A conversion between utility and information

DOI

10.2991/agi.2010.10How to use a DOI?

Keywords

Behavior, Utility, Entropy, Information

Abstract

Rewards typically express desirabilities or preferences over a set of alternatives. Here we propose that rewards can be defined for any probability distribution based on three desiderata, namely that rewards should be real-valued, additive and order-preserving, where the latter implies that more probable events should also be more desirable. Our main result states that rewards are then uniquely determined by the negative infor- mation content. To analyze stochastic processes, we define the utility of a realization as its reward rate. Under this interpretation, we show that the expected utility of a stochastic process is its negative entropy rate. Furthermore, we apply our results to analyze agent-environment interactions. We show that the expected utility that will actually be achieved by the agent is given by the negative cross-entropy from the input-output (I/O) distribution of the coupled interaction system and the agent's I/O distribution. Thus, our results allow for an information-theoretic interpretation of the notion of utility and the characterization of agent-environment interactions in terms of entropy dynamics.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Pedro A. Ortega
AU  - Daniel A. Braun
PY  - 2010/06
DA  - 2010/06
TI  - A conversion between utility and information
BT  - Proceedings of the 3d Conference on Artificial General Intelligence (2010)
PB  - Atlantis Press
SP  - 47
EP  - 52
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2010.10
DO  - 10.2991/agi.2010.10
ID  - Ortega2010/06
ER  -