Information Structures in Stochastic Programming Problems

Koichi Miyasawa
Additional contact information
Koichi Miyasawa: University of Tokyo

Management Science, 1968, vol. 14, issue 5, 275-291

Abstract: Problems of multi-stage decision under uncertainty are usually classified into "stochastic" and "adaptive" ones, depending on whether the decision maker does or does not know the relevant probability distribution. If the Bayesian approach is taken, then, in the adaptive case, the decision maker is assumed to know the prior distribution of certain parameters. It is shown in the paper that the adaptive case is then reducible to the stochastic one. The problems can also be classified according to the kind of information (memory) available to the decision maker. In the paper, optimal policies and the expected gains they yield, are determined for some important classes of "information structures." In cases in which added information does not increase the expected gain, a sufficient information structure is specified.

Date: 1968
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.14.5.275 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:14:y:1968:i:5:p:275-291

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().