory of mind to solve the Bayesian Challenge. This point is made clearer when we consider decision levels : acting as if a belief is true is sometimes rational at higher decision levels.

Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation makes Bayesian decision theory a poor explication of the relevant concept of rational choice. A satisfactory conception of Bayesian decision theory is obtained by taking Bayesian probability to be an explicatum for inductive probability given the agent’s evidence.

Bayesian decision theory operates under the fiction that in any decision-making situation the agent is simply given the options from which he is to choose. It thereby sets aside some characteristics of the decision-making situation that are pre-analytically of vital concern to the verdict on the agent's eventual decision. In this paper it is shown that and how these characteristics can be accommodated within a still recognizably Bayesian account of rational agency.

The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and outcomes in decision situations, and so they are incomplete. Second, it will be argues that when we attempt to incorporate the knowledge of such causal connections into Bayesian decision theory, a substantial technical problem arises for which there is no currently available solution that does not suffer from some damning objection or other. From a broader perspective, this then throws into question the use of decision theory as a model of human or machine planning.

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.

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.

After a brief presentation of evidential decision theory, causal decision theory, and Newcomb type prima facie counterexamples to the evidential theory, three kinds of "metatickle" defenses of the evidential theory are discussed. Each has its weaknesses, but one of them seems stronger than the other two. The weaknesses of the best of the three, and the intricacy of metatickle analysis, does not constitute an advantage of causal decision theory over the evidential theory, however. It is argued, by way of an example, that causal decision theory also stands in need of a metatickle defense.