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ADDITIONAL MATERIALS
I. Short Readings
D. Gilbert and J. Scheck, “Big Oil Companies Struggle to Justify Soaring Project
Costs,” The Wall Street Journal, January 29, 2014, p. A1.
“What are the Top Five Risks the World Faces in 2014?” Knowledge@Wharton,
January 22, 2014,
http://knowledge.wharton.upenn.edu/article/what-are-the-top-five-risks-
the-world-faces-in-2014/
J. B. Stewart, “Studios Unfazed by Colossal Wrecks,” The New York Times,
December 21, 2013, p. B1.
D. Brooks, “Forecasting Fox,” The New York Times, March 22, 2013, p. A29.
H. Araton, “Strasburg still Wrestles with Principal that Protected Him,” The New
York Times, February 18, 2013, p. B5.
C. Drew, H. Tabuchi, and J. Mouawad, “Boeing 787 Battery was a Concern before
Failure,” The New York Times, January 30, 2013, pp. A1, B4.
J. M. Broder, M. L. Wald, and T. Zeller, “At U.S. Nuclear Sites, Preparing for the
Unlikely,” The New York Times, March 29, 2011, pp. D1, D5.
N. Shirouzo and P. Landers, “Japan Ignored Warning of Nuclear Vulnerability,” The
Wall Street Journal, March 23, 2011, pp. A1, A6.
M. Salfino, “Come Fourth Down, Atlanta isn’t Kicking,” The Wall Street Journal,
January 11, 2011, p. A16.
E. Lipton, “U.S. Report Lists Possibilities for Terrorist Attacks and Likely Toll,”
The New York Times, March 16, 2005, p. A1.
G. Ip, “How Two Economic Gurus Think about Risk,” The Wall Street Journal,
February 2, 2004, p. A2.
“Microsoft’s Universe of Risk,” CFO Magazine, March 1997, p. 69.
T. R. King, “Why ‘Waterworld’ with Costner in Fins is Costliest Film Ever,” The
Wall Street Journal, January 31, 1995, p. A1.
A. M Freedman and R. Gibson, “Maker of Simplesse Discovers Its Fake Fat Elicits
Thin Demand,” The Wall Street Journal, July 31, 1991, p. 1. (A good illustration of
multiple risks)
II. Longer Readings
W. M. Patchak, “Decision Analysis Software Survey,” OR/MS Today, October
2012.
A. Borison and G.Hamm, “How to Manage Risk (after risk management has
Failed),” Sloan Management Review, October 1, 2010, 51-57.
R. L. Keeney, “Personal Decisions Are the Leading Cause of Death,” Operations
Research, 56, No.6, 2008, 1335-1347.
R. L. Keeney and D. A. Vernik, “Analysis of the Biological Clock Problem,”
Decision Analysis, 4, 2007, 117-135.
D. von Winterfeldt and T. M. O’Sullivan, “Should We Protect Commercial Airlines
Against Surface-to-Air Missile Attacks by Terrorists?” Decision Analysis, Vol. 3,
June 2006, 63-75.
M. Baucells and C. Rata, “A Survey Study of Factors Influencing Risk-Taking
Behavior in Real-World Decisions under Uncertainty,” Decision Analysis, Vol. 3,
September 2006, 263-276.
R. L. Keeney, “Making Better Decision Makers,” Decision Analysis, Vol. 1,
December 2004, 193-204.
D. N. Sull, “Disciplined Entrepreneurship,” MIT Sloan Management Reivew, 46,
Fall 2004, 71-77.
S. A. Lippman and K. F. McCardle, “Sex, Lies, and the Hillblom Estate: A
Decision Analysis,” Decision Analysis, 1, September 2004, 149-166.
Keefer, D. L., C. W. Kirkwood, and J. L. Corner, “Perspective on Decision
Analysis Applications, 1990-2001,” Decision Analysis, March 2004,
pp. 4-22.
Kirkwood, C. W., “Approximating Risk Aversion in Decision Analysis
Applications,” Decision Analysis, March 2004, pp. 51-67.
Lovallo, D. and D. Kahnman, “Delusions of Success: How Optimism Undermines
Executives’ Decisions,” Harvard Business Review, July-August 2003, 56-63.
Watkins, M. D. and M. H. Bazerman, “Predictable Surprises: The Disasters You
Should Have Seen Coming,” Harvard Business Review, April 2003, 72-80.
Kahneman, D., “Maps of Bounded Rationality: Psychology of Behavioral
Economics,” American Economic Review, 93, 2003, 1449-1475.
Stonebraker, J. S., “How Bayer Makes Decisions to Develop New Drugs,”
Interfaces, 32, November-December 2002, 77-90.
Dillon, R. L., R. John, and D. von Winterfeldt, “Assessment of Cost Uncertainties
for Large Technology Projects: A Methodology and an Application,” Interfaces, 32,
July-August 2002, 52-66.
Friedman, D., “Monty Hall’s Three Doors: Construction and Deconstruction of a
Choice Anomaly,” American Economic Review, 88, 1998, 933-946.
J. Weisberg, “Keeping the Boom from Busting,” The New York Times Sunday
Magazine, July 19, 1998, p. 24. This article examines the risk calculations and
decision methods of Robert Rubin, Treasury Secretary in the Clinton
administration.
H. Courtney, J. Kirkland and P. Viguerie, “Strategy under Uncertainty,” Harvard
Business Review, November-December 1997, pp. 67-79. Reprint 97603.
III. Cases
Chance Encounters II (UVA-QA-0783), Darden Business School Publishing,
University of Virginia, 2012.
Airbus and Boeing: Superjumbo Decisions (UVA-QA-0720), Darden Business
School Publishing, University of Virginia, 2008.
Understanding Corporate Value-at-Risk (9-206-046), Harvard Business School,
2006.
Blackout: August 14, 2003 (9-804-156), Harvard Business School, 2004.
Align Technology, Inc.: Matching Manufacturing Capacity to Sales Demand
(9-603-058), Harvard Business School, 2002.
Orion Controls (A) and (B) (UVA-QA-0602 and 0603), Darden Business School
Publishing, University of Virginia, 2002.
Merck & Co.: Evaluating a Drug Licensing Opportunity (9-201-023), Harvard
Business School, 2001. Teaching Note (5-202-001)
Complexity and Error in Medicine (9-699-024), Harvard Business School, 2000.
Canonical Decision Problems (9-396-308), Harvard Business School, 1997.
Teaching Note (5-396-313)
Amgen Inc.: Planning the Unplannable (9-492-052), Harvard Business School,
1997. Teaching Note (5-492-054)
R. D Behn, “The Shed Load Decision,” Duke University, Institute of Policy
Sciences and Public Affairs, 1993.
Biotechnology Strategies in 1992 (9-792-082), Harvard Business School, 1992.
Arundel Partners (9-292-140), Harvard Business School, 1992. Teaching Note
(5-295-118)
Strategic Response to Uncertainty (9-391-192), Harvard Business School, 1993
Freemark Abbey Winery, Harvard Business School, (9-181-027), 1981, 2 pages. (A
winery must decide when to harvest its grapes in light of uncertain weather
conditions.)
Key West Fisheries. (See the following mini-case and teacher note). A small tuna
fishing outfit must decide whether to i) contract with a Spanish importer to sell a
portion of its catch at a fixed price, or ii) reject the contract and sell all its catch at
uncertain domestic prices. Both the total size of the fishing outfit’s catch and the
domestic price are uncertain.
Key West Fisheries
In March 1997, Harry Morgan, the founder and president of Key West Fisheries faced a difficult
decision. The company has been in the tuna-fishing business for twelve years, operating a single
boat. Morgan has seen the company’s fortunes rise and ebb, mainly depending upon the price of
tuna. When prices are high, tuna fishermen prosper. When prices fall, they face tough times. He is
uneasy about the company’s future because of uncertainties about prices and the size of future
catches.
Earlier in the week, Morgan received a Fax from a Spanish tuna importer to whom he has sold
fish in the past. The importer is offering to make a one-season contract with Key West Fisheries to
deliver 150,000 pounds of tuna at a price of $1.10 per pound. The tuna is to be delivered by
October 31. Morgan has to decide in the next 10 days whether to accept or reject the Spanish
contract.
In considering this decision, Morgan has gathered the following information. The tuna season in
the south Atlantic runs from the beginning of April to the end of October. (Not all of this time can
be devoted to fishing; time in port or traveling to fishing grounds means lost fishing days.) At the
risk of some over-simplification, Morgan foresees two different types of seasons, good and bad.
(The type of season depends upon ocean currents, tuna migration patterns, the number of
competing boats, and so on.) In a good season, the total Atlantic catch will be large, and the
company can catch 300,000 pounds of tuna. In a bad season (small catches), Morgan will be able
to catch only 240,000 pounds. Before the beginning of the season, it is impossible to tell whether
it will be good of bad. Based on his past experience, Morgan sees good and bad seasons as
Copyright 2012 © Boston University. This case has been adapted from Edgartown Fisheries
(A).
equally likely. However, after the first four weeks of the season, Morgan can discern the
prevailing pattern (large or small catches).
Tuna is caught not only in the Atlantic but also in the Pacific, primarily by US, Russian, and
Japanese fishermen. Therefore, the price of Atlantic tuna depends not only on whether the
Atlantic catch is large or small but also on the size of the Pacific catch. The expected price of tuna
depends upon the catches in the respective oceans as follows:
Atlantic Large/Pacific Large: $.50 per pound
Atlantic Large/Pacific Small: $.80 per pound
Atlantic Small/Pacific Large: $.70 per pound
Atlantic Small/Pacific Small: $1.00 per pound
Morgan considers each of these four possibilities to be equally likely. (The type of Pacific season
is fifty-fifty, and the outcomes of the two seasons are independent of one another.) As with the
Atlantic catch, the size of the Pacific catch cannot be predicted prior to the season. However, the
type of Pacific season will be known after the first three or four weeks of fishing.
If Morgan accepts the contract, there are two options for delivering the fish to Spain. He can hire
a commercial freighter to take all or part of the 150,000 pounds at a cost of $.44 per pound.
Alternatively, Morgan can use his own boat to deliver the fish. This would require two round-trips
since his boat can carry only 75,000 pounds. (The cost difference between operating the boat for
shipping versus fishing is insignificant.) However, if the company delivers the fish itself, it will
lose about 1/3 of the available fishing days (meaning a 1/3 reduction in its total catch).
Revenues, Costs, and Assets. The company’s revenues in the coming season consist of its
domestic sales (at uncertain prices) plus revenues from the importer (if the contract is accepted).
The company’s total cost for the entire fishing season is $180,000 (the sum of crew’s wages,
interest expenses, office rent, etc.) regardless of how much fish is caught. Besides its boat, the
company currently has $30,000 in liquid assets.
Morgan has decided to sketch a decision tree to help him determine whether to accept the contract
(and if so, how to deliver the fish). He has decided to measure outcomes in terms of
end-of-season liquid assets. For example, if he rejects the contract and there are large catches in
both oceans, his end-of-season assets are: 30 + [($.50)(300) - 180] = $0 thousand. (The low price
results in a $30 thousand dollar loss reducing his assets to zero.)
If he accepts the contract, uses the freighter, and both catches are large, his assets are: 30 + [165 -
66 + 75 - 180] = $24 thousand, and so on.
Assuming Morgan is risk neutral, what is his best course of action?
Additional questions.
1. Suppose that Morgan is able to delay his contract decision until after the first of May
(when he will know the types of Atlantic and Pacific season). How much is this postponement
worth to him?
2. Suppose instead that Morgan can hire the services of a marine biologist who will be able
to give him a perfect forecast of the Pacific season (large or small) immediately. How much is
this forecast worth?
3. Morgan is risk averse. He has provided the following certainty equivalents for fifty-fifty
gambles. Sketch his utility curve, and use this to determine his preferred course of action:
50-50 Gamble
Certainty
Equivalent 50-50 Gamble
Certainty
Equivalent
a) $90,000
and $0
$24,000 d) $90,000
and $52,000
$69,000
b) $90,000
and $24,000
$52,000 e) $52,000
and $24,000
$35,000
c) $24,000
and $0
$6,000 f) $24,000
and $6,000
$13,000
