I Don't Want to Think About it Now: Decision Theory with Costly Computation

By Joseph Y. Halpern. 2010

24 citations recorded by the source corpus.

Computation plays a major role in decision making. Even if an agent is willing to ascribe a probability to all states and a utility to all outcomes, and maximize expected utility, doing so might present serious computational problems. Moreover, computing the outcome of a given act might be difficult. In a companion paper we develop a framework for game theory with costly computation, where the objects of choice are Turing machines. Here we apply that framework to decision theory. We show how well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on), belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions), and the status quo bias (people are much more likely to stick with what they already have) can be easily captured in that framework. Finally, we use the framework to define some new notions: value of computational information (a computational variant of value of information) and computational value of conversation.

Subfields: Artificial Intelligence, General Decision Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In General Decision Sciences

Citations in the corpus

Research Priorities for Robust and Beneficial Artificial Intelligence (2015, background): “These topics may benefit from being considered together, since they appear deeply linked (Halpern and Pass 2011; Halpern, Pass, and Seeman 2014).”
Decision Theory with Resource-Bounded Agents (2013, methodology): “In this section we review and discuss the approach that we used in [7] to take cost of computation into account in decision theory.”