#DECISION TREE SOFTWARE ENGINEERING PPT TRIAL#

In this case, we call $6 the certainty equivalent of the chance. That is, they would be just as happy to have $6 as they would to have the opportunity to flip a fair coin and get paid $40 if heads comes up and lose $10 if tails comes up.

#DECISION TREE SOFTWARE ENGINEERING PPT TRIAL#

Trial and error would reveal some value, say $6, for which that person would be indifferent between the two alternatives. A person who would prefer the guaranteed $8 might also prefer $7 or $6 to the risky investment. Some would question this logic and say that they prefer the sure thing of $8 to the chance of losing $10. The “correct” path is the risky investment, and its value is $15. We solve the decision tree by first calculating the expected value of the chance node, 0.5 × 40 + 0.5 × (–10) = 15, and then selecting the better of the two alternatives: $15 vs. Selecting the risky alternative results in a 50% chance of winning $40 and a 50% chance of losing $10. The decision tree represents a choice between a safe and a risky investment. 1 shows a simple decision tree with one choice node and one chance node. Thus, the optimal action at each square might be a maximum (for NPV) or a minimum (for cost) of the various branches emanating from that square.įig. The numbers at end nodes generally represent either net present value (NPV) or marginal cost-the goal being to either maximize NPV or minimize cost. The solution to the decision tree consists in this pairing of root value and optimal path.

Ultimately, we obtain a value for the root node. The evaluation is simple, consisting of alternating between calculating weighted averages or expected values at each circle, then choosing the best action from each square. By assigning probabilities and values along the way, we can evaluate each path to select an optimal path. The actions are within the control of the decision-makers, but the events are not. The idea is to describe several possible paths representing deliberate actions or choices, followed by events with different chances of occurrence. Beyond these initial uncertainty nodes’ branches, there may be more squares and more circles, which generally alternate until each path terminates in an end node.

The circle’s branches represent the possible outcomes along with their respective probabilities (which sum to 1.0). At the end of these initial branches, there is either an end node (triangle, also called a value node) or an uncertainty node (circle, also called a chance node). For now, observe that it grows from left to right, beginning with a root decision node (square, also called a choice node) the branches of which represent two or more competing options available to the decision makers. 1, explained in detail later in this article. When we simply wish to quantify the risk or the uncertainty, the tool of choice is Monte Carlo simulation.Ī decision tree is a visual model consisting of nodes and branches, such as Fig. Often when we are choosing between competing alternatives, we turn toward decision trees. The tools we use depend on the nature of the problem we are trying to solve. Sometimes, but not always, we are trying to choose between competing courses of action. Always, we are trying to estimate something of value or cost. By risk analysis, we mean applying analytical tools to identify, describe, quantify, and explain uncertainty and its consequences for petroleum industry projects. Risk analysis is a term used in many industries, often loosely, but we shall be precise.

Decision tree software engineering ppt