On the Foundations of Decision Making Under Partial Information

Author(s):  
David Rios Insua

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 342 ◽  
Author(s):  
Krishankumar ◽  
Ravichandran ◽  
Ahmed ◽  
Kar ◽  
Peng

As a powerful generalization to fuzzy set, hesitant fuzzy set (HFS) was introduced, which provided multiple possible membership values to be associated with a specific instance. But HFS did not consider occurrence probability values, and to circumvent the issue, probabilistic HFS (PHFS) was introduced, which associates an occurrence probability value with each hesitant fuzzy element (HFE). Providing such a precise probability value is an open challenge and as a generalization to PHFS, interval-valued PHFS (IVPHFS) was proposed. IVPHFS provided flexibility to decision makers (DMs) by associating a range of values as an occurrence probability for each HFE. To enrich the usefulness of IVPHFS in multi-attribute group decision-making (MAGDM), in this paper, we extend the Muirhead mean (MM) operator to IVPHFS for aggregating preferences. The MM operator is a generalized operator that can effectively capture the interrelationship between multiple attributes. Some properties of the proposed operator are also discussed. Then, a new programming model is proposed for calculating the weights of attributes using DMs’ partial information. Later, a systematic procedure is presented for MAGDM with the proposed operator and the practical use of the operator is demonstrated by using a renewable energy source selection problem. Finally, the strengths and weaknesses of the proposal are discussed in comparison with other methods.



1984 ◽  
Vol 35 (12) ◽  
pp. 1079 ◽  
Author(s):  
E. Kofler ◽  
Z. W. Kmietowicz ◽  
A. D. Pearman


2012 ◽  
Vol 9 (4) ◽  
pp. 329-347 ◽  
Author(s):  
Luis V. Montiel ◽  
J. Eric Bickel


AI Magazine ◽  
2012 ◽  
Vol 33 (4) ◽  
pp. 82 ◽  
Author(s):  
Prashant J. Doshi

Decision making is a key feature of autonomous systems. It involves choosing optimally between different lines of action in various information contexts that range from perfectly knowing all aspects of the decision problem to having just partial knowledge about it. The physical context often includes other interacting autonomous systems, typically called agents. In this article, I focus on decision making in a multiagent context with partial information about the problem. Relevant research in this complex but realistic setting has converged around two complementary, general frameworks and also introduced myriad specializations on its way. I put the two frameworks, decentralized partially observable Markov decision process (Dec-POMDP) and the interactive partially observable Markov decision process (I-POMDP), in context and review the foundational algorithms for these frameworks, while briefly discussing the advances in their specializations. I conclude by examining the avenues that research pertaining to these frameworks is pursuing.



2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Eduarda Asfora Frej ◽  
Lucia Reis Peixoto Roselli ◽  
Jônatas Araújo de Almeida ◽  
Adiel Teixeira de Almeida

This article puts forward a decision model for solving a supplier selection problem in a food industry by considering multiple objectives that influence the decision-making process. In times of increasing competitiveness, companies strive hard to improve their profitability, and selection of supply sources may help if an appropriate decision is made through a well-structured decision-making process. Preference modeling is conducted in a flexible and interactive elicitation manner with the decision-maker (DM), aided by FITradeoff method. Partial information is gathered about the DM’s preferences in such a way that less effort is spent on finding a final solution for the problem.



1992 ◽  
Vol 33 (1) ◽  
pp. 83-100 ◽  
Author(s):  
David Rios Insua


1994 ◽  
Vol 9 (4) ◽  
pp. 365-378 ◽  
Author(s):  
M. Delgado ◽  
J. L. Verdegay ◽  
M. A. Vila


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