Influence diagram in decision analysis
Influence Diagrams Belief Nets Decisio by Clare OliverBased on the proceedings of a conference on Influence Diagrams for Decision Analysis, Inference and Prediction held at the University of California at Berkeley in May of 1988, this is the first book devoted to the subject. The editors have brought together recent results from researchers actively investigating influence diagrams and also from practitioners who have used influence diagrams in developing models for problem-solving in a wide range of fields.
Use of influence diagrams to structure medical decisions.
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research , specifically in decision analysis , to help identify a strategy most likely to reach a goal , but are also a popular tool in machine learning. A decision tree is a flowchart -like structure in which each internal node represents a "test" on an attribute e. The paths from root to leaf represent classification rules. In decision analysis , a decision tree and the closely related influence diagram are used as a visual and analytical decision support tool, where the expected values or expected utility of competing alternatives are calculated. A decision tree consists of three types of nodes: .
An influence diagram ID also called a relevance diagram , decision diagram or a decision network is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesian network , in which not only probabilistic inference problems but also decision making problems following the maximum expected utility criterion can be modeled and solved. ID was first developed in the mids by decision analysts with an intuitive semantic that is easy to understand. It is now adopted widely and becoming an alternative to the decision tree which typically suffers from exponential growth in number of branches with each variable modeled. ID is directly applicable in team decision analysis , since it allows incomplete sharing of information among team members to be modeled and solved explicitly.
In , Howard and Matheson introduced the idea of representing a Bayesian decision problem in terms of a graph called an influence diagram. These graphs have various advantages over decision trees, especially when a decision problem exhibits many symmetries. In particular, no early commitment from the client to the Bayesian paradigm is required to construct an influence diagram. Despite being very generally applicable, it is often possible to make strong insightful deductions from an initial set of irrelevance statements which can then be fed back to the client for possible adjustment. They can therefore provide a framework through which the client can begin to learn about her belief structures and develop them. Only once such a structure is agreed will more quantitative decision analysis take place.
trees and influence diagrams. Bibiography: P. Goodwin & G. Wright () Decision Analysis for Management. Judgement, John Wiley and Sons (chapter 6) .
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Influence diagrams are closely related to decision trees and often used in conjunction with them. An influence diagram displays a summary of the information contained in a decision tree. It involves four variable types for notation: a decision a rectangle , chance an oval , objective a hexagon , and function a rounded rectangle. Influence diagrams also use solid lines to denote influence. Their appearance is very similar to a flowchart. Influence diagrams show the dependencies among variables.
Every DPL model is a combination of an Influence Diagram and a Decision Tree but I admit that we tend to play up the decision tree side of our software, as it is the analytic backbone of the DPL software. Furthermore, as a marketer, I'm also vested in search term prominence. See how the two terms compare in Google Trends above. Are Influence Diagrams less important or less hip than their tree counterpart? Should they be deemed unnecessary and thrown to the wayside?