Decision Trees in Decision Analysis

About

Definition

A decision making tree is essentially a diagram that represents, in a specially organized way, the decisions, the main external or other events that introduce uncertainty, as well as possible outcomes of all those decisions and events.

Components of a decision tree

Decision node: a square node; represents decisions you can make

Chance (uncertainty) node: a circle node; shows the occurrence of events over which the decision maker has no direct control

Consequences (outcomes: utilities or costs

Process of building a decision tree

Interview decision makers and construct a preliminary tree

Present the tree and show how various concerns are caputred

Solicit a list of new concerns

Revise the tree

Advantages

Some of the advantages of using decision trees for decision making and planning are the following:

Clear lay out of the problem so that all options can be challenged

Full analysis of the possible consequences of a decision

Provide a framework to quantify the values of outcomes and the probabilities of achieving them; white box model - If a given result is provided by a model, the explanation for the result is easily replicated by simple math

Help us to make the best decisions on the basis of existing information and best guesses

Disadvantages

Some of the disadvantaged of using decision tress for decision making and planning are the following:

Even a small change in input data can at times, cause large changes in the tree

Decisions contained in the decision tree are based on expectations, and irrational expectations can lead to flaws and errors in the decision tree

Large decision trees can be unwieldy and complex to us

Tools

Decision trees which purpose is to facilitate decision making could easily be drawn manually on a piece of paper. However, it could be more convenient for a user to use computers because it is easy to make changes or to make some computations. Simple text editors and table sheets could serve for that purpose, i.e. MS Word and MS Excel. Additionally, other specialized software was developed to support decision making. Once a user learns how to use the software decision trees are quick and efficient. In fact,visual communication can be six times more effective than communication with words only. Nevertheless, we should always keep in mind that the software is only as good as the user. It is not the program that makes decisions because it is simply following user's instructions, thus a user should learn how to give good instructions.

Here is the list of some specialized decision tress tools:

1. Insight Tree

easy to use if you are familiar with decision trees; it allows you to quickly add branches, optimize sub nodes, use different style sheets; free for personal use

http://www.visionarytools.com/index.htm

2. Lumenaut

add-in to MS Excel

provides a range of tools (Monte Carlo simulation software and Decision Tree analysis software) allowing you to build decision tree models from within Excel

allows for different types of sensitivity and statistical analysis

http://www.lumenaut.com/

3. Vanguard Studio

a standalone program and is described as combining features of artificial intelligence, math applications and spreadsheets

the decision tree software aspect has a nice wizard which takes you step-by-step through creating the whole decision tree

it also offers Monte Carlo simulation, another wizard for forecasting, statistical decision tree analysis and other methods

http://www.vanguardsw.com/products/vanguard-system/components/vanguard-studio/

4. SmartDraw

popular for business graphics and allows you to quickly and easily draw various diagrams

http://www.smartdraw.com/

Bibliography

Read more