On the Foundations of Decision Making Under Partial Information

1992 ◽  
pp. 93-100
Author(s):  
David Rios Insua
Keyword(s):  
Decision Making ◽  
Partial Information

scholarly journals Interval-Valued Probabilistic Hesitant Fuzzy Set Based Muirhead Mean for Multi-Attribute Group Decision-Making

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 342 ◽  
Author(s):  
Krishankumar ◽  
Ravichandran ◽  
Ahmed ◽  
Kar ◽  
Peng
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Set ◽  
Group Decision ◽  
Partial Information ◽  
Programming Model ◽  
Hesitant Fuzzy Set ◽  
Occurrence Probability ◽  
Source Selection ◽  
Interval Valued

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.

Decision Making with Linear Partial Information (L.P.I.)

1984 ◽  
Vol 35 (12) ◽  
pp. 1079 ◽  
Author(s):  
E. Kofler ◽  
Z. W. Kmietowicz ◽  
A. D. Pearman
Keyword(s):  
Decision Making ◽  
Partial Information ◽  
Linear Partial Information

A Simulation-Based Approach to Decision Making with Partial Information

2012 ◽  
Vol 9 (4) ◽  
pp. 329-347 ◽  
Author(s):  
Luis V. Montiel ◽  
J. Eric Bickel
Keyword(s):  
Decision Making ◽  
Partial Information ◽  
Simulation Based

scholarly journals Decision Making in Complex Multiagent Contexts: A Tale of Two Frameworks

AI Magazine ◽  
2012 ◽  
Vol 33 (4) ◽  
pp. 82 ◽  
Author(s):  
Prashant J. Doshi
Keyword(s):  
Decision Making ◽  
Markov Decision Process ◽  
Decision Process ◽  
Partial Information ◽  
Autonomous Systems ◽  
Relevant Research ◽  
Physical Context ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable

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.

scholarly journals A Multicriteria Decision Model for Supplier Selection in a Food Industry Based on FITradeoff Method

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Eduarda Asfora Frej ◽  
Lucia Reis Peixoto Roselli ◽  
Jônatas Araújo de Almeida ◽  
Adiel Teixeira de Almeida
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Food Industry ◽  
Decision Model ◽  
Partial Information ◽  
Decision Making Process ◽  
Final Solution ◽  
Structured Decision Making ◽  
Supplier Selection Problem ◽  
Selection Of

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.

On the foundations of decision making under partial information

1992 ◽  
Vol 33 (1) ◽  
pp. 83-100 ◽  
Author(s):  
David Rios Insua
Keyword(s):  
Decision Making ◽  
Partial Information

A model for linguistic partial information in decision-making problems

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