 Decision Analysis—Expected Value MaximizationJeffrey M. Keisler
Chapter 7 in Prescriptive Analytics, 2024, pp 135-157 from Springer Abstract: Abstract Decision analysis provides a systematic approach to rational action under uncertainty. It starts with specifying which variables represent decisions and which are to be treated as uncertain. A decision table contains the calculated payoffs for each potential combination of input values. Probabilities are assigned to the possible states of uncertain variables. From this, we calculate the expected value (EV), the probability-weighted average of the payoffs for each alternative, and we identify which alternative maximizes the EV. This chapter builds up to multi-stage decisions that unfold over time. This can involve assigning probabilities over values of later uncertain variables conditionally on the values of the earlier variables. Then various expected values and maximum values are aggregated from this data. Timing of decisions and resolution of uncertainties is then incorporated into the analysis in terms of which data are aggregated in which order with which rule. This takes us from identifying the EV maximizing alternative to identifying the EV maximizing course of action. Date: 2024 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-031-59353-6_7 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-59353-6_7 Access Statistics for this chapter More chapters in Springer Texts in Business and Economics from Springer |
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