Introduction to Decision Analysis

MGT3303Michel Leseure

Introduction to Decision Analysis

• Decision analysis studies the process of making difficult decisions

• The objective is to update, model, and document the intuition of managers

• A structured approach to decision making is especially critical in group decision making

• Key importance in the case of resource decisions (operations strategy)


Why are decisions difficult?

• Complexity– Simply keeping all the issues in mind at

one time is nearly imposible

• Uncertainty– Making a decision is especially difficult

when you are not sure about one decision variable’s state

• Multiple objectives• Different perspectives lead to different

conclusions


Example

• Winter of 1985:– The Oregon Department of Agriculture

(ODA) faced an infestation of gypsy moth in Lane County in Western Oregon

– Forest industry representative call for an aggressive eradication campaign


Alternatives

• Use BT, a bacterial insecticide– target specific– ecologically safe– reasonably effective

• Use Orthene, a chemical spray– registered as acceptable for home garden use– questions about its ultimate ecological effects– possible danger to humans

• Possible to use both


Opinions

• Forestry officials: – Argue Orthene is more potent and is necessary to

ensure eradication

• Environmentalists:– Orthene potentially too dangerous

• Others– Already too late anyway

• Others...– Still time but decision/implementation has to be

made now


Subjective Judgments

• In decisional contexts such as the one faced by ODA– objective data is lacking– a procedure determining an « optimal » decision

derived from objective data is of little use...– personal statements about uncertainty and value

become important inputs (no choice...)– Discovering and finalizing these judgments is a key

issue in decision analysis– Instead of criticizing them, we will learn how to

better assess and use them


The Decision Analysis Process

• Identify the decision situation and understand objectives

• Identify alternatives• Decompose and model the problem

– model of problem structure– model of uncertainty– model of preferences

• Choose the best alternative• Sensitivity analysis• Is further analysis needed? yes/no?• Implement the chosen alternative


Requisite Decision Models

• A model can be considered requisite only when no new intuitions emerge about the problem

• or when it contains everything that is essential for solving the problem


Elements of a Decision Problem

• Values and objectives• Decisions to make• Uncertain events• Consequences


Values and Objectives

• Value: used in its general sense « things that matter to you »

• Objective: Specific thing that you want to achieve

• The set of objectives taken up together make up the values

• Values are the reason for making the decision in the first place

• They define the decision context


Objectives for Boeing’s Supercomputer

SupercomputerObjectives

CostFive-year costsCost of improved performance

PerformanceSpeedThroughputMemory SizeDisk SizeOn-site performance

User NeedsInstallation dateRoll in/Roll outEase of UseSoftware compatibilityMean timebetween failures

Operational NeedsSquare footageWater coolingOperator toolsTelecommuni--cationsVendor support

ManagementIssueVendor HealthUS OwnershipCommitmentto supercomputer


Decision to Make

• Given a decisional context, one (or several) decision(s) has to be made

• In some cases, several decisions may have to be made in a sequence

Decision 1 Decision 3Decision 2

Time


Uncertain Events

– Uncertain events are either linked to chance or are linked to a probability distribution

– Uncertain events have outcome– It is important to position uncertain events

appropriately between decisions


Time


Consequences

• After the last decision has been made and the last uncertain event has been resolved, the decision maker’s fate is finally determined


Time

Consequence


Example

PolicyDecision

AccidentManagement

DecisionsTime

Accident

Consequence:Cost $

Environmentaldamage

PR damage

Cause

Weather

Location

Weatherfor

Cleanup

Cost

EnvironmentalDamage


Making Choices

• Decision Trees• Example: Texaco vs. Pennzoil• Decision trees and expected value

– certainty equivalent


Decision Trees

• Decision trees are a graphical representation of a decision problem

Invest

Do not invest

Venture succeeds

Venture fails

Typical return earnedon less risky investment

Funds lost

Large return


Decision Tree

Forecast

Evacuate

Stay

Storm hits Miami

Storm misses Miami

Safety Cost

Safe

Safe

Danger

High

Low

Low


Cash Flows and Probabilities

• To each branch of the tree, we can attach– a probability– and/or, a cash flow– or any measure replacing monetary values

for a specific problem


Case Study: Texaco vs. Pennzoil

• In early 1984, Pennzoil and Getty Oil agreed to the terms of a merger

• Before the signature of the formal agreement, Texaco offered Getty a substantially better price , and Gordon Getty (majority stockholder) defected on Pennzoil and sold to Texaco



• Pennzoil felt this was unfair practice and filed a lawsuit against Texaco, alleging that Texaco had interfered illegally in the the Pennzoil-Getty negotiations

• Pennzoil won the case in late 1985 and was awarded $11.1 billion – the largest settlement in the US at this point in time

• Texaco appealed and the settlement was reduced by $2 billion – but interest and penalty got the amount back to $10.3 billion



• Kinnear, Texaco’s CEO, announced that Texaco would file for bankruptcy if Pennzoil obtained court permission to secure the judgment by filing liens against Texaco’s assets

• Kinnear promised to fight the case all the way to the Supreme Court


Texaco’s Offer

• In April 1987, just before Pennzoil started filing liens, Texaco offered to pay Pennzoil $2billion to settle the entire case

• Liedtke, chairman of Pennzoil, announced that his advisors estimated that a settlement of 3-5 billions would be fair

What should Liedtke do?


Decision Tree SettlementAmount($billion)

2

Counteroffer$5 billion

5Texaco accepts $5 billion

Texacocounteroffers$3 billion

3Accepts $3 billion

Refuse

Final Court Decision

0510.3


05

10.3Texacorefusescounteroffer

Accepts $2 billion


Subjective Probabilities

• In the decision tree, we are missing probability estimates of the each event

• For this lecture, we will take these probability values for granted


Decision Tree SettlementAmount($billion)

2




3Accepts $3 billion

Refuse


05

10.3


05

10.3Texacorefusescounteroffer

(0.17)

(0.33)(0.50)

(0.2)

(0.5)

(0.3)

(0.2)

(0.5)

(0.3)

Accepts $2 billion


Expected Monetary Value

• Computing an expected monetary value is a way of selecting among risky alternative

• Computing expected values bring the problem back to a « certainty equivalent »

• What is the expected value of the court judgment?


Expected Value of the Court Judgment

• EV = 0.2 * 10.3 + 0.5 * 5 + 0.3 * 0• EV = $ 4.56 billion


0510.3

(0.5)

(0.3)

(0.2)

It is possible to reduce the tree with this certaintyequivalent


Reduced Tree

2




3Accepts $3 billion

Refuse

Texaco refuses counteroffer

(0.17)

(0.33)

(0.50)4.56

4.56

Eliminated

Accepts $2 billion


Expected Monetary Value of the Counteroffer

• What is the expected monetary value of Pennzoil $5 billion counter offer:

• EV = P(Texaco accepts) * 5 + P(Texaco refuse) * 4.56 + P(Texaco counteroffers) * 4.56

• EV = 4.63Liedtke should not accept the $2 billion offer, and should counter-offer $5 billion. If Texacorefuses, then the matter should be taken tocourt


Reducing the Decision Tree

• In practice, we do not reduce the decision tree but report expected values on the nodes


Resolved Decision Tree

2

4.63




3Accepts $3 billion

4.56Refuse


0510.3

4.56Final Court Decision

0510.3

Texacorefusescounteroffer

(0.17)

(0.33)(0.50)

(0.2)

(0.5)

(0.3)

(0.2)

(0.5)

(0.3)

Accepts $2 billion


Suggested Homework

• Problem S2-10, p. 70• Problem S2-13, p. 71

Documents

Introduction to Decision Analysis