Using Decision Trees To Teach Value Of Information Concepts
- Conference
- Location
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Pittsburgh, Pennsylvania
- Publication Date
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June 22, 2008
- Start Date
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June 22, 2008
- End Date
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June 25, 2008
- ISSN
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2153-5965
- Conference Session
- Tagged Division
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Engineering Economy
- Page Count
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14
- Page Numbers
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13.1335.1 - 13.1335.14
- DOI
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10.18260/1-2--3141
- Permanent URL
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https://peer.asee.org/3141
- Download Count
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1285
Paper Authors
Christopher Jablonowski University of Texas at Austin
Christopher J. Jablonowski is an Assistant Professor in the Department of Petroleum and Geosystems Engineering, and Associate Director of the Energy and Earth Resources Graduate Program at the University of Texas at Austin. Prior to joining the faculty at UT, he worked as a consultant with Independent Project Analysis, Inc. where he performed empirical research and capital project studies for oil and gas companies worldwide. He has also held positions as a Senior Drilling Engineer and Buyer with Shell Oil Company, and as an Energy Economist with the U.S. Government where he specialized in African and Middle East energy issues and quantitative analysis of energy markets. Dr. Jablonowski earned a B.S. in Civil Engineering from Virginia Tech, a M.B.A. from Tulane University, and a Ph.D. in Energy, Environmental, and Mineral Economics from Penn State.
Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract
Using Decision Trees to Teach Value of Information Conceptsa
Abstract
Most undergraduate engineering economics textbooks and related curricula include elements of decision analysis, and decision trees are often introduced and promoted as a decision making tool. The teaching of value of information analysis is less prevalent, notwithstanding the variety of potential applications to everyday decisions in engineering practice. This paper addresses this gap by providing a detailed demonstration of how decision trees can be used to value information. It includes a detailed set of decision trees that guide the student through a decision under uncertainty. After definition of a base case, cases are provided for the value of perfect and imperfect information. The value of incremental improvement in information is addressed, and a probabilistic approach is described and demonstrated. The influence of risk preference is also addressed.
Introduction
Most undergraduate engineering economics textbooks and related curricula include chapters or modules on decision analysis, and decision trees are often introduced and promoted as a decision making tool. The teaching of value of information (VOI) analysis within the decision analysis frame is less prevalent, notwithstanding the variety of potential applications to everyday decisions in engineering practice. This paper seeks to remedy this gap by providing an accessible demonstration of how decision trees can be used to teach important VOI concepts, including an analysis of the role of risk preferences.
It is often most efficient to set up and solve VOI problems in a generalized analytical framework, and this is often the approach taken in applied research.1-3 But analytical representations of VOI problems are not particularly intuitive and can confuse rather than enlighten students. Experience in the classroom has shown that decision trees are a more effective vehicle for teaching VOI concepts. The graphical representation is appealing and students tend to grasp the concepts rather quickly, typically in just one extended lecture.
This paper provides a detailed set of decision trees that guide the student through a decision under uncertainty. After definition of a base case, cases are provided for the value of perfect and imperfect information. The value of incremental improvement in information is addressed, and a probabilistic approach is described and demonstrated. The influence of risk preference is also addressed. The case is targeted at upper level undergraduates in project economics and engineering statistics.
The Base Case
We first examine a risk-neutral utility maximizing decision-maker with a utility function given by U $ X Xu , where u is the unit of utility.b The base case is depicted in Figure 1. The decision-maker faces a choice between investing in a project or doing nothing. If he invests, the unconditional probability of success is estimated to be 0.15 with a payoff of $500. The
Jablonowski, C. (2008, June), Using Decision Trees To Teach Value Of Information Concepts Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. 10.18260/1-2--3141
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