Causal Decision Theory and Decision Instability

The problem of the man who met death in Damascus appeared in the infancy of the theory of rational choice known as causal decision theory. A straightforward, unadorned version of causal decision theory is presented here and applied, along with Brian Skyrms’ deliberation dynamics, to Death in Damascus and similar problems. Decision instability is a fascinating topic, but not a source of difficulty for causal decision theory. Andy Egan’s purported counterexample to causal decision theory, Murder Lesion, is considered; a simple response shows how Murder Lesion and similar examples fail to be counterexamples, and clarifies the use of the unadorned theory in problems of decision instability. I compare unadorned causal decision theory to previous treatments by Frank Arntzenius and by Jim Joyce, and recommend a well-founded heuristic that all three accounts can endorse. Whatever course deliberation takes, causal decision theory is consistently a good guide to rational action

Deliberation and pragmatic belief

To what extent do our beliefs, and how strongly we hold them, depend upon how they matter to us, on what we take to be at stake on them? The idea that beliefs are sometimes stake-sensitive (Armendt 2008, 2013) is further explored here, with a focus on whether beliefs may be stake-sensitive and rational. In contexts of extended deliberation about what to do, beliefs and assessments of options interact. In some deliberations, a belief about what you will do may rationally influence your estimate of the value of doing it; deliberation dynamics provides a framework for modeling such interactions. A distinction is drawn between sensitivity to the magnitude of the stakes, and sensitivity to the shape of the stakes. Contexts of extended deliberation are settings in which some beliefs that p rationally depend on the shape of the stakes on p. The dependence is either rational stake-sensitivity or an outcome of rational learning; empirical evidence concerning contexts of deliberation may lead us to model rational beliefs in one way or the other

Frank Plumpton Ramsey

On the work of Frank Ramsey, emphasizing topics most relevant to philosophy of science

On Risk and Rationality

It is widely held that the influence of risk on rational decisions is not entirely explained by the shape of an agent’s utility curve. Buchak (Erkenntnis 2013; Risk and rationality, Oxford University Press, Oxford, in press) presents an axiomatic decision theory, risk-weighted expected utility theory (REU), in which decision weights are the agent’s subjective probabilities modified by his risk-function r. REU is briefly described, and the global applicability of r is discussed. Rabin’s (2000) calibration theorem strongly suggests that plausible levels of risk aversion cannot be fully explained by concave utility functions; this provides motivation for REU and other theories. But applied to the synchronic preferences of an individual agent, Rabin’s result is not as problematic as it may first appear. Theories that treat outcomes as gains and losses (e.g. prospect theory and cumulative prospect theory) account for risk sensitivity in a way not available to REU. Reference points that mark the difference between gains and losses are subject to framing, many instances of which cannot be regarded as rational. Yet rational decision theory may recognize the difference between gains and losses, without endorsing all ways of fixing the point of reference. In any event, REU is a very interesting theory

The Bayesian boom: good thing or bad?

A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from these critiques and evaluates them in light of specific models. Closer consideration of actual examples of Bayesian treatments of different cognitive phenomena allows one to defuse these critiques showing that they cannot be sustained across the diversity of applications of the Bayesian framework for cognitive modeling. More generally, there is nothing in the Bayesian framework that would inherently give rise to the deficits that these critiques perceive, suggesting they have been framed at the wrong level of generality. At the same time, the examples are used to demonstrate the different ways in which consideration of rationality uniquely benefits both theory and practice in the study of cognition