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UVA-MOD-0101Y
Rev. Nov. 5, 2013
DECISION ANALYSIS
Syllabus
Introduction
Business leaders and managers make countless
decisions both large and small. Some of these decisions
warrant careful consideration, whereas some can be
nearly automated. Regardless of the differences among
them, nearly all business decisions are complicated by
the presence of uncertainty. Uncertainty exists in
business because managers must choose a course of
action before they can know exactly what outcome will
transpire. By extension, managers must choose between
competing courses of action before they know which
action will lead to the most desired outcome. We say
that uncertainty gives rise to risk: If we agree that a
manager must choose a course of action without
knowing its precise outcome, then risk is the degree to
which the actual outcome might deviate from the
manager’s expectations.
About Darden Course Syllabi
The Darden Graduate School of
Business Administration is regularly
recognized as having one of the
world’s premier teaching faculties
within business education. Darden
Business Publishing is pleased to
provide current Darden course syllabi
for verified faculty members. These
syllabi provide instructors with context
as to how cases could be used in a
particular sequence to achieve the
learning outcomes of the teaching
teams at the Darden School. Use the
modules in these course syllabi as a
reference for updating case materials
within your school’s programs.
All too often, decision makers make risky
choices from a qualitative gut reaction. This behavior is understandable: The human mind has
both cognitive and emotional difficulties in dealing with risk. In seeking to improve on this
behavior, the course is based on the following claims:
1. Nearly all business decisions can be evaluated quantitatively: The influencing factors and
potential outcomes for any decision can be represented and understood numerically.
Objectives that are not strictly economic can be included.
2. Individual judgment and intuition based on experience can be incorporated into the
quantitative approach.
This syllabus was prepared by the Decision Analysis faculty of the Darden Graduate School of Business
Administration at the University of Virginia. Copyright  2011 by the University of Virginia Darden School
Foundation, Charlottesville, VA. All rights reserved. To order copies, send an e-mail to
[email protected]. No part of this publication may be reproduced, stored in a retrieval system,
used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying,
recording, or otherwise—without the permission of the Darden School Foundation.
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3. Business people will achieve consistently better outcomes when they explicitly
incorporate risk into their decision making.
Course Description
This course highlights the analytical methods that decision makers use to gain insight into
risk and uncertainty to help students develop the skill and sophistication to artfully use them.
Students will become better decision makers as they learn to proactively manage risk in creative
ways.
The course emphasizes the design of analyses to fit circumstances and interpretation of
results; it does not emphasize the mastery of sophisticated mathematical techniques. It studies
and integrates individual judgment and personal intuition in realistic business situations with the
most widely applicable methodologies of decision and risk analysis, probability and statistics,
competitive analysis, and management science. The goal is always the quality of the decisions
made in light of better analysis and deeper thinking, rather than simply the analysis itself.
The first half of this course focuses more on a general framework for thinking about and
managing risk. Students use the language of probability distributions to describe the uncertainty
they face and simulation as a tool to explore the impact of that uncertainty for the decisions they
face. Situations involving uncertain future cash-flow streams and the role of the timing of those
cash flows on the valuing of opportunities will be examined. The role data can play in shaping
calibration of future uncertainties, specifically touching on the use of parametric probability
distributions as a way to describe uncertainty, sampling as a source of data, and regression as a
tool to capture the relationship between uncertain quantities to produce better forecasts are
considered. Students will also gain skill in spreadsheet modeling.
The second half of the course builds on the first by focusing more on ways to proactively
manage risk, particularly through identifying and/or creating opportunities to add value and/or
reduce risk through the sequencing of decisions. Students will consider the value of acquiring
additional information before decisions have to be made, as well as the value of strategies to
reduce (or eliminate) risk at the time of decision. Gaining experience with assessing uncertainties
and forecasting probability distributions and learning ways to address competitive situations,
where uncertainty includes not knowing how a competitor might behave, will be topics. Students
will learn how to influence active competitors that are capable of anticipating their actions and
responding to them (in part, via analysis of matrix games). Finally, the use of linear constrained
optimization models (and Excel Solver) to aid decisions with a large number of decision
variables and constraints will be studied.
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Objectives

To develop a process for knowing when and how to do managerially relevant analysis
under conditions of uncertainty, many decision variables, and unstructured contexts,
using both data and personal judgment.

To develop a framework for understanding uncertainty, a language for describing
uncertainty, and methodologies for making decisions in light of uncertainty, all through
field-based case studies about practicing managers.

To provide the basic skills and conceptual understanding of the most widely applicable
methodologies of decision and risk analysis, probability and statistics, competitive
analysis, optimization, and management science.
Course Materials
The cases and technical notes included in the course outline are required course materials
to be prepared prior to each class meeting. Some days, there will be an available electronic
spreadsheet containing data from the case. There will be online learning modules occasionally
made available to students. These can be tutorials, software demonstrations, or computing tasks.
Electronic items are linked from assignments or found in the online learning management
system.
Detailed assignment questions suggest specific steps in preparing for class. These skillbuilding days are based on expository reading about methodology to be mastered in carrying out
an analysis. Sometimes only the case is indicated with a single assignment question (or perhaps
none). This does not mean that no quantitative analysis is needed. Indeed, the task for students
includes diagnosing the situation and structuring an approach to the analysis from the case itself
as well as carrying out the analysis. Later, students will diagnose the problem and structure a
framework for a decision key.
A list of supplementary references follows. Since the course approaches topics from a
pragmatic, decision-oriented perspective, students should, in general, be wary of devoting too
much time to reading text but use the better sources of help provided by a learning team or
instructor. Nonetheless, of the supplementary references, the first three are among the best.

Bell, D. E. and A. Schleifer, Jr., Decision-Making Under Uncertainty (Cambridge, MA:
Course Technology, Inc., 1995).

Bodily, S. E., R. L. Carraway, S. C. Frey Jr., and P. E. Pfeifer, Quantitative Business
Analysis: Text and Cases (New York: Irwin McGraw-Hill, 1998).

Brightman, H. J., Statistics in Plain English (Cincinnati, OH: South-Western Publishing
Co., 1986).
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
Clemen, R. T., Making Hard Decisions: An Introduction to Decision Analysis (Boston:
PWS-Kent Publishing Company, 1991).

Curwin, J. and R. Slater, Numeracy Skills for Business (London: Chapman & Hall, 1994).

Eppen, G. D., F. J. Gould, and C. P. Schmidt, Introductory Management Science, 2nd ed.
(Englewood Cliffs, NJ: Prentice-Hall, Inc., 1987).

Freedman, D., R. Pisani, and R. Purves, Statistics (NY: W.W. Norton & Company, Inc.,
1978).

Hamburg, M., Statistical Analysis for Decision Making, 4th ed. (NY: Harcourt Brace
Jovanovich, Publishers, 1987).

Holloway, C. A., Decision Making Under Uncertainty (Englewood Cliffs, NJ: PrenticeHall, Inc., 1979).

Keeney, R. L., Value-Focused Thinking: A Path to Creative Decision-Making (Harvard
University Press, 1996).

Kirkwood, C. W., Strategic Decision Making: Multi-Objective Decision Analysis with
Spreadsheets (Belmont, CA: Duxbury Press, 1997).

Lapin, L. L., Statistics for Modern Business Decisions, 2nd ed. (NY: Harcourt Brace
Jovanovich, Publishers, 1978).

Plane, D. E., Management Science: A Spreadsheet Approach (Danvers, MA: Boyd &
Fraser Publishing, 1994).

Powell, S. G. and K. R. Baker, Management Science: The Art of Modeling with
Spreadsheets, 2nd ed. (Hoboken, NJ: 2007).

Ragsdale, C. T., Spreadsheet Modeling and Decision Analysis: A Practical Introduction
to Management Science (Cambridge, MA: Course Technology, Inc., 1995).

Rosenthal, J. S., Struck by Lightning: The Curious World of Probabilities (Washington,
DC: Joseph Henry Press, 2006).

Savage, S. L., The Flaw of Averages: Why We Underestimate Risk in the Face of
Uncertainty (Hoboken, NJ: John Wiley & Sons, Inc., 2009).
Prior to class, students should not discuss the case assigned to that class with anyone who
has sat through a previous (or simultaneous) class discussion of the case as side discussion and
information impairs the pedagogical design of many classes and interferes with the learning
process. Students are preparing for situations in which they will not have hindsight information,
and they should get ready for them now.
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UVA-MOD-0101Y
Darden Course Instructors
Darden Teaching Faculty
Phillip Pfeifer
Yael Grushka-Cockayne
Casey Lichtendahl
Samuel Bodily
Cases by This Author
Pfeifer cases
Grushka-Cockayne cases
Lichtendahl cases
Bodily cases
Course Outline
Class
Materials
Introduction
1
“Reid’s Raisin Company (A)” (Dartmouth Tuck case)
“Influence Diagram Exercises” (UVA-QA-0373)
“Framework for Analyzing Risk” (UVA-MOD-0101)
2
“George’s T-Shirts” (UVA-QA-0346)
“George’s T-Shirts Addendum” (UVA-QA-0548)
3
“George’s Revised Forecasts” (UVA-QA-0560)
Probability Distributions: Ch. 11, QBA
“PriceMax Forecasting Exercise” (UVA-QA-0748)
Supplemental Spreadsheet Available.
“Eliciting & Evaluating Expert Forecasts” (UVA-QA0734)
5
“Skanska USA Building” (UVA-QA-0735R)
Supplemental Spreadsheet Available. Postclass learning
module: Building Dhahran Spreadsheet
“Evaluating Multiperiod Performance” (UVA-QA-0518)
6
“Appshop, Inc.” (UVA-QA-0618)
“Quick Start Guide to Crystal Ball” (UVA-QA-0658)
“Analyzing Uncertainty, Probability Distributions, and
Simulation” (UVA-QA-0660)
Learning Module: Crystal Ball Litigate Demo
“Data and Distributions” (UVA-MOD-0102)
7
“Engine Services, Inc.” (UVA-QA-0663)
Supplemental Spreadsheet Available.
“A Brief Primer on Probability Distributions” (UVA-QA0517)
8
“Sprigg Lane (A)” (UVA-QA-0372)
Supplemental Spreadsheet Available.
9
“Ponca City Cogeneration Plant” (UVA-QA-0469)
“Ponca City Cogeneration Plant: Model Improvement and
Final Decision” (UVA-QA-0747)
“Ponca City Cogeneration Plant Supplement” (UVA-QA0470)
4
Topic
Building and Vetting Spreadsheets; Search for
Optimal Quantity
Choice and Expected Monetary Value
Language, Forecasting, and Use of Probability
Distributions for Discrete and Continuous
Uncertainties
Comparing Forecasters, Overconfidence
Multiperiod Pro Forma and NPV
Simulating NPV
Analytical Distribution from Data; Searching
for a Quantity
Probability Distributions and Spreadsheet
Modeling; Risk
Risk Management
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Supplemental Spreadsheet Available. Learning Module:
Tornado Sensitivity
10
“Amore Frozen Foods (A) Macaroni and Cheese Fill
Sample Uncertainty
Targets” (UVA-QA-0317)
Supplemental Spreadsheet Available.
“Sampling” (UVA-QA-0513)
“Regression” (UVA-MOD-0103)
11
“Hightower Department Stores: Imported Stuffed Animals Introduction to Regression
Introduction to Least Squares” (UVA-QA-0268)
Supplemental Spreadsheet Available.
12
“Edgcomb Metals (A)” (UVA-OM-0549)
Multiple Regression and Dummy Variables
Supplemental Spreadsheet Available.
13
“Ballis’s Benchmark (A)” (UVA-F-1621)
Data Filtering, Pivot Tables, and Regression
Supplemental Spreadsheet Available.
“Ballis’s Benchmark (A) Supplement” (UVA-F-1623)
14
“The Oakland A’s (A)” (UVA-QA-0282)
Regression Modeling
The Oakland A’s (A) Supplement
“Linear Model-Building” (UVA-QA-0293)
15
“Lac Leman Festival de la Musique (A)”(UVA-QA-0707) Summary
“Lac Leman Festival de lat Musique (B)” (UVA-QA0708)
Supplemental Spreadsheet Available.
“Decision Trees and Downstream Decisions” (UVA-MOD-0104)
16
“Freemark Abbey Winery” (HBSP case)
Decision Tree Analysis, Value of Information
Learning Module: Building a Freemark Decision Tree with & Control, Risk Aversion
TreePlan
17
“Orion Controls (A)” (UVA-QA-0602)
Downstream Decision
18
“Merck & Company: Product KL-798” (UVA-QA-0582) Decision Tree Analysis Practice
19
“Orion Controls (B)” (UVA-QA-0603)
Downstream Decision with Continuous
Uncertainties
20
“Chance Encounters II” (UVA-QA-0783)
Real Options
Supplemental Spreadsheet Available.
“Strategic Interactive Decisions” (UVA-MOD-0105)
21
“Lesser Antilles Lines: The Island of San Huberto” (UVA- Matrix Game Analysis
QA-0355)
Supplemental Spreadsheet and Link to Online Competition
Available.
“Structuring a Competitive Analysis: Decision Trees,
Decision Forests, and Payoff Matrices” (UVA-QA-0674)
“Competitor Analysis” (UVA-QA-0527)
22
“Germania Fluggesellschaft MBH (A)” (UVA-QA-0620) Structuring Competitive Analysis
23
“Kelly Solar” (UVA-F-1614)
Negotiation Analysis
24
“Bidding for the Legacy Hotel” UVA-QA-0696)
Bidding and Auctions
Supplemental Spreadsheet Available.
25
“Maxco, Inc., and the Gambit Company” (HBSP case)
Competitive Bidding
“Optimization with Solver” (UVA-MOD-0106)
26
“Jaikumar Textiles, Ltd. (A):The Nylon Division” (UVA- Optimizing with Constraints
QA-0364)
-7“Jaikumar Textiles, Ltd. (B): The Nylon Division” (UVAQA-0391)
28
“Chandpur Enterprises Limited, Steel Division” (UVAQA-0761)
Supplemental Spreadsheet Available.
29
“Solver Optimization Modeling Exercises” (UVA-QA0606)
Supplemental Spreadsheet Available.
“Integration”
30
“Salmones Puyuhuapi (A)” (UVA-QA-0749)
“Salmones Puyuhuapi (B)” (UVA-QA-0750)
“Salmones Puyuhuapi (C)” (UVA-QA-0751)
Supplemental Spreadsheet Available.
27
UVA-MOD-0101Y
Linear Programming; Optimization Using
Excel’s Solver Add-In
Using Linear Programming; Results and
Sensitivity
Optimization Modeling with Solver
Integration and Summary