Probabilistic decision graphs for optimization under uncertainty

Finn Jensen () and Thomas Nielsen ()

Annals of Operations Research, 2013, vol. 204, issue 1, 223-248

Abstract: This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, we present a couple of approaches for calculating approximate solutions. The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software systems that implement the frameworks described in the paper. Copyright Springer Science+Business Media New York 2013

Keywords: Survey; Probabilistic decision graphs; Influence diagrams (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-012-1263-6

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