Measure Theoretic Structures for Sequential Decision Models.
Purchase
Purchase Print Copy
| Format | List Price | Price | |
|---|---|---|---|
| Add to Cart | Paperback21 pages | $20.00 | $16.00 20% Web Discount |
An expository paper dealing with the elements which must be represented in probabilistic models of general state space sequential decision problems. These elements are described, and the ways they are represented in two such models, a dynamic programming model due to Blackwell and a gambling model due to Dubins and Savage, are examined. If the set of possible positions in which the decisionmaker might find himself is uncountable, then a measure theoretic structure connecting the basic elements of the problem is required in order to ensure adequate definitions of the overall dynamics of the problem and the value to the decisionmaker of his overall behavior. The dynamic programming model uses a countably additive measure structure based on metric considerations, while the gambling model uses finitely additive measures defined on all subsets of the basic space. 21 pp. Ref.
This report is part of the RAND Corporation Paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.
This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.
The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.