Decision Influence Diagrams and Their Uses

1994 ◽  
pp. 33-51 ◽  
Author(s):  
Jim Q. Smith
Keyword(s):  
Influence Diagrams

Elicitation of expert knowledge to develop decision support tools for use in water demand management planning

2007 ◽  
Vol 7 (5-6) ◽  
pp. 53-60
Author(s):  
D. Inman ◽  
D. Simidchiev ◽  
P. Jeffrey
Keyword(s):  
Decision Support ◽  
Water Demand ◽  
Expert Knowledge ◽  
Demand Management ◽  
Decision Processes ◽  
Conservation Strategy ◽  
Influence Diagrams ◽  
Decision Support Tools ◽  
Water Demand Management ◽  
Support Tools

This paper examines the use of influence diagrams (IDs) in water demand management (WDM) strategy planning with the specific objective of exploring how IDs can be used in developing computer-based decision support tools (DSTs) to complement and support existing WDM decision processes. We report the results of an expert consultation carried out in collaboration with water industry specialists in Sofia, Bulgaria. The elicited information is presented as influence diagrams and the discussion looks at their usefulness in WDM strategy design and the specification of suitable modelling techniques. The paper concludes that IDs themselves are useful in developing model structures for use in evidence-based reasoning models such as Bayesian Networks, and this is in keeping with the objectives set out in the introduction of integrating DSTs into existing decision processes. The paper will be of interest to modellers, decision-makers and scientists involved in designing tools to support resource conservation strategy implementation.

scholarly journals Risk Assessment of Circuit Breakers Using Influence Diagrams with Interval Probabilities

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 737
Author(s):  
Jelena D. Velimirovic ◽  
Aleksandar Janjic
Keyword(s):  
Power System ◽  
Predictive Modelling ◽  
Optimal Time ◽  
Influence Diagrams ◽  
Circuit Breakers ◽  
Interval Probability ◽  
Complex Power ◽  
Interval Width ◽  
Credal Sets ◽  
The One

This paper deals with uncertainty, asymmetric information, and risk modelling in a complex power system. The uncertainty is managed by using probability and decision theory methods. More specifically, influence diagrams—as extended Bayesian network functions with interval probabilities represented through credal sets—were chosen for the predictive modelling scenario of replacing the most critical circuit breakers in optimal time. Namely, based on the available data on circuit breakers and other variables that affect the considered model of a complex power system, a group of experts was able to assess the situation using interval probabilities instead of crisp probabilities. Furthermore, the paper examines how the confidence interval width affects decision-making in this context and eliminates the information asymmetry of different experts. Based on the obtained results for each considered interval width separately on the action to be taken over the considered model in order to minimize the risk of the power system failure, it can be concluded that the proposed approach clearly indicates the advantages of using interval probability when making decisions in systems such as the one considered in this paper.

scholarly journals A Note on Equivalence Classes of Directed Acyclic Independence Graphs

1993 ◽  
Vol 7 (3) ◽  
pp. 409-412 ◽  
Author(s):  
David Madigan
Keyword(s):  
Model Selection ◽  
Expert Systems ◽  
Equivalence Classes ◽  
Influence Diagrams ◽  
Single Class ◽  
Selection Procedures ◽  
Probabilistic Expert Systems ◽  
Recent Developments ◽  
Independence Graphs ◽  
Markov Properties

Directed acyclic independence graphs (DAIGs) play an important role in recent developments in probabilistic expert systems and influence diagrams (Chyu [1]). The purpose of this note is to show that DAIGs can usefully be grouped into equivalence classes where the members of a single class share identical Markov properties. These equivalence classes can be identified via a simple graphical criterion. This result is particularly relevant to model selection procedures for DAIGs (see, e.g., Cooper and Herskovits [2] and Madigan and Raftery [4]) because it reduces the problem of searching among possible orientations of a given graph to that of searching among the equivalence classes.

scholarly journals Variable elimination for influence diagrams with super value nodes

2010 ◽  
Vol 51 (6) ◽  
pp. 615-631 ◽  
Author(s):  
Manuel Luque ◽  
Francisco Javier Díez
Keyword(s):  
Influence Diagrams ◽  
Variable Elimination

Prediction Under the Influence: Bayes Linear Influence Diagrams for Prediction in a Large Brewery

1993 ◽  
Vol 42 (4) ◽  
pp. 445 ◽  
Author(s):  
Michael Goldstein ◽  
Malcolm Farrow ◽  
Takis Spiropoulos
Keyword(s):  
Influence Diagrams
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