scholarly journals Decision making with hybrid influence diagrams using mixtures of truncated exponentials

2008 ◽  
Vol 186 (1) ◽  
pp. 261-275 ◽  
Author(s):  
Barry R. Cobb ◽  
Prakash P. Shenoy
Keyword(s):  
Decision Making ◽  
Influence Diagrams ◽  
Mixtures Of Truncated Exponentials

scholarly journals Decision-making under uncertainty through extending influence diagrams with interval-valued parameters

2018 ◽  
Vol 35 (4) ◽  
pp. e12277 ◽  
Author(s):  
Lihua Zhou ◽  
Kevin Lü ◽  
Weiyi Liu ◽  
Changchun Ren
Keyword(s):  
Decision Making ◽  
Influence Diagrams ◽  
Decision Making Under Uncertainty ◽  
Interval Valued

Bayesian Belief Networks in IT Investment Decision Making

2017 ◽  
pp. 75-107 ◽  
Keyword(s):  
Decision Making ◽  
Investment Decision ◽  
Bayesian Belief Networks ◽  
Influence Diagrams ◽  
Belief Networks ◽  
It Investment ◽  
Investment Decision Making ◽  
Availability Modeling

This section aims at describing the concept of Bayesian Belief Networks (BBN), building principles and application of BBN and influence diagrams, as well as the reasons why BBN are considered an adequate tool for IS availability modeling.

A MODEL BASED ON INFLUENCE DIAGRAMS FOR MULTI-CRITERIA DECISION-MAKING

2012 ◽  
Vol 21 (04) ◽  
pp. 1250018 ◽  
Author(s):  
KARIMA SEDKI ◽  
VÉRONIQUE DELCROIX
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Decision Problem ◽  
Expert Knowledge ◽  
External Factors ◽  
Influence Diagrams ◽  
Multi Criteria Decision Making ◽  
Model Based ◽  
Decision Variables ◽  
Other Information

In this paper, we focus on multi-criteria decision-making problems. We propose a model based on influence diagrams; this model is able to handle uncertainty, represent interdependencies among the different decision variables and facilitate communication between the decision-maker and the analyst. The particular structure of the proposed model makes it possible to take into account the alternatives described by an attribute set, the decision-maker's characteristics and preferences, and other information (e.g., internal or external factors) that influence the decision. Modeling the decision problem in terms of influence diagrams requires a lot of work to gather expert knowledge. However, once the model is built, it can be easily and efficiently used for different instances of the decision problem. In fact, using our model simply requires entering some basic information, such as the values of internal or external factors and the decision-maker's characteristics. Our model also defines the importance of each criterion in terms of what is known about the decision maker, the quality index and the utility of each alternative.

scholarly journals An Algebraic Graphical Model for Decision with Uncertainties, Feasibilities, and Utilities

2007 ◽  
Vol 29 ◽  
pp. 421-489 ◽  
Author(s):  
C. Pralet ◽  
G. Verfaillie ◽  
T. Schiex
Keyword(s):  
Decision Making ◽  
Graphical Models ◽  
Graphical Model ◽  
Operational Semantics ◽  
Influence Diagrams ◽  
Sequential Decision ◽  
Quantified Boolean Formulas ◽  
Boolean Formulas ◽  
Express Information ◽  
Generic Algorithms

Numerous formalisms and dedicated algorithms have been designed in the last decades to model and solve decision making problems. Some formalisms, such as constraint networks, can express "simple" decision problems, while others are designed to take into account uncertainties, unfeasible decisions, and utilities. Even in a single formalism, several variants are often proposed to model different types of uncertainty (probability, possibility...) or utility (additive or not). In this article, we introduce an algebraic graphical model that encompasses a large number of such formalisms: (1) we first adapt previous structures from Friedman, Chu and Halpern for representing uncertainty, utility, and expected utility in order to deal with generic forms of sequential decision making; (2) on these structures, we then introduce composite graphical models that express information via variables linked by "local" functions, thanks to conditional independence; (3) on these graphical models, we finally define a simple class of queries which can represent various scenarios in terms of observabilities and controllabilities. A natural decision-tree semantics for such queries is completed by an equivalent operational semantics, which induces generic algorithms. The proposed framework, called the Plausibility-Feasibility-Utility (PFU) framework, not only provides a better understanding of the links between existing formalisms, but it also covers yet unpublished frameworks (such as possibilistic influence diagrams) and unifies formalisms such as quantified boolean formulas and influence diagrams. Our backtrack and variable elimination generic algorithms are a first step towards unified algorithms.

USING INFLUENCE DIAGRAMS IN MULTIPLE CRITERIA DECISION MAKING TASKS

1999 ◽  
Vol 1 ◽  
pp. 176
Author(s):  
Clara Savchenko
Keyword(s):  
Decision Making ◽  
Formal Model ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Influence Diagrams ◽  
Decision Making Under Risk

The paper presents investigation into the solution of multiple criteria decision making problems. Influence diagrams can be used as a formal model of decision making under risk.

Structural, elicitation and computational issues faced when solving complex decision making problems with influence diagrams

2000 ◽  
Vol 27 (7-8) ◽  
pp. 725-740 ◽  
Author(s):  
C. Bielza ◽  
M. Gómez ◽  
S. Rı́os-Insua ◽  
J.A.Fernández del Pozo
Keyword(s):  
Decision Making ◽  
Influence Diagrams ◽  
Complex Decision Making ◽  
Complex Decision

Research of Decision-Making in the Multi-Agent System Based on Interactive Influence Diagrams

2011 ◽  
Vol 467-469 ◽  
pp. 1947-1952
Author(s):  
Bo Li ◽  
Jian Luo ◽  
Jin Fa Zhuang
Keyword(s):  
Decision Making ◽  
Mental Models ◽  
Mental Model ◽  
High Accuracy ◽  
Influence Diagrams ◽  
Multi Agent System ◽  
Empirical Results ◽  
Model Spaces ◽  
Multi Agent ◽  
Sufficient Space

Interactive influence diagrams(I-IDs) offer a transparent and representation for the decision-making in multiagent settings. In I-IDs, for the sake of predicting the behavior of other agent accurately, the modeling agent starts from an initial set of possible models for another agent and then maintains belief about which of those models applies. This initial set of models in the model node is typically a fully specification of possible agent types. Although such a rich space gives the modeling agent high accuracy in its beliefs, it will also incur high cost in maintaining those beliefs. In this paper, we demonstrate that we can choose a minimal, but sufficient, space of mental models by combining models that action or utility equivalence. We illustrate our framework using the two-tiger game and provide empirical results by showing the minimal mental model spaces that it generates.

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