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Chapter 6 - Active Inference, Bayesian Optimal Design, and Expected Utility

from Part II - How Do Humans Search for Information?

Published online by Cambridge University Press:  19 May 2022

By
Noor Sajid ,
Lancelot Da Costa ,
Thomas Parr  and
Karl Friston
Edited by
Irene Cogliati Dezza ,
Eric Schulz  and
Charley M. Wu
Irene Cogliati Dezza
Affiliation:
University College London
Eric Schulz
Affiliation:
Max-Planck-Institut für biologische Kybernetik, Tübingen
Charley M. Wu
Affiliation:
Eberhard-Karls-Universität Tübingen, Germany
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Summary

Active inference, a corollary of the free energy principle, is a formal way of describing the behavior of certain kinds of random dynamical systems that have the appearance of sentience. In this chapter, we describe how active inference combines Bayesian decision theory and optimal Bayesian design principles under a single imperative to minimize expected free energy. It is this aspect of active inference that allows for the natural emergence of information-seeking behavior. When removing prior outcomes preferences from expected free energy, active inference reduces to optimal Bayesian design (i.e., information gain maximization). Conversely, active inference reduces to Bayesian decision theory in the absence of ambiguity and relative risk (i.e., expected utility maximization). Using these limiting cases, we illustrate how behaviors differ when agents select actions that optimize expected utility, expected information gain, and expected free energy. Our T-maze simulations show optimizing expected free energy produces goal-directed information-seeking behavior while optimizing expected utility induces purely exploitive behavior, and maximizing information gain engenders intrinsically motivated behavior.

Keywords

Active inferenceBayesian decision theoryoptimal Bayesian designfree energyinformation gain
Type
Chapter
Information
The Drive for Knowledge
The Science of Human Information Seeking
 , pp. 124 - 146
Publisher: Cambridge University Press
Print publication year: 2022

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