We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Department of Mathematical Sciences

Seminar Archives

On this page you can find information about seminars in this and previous academic years, where available on the database.

Stats4Grads: Sequential decision analysis with general choice functions

Presented by Nathan Huntley, Durham University

22 October 2008 14:15 in CM221

Decision trees are useful graphical representations of sequential decision problems. Typically, one of two solution types are used to analyse decision trees: the normal form solution and the extensive form solution. However, common definitions of these solution types are not sufficient to describe all possible approaches to solving decision trees. I shall outline more general definitions for these solution types, which can then describe a much wider variety of solutions, and show that the two forms are fundamentally different.

I shall then consider normal form solutions in more detail. These solutions, in which one initially specifies one's policy for all eventualities, are in theory easy to deal with: one simply needs to compare all available policies and decide which ones are optimal. In practice, there are often too many policies to reasonable compare. This issue can be addressed by extending the usual method of backward induction, which maximizes expected utility, to arbitrary choice functions. We find necessary and sufficient conditions on a choice function under which this method yields the optimal normal form solution.

Contact or for more information

See the Stats4Grads page for more details about this series.