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Distribution of expected utility in second-order decision analysis
KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
2007 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [la]

In explicatione consiliorum, maxima facere communis utilitas saepe trita ratio deligendi meliorem optionem est. Verum si probabilitates et utilitates incertae vel dubiae sint, communis utilitas perturbationes affert. Studium secundi ordinis effectuum in explicatione consiliorum explanat momentum structurae quaestionium consilii, insidias aliquas ad consilium capiendum indicat et facilem ad efficiendum et intellegendum rationem comparandi varia consilia suadet. Haec thesis tractat de secundi ordinis effectibus explicationis consilii, praesertim de commune utilitate et de probabilitatibus coniunctis intervallo. Voces apertae distributionum ordinis secundi in probabilitatibus intervallo conjunctis insitarum omnino et item distributionum utilitatis expectatae in parvis quaestionibus consiliorum eduntur. His distributionibus cognitis studetur res inflexionis, aliter dictu intentio fidei.

Abstract [en]

In decision analysis maximising the expected utility is an often used approach in choosing the optimal alternative. But when probabilities and utilities are vague or imprecise expected utility is fraught with complications. Studying second-order effects on decision analysis casts light on the importance of the structure of decision problems, pointing out some pitfalls in decision making and suggesting an easy to implement and easy to understand method of comparing decision alternatives. The topic of this thesis is such second-order effects of decision analysis, particularly with regards to expected utility and interval-bound probabilities. Explicit expressions for the second-order distributions inherent in interval-bound probabilities in general and likewise for distributions of expected utility for small decision problems are produced. By investigating these distributions the phenomenon of warping, that is concentration of belief, is studied.

Place, publisher, year, edition, pages
Stockholm: KTH , 2007. , vi, 20 p.
Series
Report series / DSV, ISSN 1101-8526 ; 07-006
National Category
Information Science
Identifiers
URN: urn:nbn:se:kth:diva-4442OAI: oai:DiVA.org:kth-4442DiVA: diva2:12327
Presentation
2007-06-05, Sal C, KTH-Forum, entréplanet, trapphus C, Isafjordsgatan 39, Kista, 13:00
Opponent
Supervisors
Note
QC 20101118Available from: 2007-06-18 Created: 2007-06-18 Last updated: 2010-11-18Bibliographically approved
List of papers
1. Cross-disciplinary research in analytic decision support systems
Open this publication in new window or tab >>Cross-disciplinary research in analytic decision support systems
Show others...
2006 (English)In: ITI 2006: Proceedings of the 28th International Conference on Information Technology Interfaces / [ed] LuzarStiffler V, Dobric VH, New York: IEEE , 2006, 123-128 p.Conference paper, Published paper (Refereed)
Abstract [en]

A main problem in decision support contexts is that unguided decision making is difficult and can lead to inefficient decision processes and undesired consequences. Therefore, decision support systems (DSSs) are of prime concern to any organization and there have been numerous approaches to delivering decision support from, e.g., computational, mathematical, financial, philosophical, psychological, and sociological angles. A key observation, however, is that effective and efficient decision making is not easily achieved by using methods from one discipline only. This paper describes some efforts made by the DECIDE Research Group to approach DSS development and decision making tools in a cross-disciplinary way.

Place, publisher, year, edition, pages
New York: IEEE, 2006
Keyword
decision support systems, decision analysis, uncertain reasoning
National Category
Information Science
Identifiers
urn:nbn:se:kth:diva-7338 (URN)000239576000023 ()953-7138-05-4 (ISBN)
Conference
28th International Conference on Information Technology Interfaces Cavtat, CROATIA, JUN 19-22, 2006
Note
QC 20101117Available from: 2007-06-18 Created: 2007-06-18 Last updated: 2010-11-17Bibliographically approved
2. Structure information in decision trees and similar formalisms
Open this publication in new window or tab >>Structure information in decision trees and similar formalisms
2007 (English)In: Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007, 2007, 62-67 p.Conference paper, Published paper (Refereed)
Abstract [en]

In attempting to address real-life decision problems, where uncertainty about input data prevails, some kind of representation of imprecise information is important and several have been proposed over the years. In particular, first-order representations of imprecision, such as sets of probability measures, upper and lower probabilities, and interval probabilities and utilities of various kinds, have been suggested for enabling a better representation of the input sentences. A common problem is, however, that pure interval analyses in many cases cannot discriminate sufficiently between the various strategies under consideration, which, needless to say, is a substantial problem in real-life decision making in agents as well as decision support tools. This is one reason prohibiting a more wide-spread use. In this article we demonstrate that in many situations, the discrimination can be made much clearer by using information inherent in the decision structure. It is discussed using second-order probabilities which, even when they are implicit, add information when handling aggregations of imprecise representations, as is the case in decision trees and probabilistic networks. The important conclusion is that since structure carries information, the structure of the decision problem influences evaluations of all interval representations and is quantifiable.

Keyword
Formal logic, Information analysis, Knowledge representation, Probability, Problem solving
National Category
Information Science
Identifiers
urn:nbn:se:kth:diva-7339 (URN)978-157735319-5 (ISBN)
Conference
20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007; Key West, FL; 7 May 2007 through 9 May 2007
Note
QC 20101117Available from: 2007-06-18 Created: 2007-06-18 Last updated: 2010-11-17Bibliographically approved
3. Some second order effects on interval based probabilities
Open this publication in new window or tab >>Some second order effects on interval based probabilities
2006 (English)In: FLAIRS 2006 - Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, 2006, 848-853 p.Conference paper, Published paper (Refereed)
Abstract [en]

In real-life decision analysis, the probabilities and values of consequences are in general vague and imprecise. One way to model imprecise probabilities is to represent a probability with the interval between the lowest possible and the highest possible probability, respectively. However, there are disadvantages with this approach, one being that when an event has several possible out-comes, the distributions of belief in the different probabilities are heavily concentrated to their centers of mass, meaning that much of the information of the original intervals are lost. Representing an imprecise probability with the distribution's center of mass therefore in practice gives much the same result as using an interval, but a single number instead of an interval is computationally easier and avoids problems such as overlapping intervals. Using this, we demonstrate why second-order calculations can add information when handling imprecise representations, as is the case of decision trees or probabilistic networks. We suggest a measure of belief density for such intervals. We also demonstrate important properties when operating on general distributions. The results herein apply also to approaches which do not explicitly deal with second-order distributions, instead using only first-order concepts such as upper and lower bounds

Keyword
Computational methods; Distributed parameter control systems; Information analysis; Probability; Problem solving; Real time systems
National Category
Information Science
Identifiers
urn:nbn:se:kth:diva-7340 (URN)2-s2.0-33746052058 (Scopus ID)
Conference
FLAIRS 2006 - 19th International Florida Artificial Intelligence Research Society Conference; Melbourne Beach, FL; 11 May 2006 through 13 May 2006
Note
QC 20101117Available from: 2007-06-18 Created: 2007-06-18 Last updated: 2010-11-17Bibliographically approved
4. Some properties of aggregated distributions over expected values
Open this publication in new window or tab >>Some properties of aggregated distributions over expected values
2008 (English)In: / [ed] Gelbukh A., Morales E.F., Berlin: Springer Verlag , 2008, 699-709 p.Conference paper, Published paper (Refereed)
Abstract [en]

Software agents and humans alike face severe difficulties in making decisions in uncertain contexts. One approach is to formalise the decision situation by means of decision theory, i.e. probabilities and utilities leading to the principle of maximising the expected utility. Expected utility is here considered as a stochastic variable; under the assumption that all utility values are equally likely; and that each vector of probability values is equally likely, the probability distribution of expected utility is calculated for two, three, and four possible outcomes. The effect of these probability distributions concentrating around the middle value is explored and its significance for making decisions.

Place, publisher, year, edition, pages
Berlin: Springer Verlag, 2008
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 5317
National Category
Information Science
Identifiers
urn:nbn:se:kth:diva-7341 (URN)10.1007/978-3-540-88636-5_66 (DOI)000261873400066 ()2-s2.0-57049112574 (Scopus ID)978-3-540-88635-8 (ISBN)
Conference
7th Mexican International Conference on Artificial Intelligence (MICAI 2008) Atizapan de Zaragoza, MEXICO, OCT 27-31, 2008
Note

QC 20101117

Available from: 2007-06-18 Created: 2007-06-18 Last updated: 2014-10-28Bibliographically approved

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