Brownley Chap One

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Multi-objective DecisionAnalysis


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Multi-objective DecisionAnalysis

Managing Trade-offs and Uncertainty

Clinton W. Brownley, Ph.D.


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Multi-objective Decision Analysis: Managing Trade-offs and Uncertainty

Copyight Business Epet Pess, 2013.

All ights eseved. No pat of this publication may be epoduced,

stoed in a etieval system, o tansmitted in any fom o by any

meanselectonic, mechanical, photocopy, ecoding, o any othe

ecept fo bief quotations, not to eceed 400 wods, without the pio

pemission of the publishe.

Fist published in 2013 by

Business Epet Pess, LLC

222 East 46th Steet, New Yok, NY 10017www.businessepetpess.com

ISBN-13: 978-1-60649-452-3 (papeback)

ISBN-13: 978-1-60649-453-0 (e-book)

Business Epet Pess Quantitative Appoaches to Decision

Making Collection

Collection ISSN: 2163-9515 (pint)

Collection ISSN: 2163-9582 (electonic)

Cove and inteio design by Eete Pemedia Sevices Pivate Ltd.,

Chennai, India

Fist edition: 2013

10 9 8 7 6 5 4 3 2 1

Pinted in the United States of Ameica.


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For my wife, Anushka, who insists goals can be achieved through

focus, enthusiasm, and perseverance


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Abstract

Whether managing strategy, operations, or products, making the best

decision in a complex, uncertain business environment is challenging.One o the major diculties acing decision makers is that they oten

have multiple, competing objectives, which means trade-os will need

to be made. o urther complicate matters, uncertainty in the business

environment makes it hard to explicitly understand how dierent objec-

tives will impact potential outcomes. Fortunately, these problems can be

solved with a structured ramework or multiobjective decision analysis

that measures trade-os among objectives and incorporates uncertaintiesand risk preerences.

Tis book is designed to help decision makers by providing such an anal-

ysis ramework implemented as a simple spreadsheet tool. Tis ramework

helps structure the decision-making process by identiying what inorma-

tion is needed in order to make the decision, dening how that inormation

should be combined to make the decision, and, nally, providing quanti-

able evidence to clearly communicate and justiy the nal decision.Te process itsel involves minimal overhead and is perect or busy

proessionals who need a simple, structured process or making, track-

ing, and communicating decisions. With this process, decision making is

made more ecient by ocusing only on inormation and actors that are

well dened, measureable, and relevant to the decision at hand. Te clear

characterization o the decision required by the ramework ensures that a

decision can be traced and is consistent with the intended objectives and

organizational values. Using this structured decision-making ramework,

anyone can eectively and consistently make better decisions to gain a

competitive and strategic advantage.

Keywords

decision making, decision analysis, decision modeling, strategic deci-

sions, business decisions, how to decide, trade-os, multiobjective, values,

weights, value unctions, objectives, measures, alternatives, uncertainty,

probability, discrete, continuous, linear, exponential, expected value, utility,

expected utility, risk tolerance, certainty equivalents


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Contents

Acknowledgments ix

Chapter 1 Introduction to Multiobjective Decision Analysis ..............1

Chapter 2 Structuring Objectives and Developing Alternatives ........19

Chapter 3 Value Functions and Preerence Weights ..........................45

Chapter 4 Uncertainty: Probability Distributions andExpected Value ................................................................73

Chapter 5 Uncertainty: Risk olerance and Expected Utility ............95

Chapter 6 Multiobjective Decision Analysis Under Uncertainty .....111

Chapter 7 Conclusion ....................................................................133

Notes 145

References 151

Index 157


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Acknowledgments

A book like this cannot be written without help rom many people. Four

in particular played key roles. First and oremost, I want to thank my

wie, Anushka, or being patient and supportive during all o the nights

and weekends I spent writing. An insightul reviewer, she also suggested

many ways to make the book more consistent, clear, and concise. Second,

I want to thank Steven Nahmias or reviewing the manuscript and pro-

viding helpul suggestions. Tird, I want to recognize Cindy Durand orproviding excellent production assistance. She provided superb guidance

on gathering permissions, organizing the images and tables, and com-

piling the index. Finally, I owe a special debt to the collections editor,

Don Stengel, or shepherding this book rom start to nish, improving it

with his editorial direction, and providing a cheerul, proessional hand

throughout.

I also want to thank the sta o Business Expert Press, especially ScottIsenberg and David Parker, or their support and assistance with this pro-

ject. Scott provided seasoned advice on an assortment o topics and was

also tremendously helpul during the manuscript review process.

Finally, I want to acknowledge that Multi-objective Decision Analysis

refects an intellectual journey as well as a writing project. I rst became

interested in decision analysis as an aid to judgment and decision making

while I was a student at Carnegie Mellon University. At CMU, I had the

great ortune to receive instruction, research experience, and inspiration

rom many distinguished decision scientists and operations researchers,

including Paul Fischbeck, Baruch Fischho, George Loewenstein, Don

Moore, Otto oby Davis, Robyn Dawes, Herbert Simon, M. Granger

Morgan, Jonathan Caulkins, Alred Blumstein, Michael rick, and

Michael Johnson.

Since then, I have enriched my understanding o decision analysis and

other eective techniques or strategic decision making by studying and

implementing the methodologies o many other exceptional decision

scientists, including Ward Edwards, Detlo von Wintereldt, Craig

Kirkwood, Howard Raia, Ronald Howard, Robert Schlaier, Ralph Keeney,


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Robert Clemen, Robert Winkler, Reid Hastie, Harold Sox, Peter Moore,

Howard Tomas, John Hammond, John Magee, Robin Hogarth, Hillel

Einhorn, Rex Brown, Kenneth Hammond, Daniel Kahneman, Amos

versky, Richard Taler, Tomas Gilovich, Paul Slovic, Max Bazerman,

Scott Plous, Jerey Keisler, Paul Goodwin, George Wright, Jacob Ulvila,

David Hertz, Samuel Bodily, Myriam Hunink, Paul Glaziou, and Gerd

Gigerenzer.

Each o these individuals has contributed to my understanding o

decision analysis as an aid to judgment and decision making, and this

book benets enormously rom the theoretical and applications-based

advances they pioneered.

x ACKNOWLEDGMENTS


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CHAPTER 1

Introduction toMultiobjective Decision

Analysis

Your best hope for a good decision outcome is a good decision process

J. Russo & P. Schoemaker

odays business environment is raught with complexity and uncertainty.

A variety o actors contribute to such complexitythe desire to achieve

multiple objectives at once, the wish to address the values and attitudes

toward risk o multiple stakeholders, the diculty o identiying suitablealternatives, the challenge o measuring intangibles, and the oten

impossibility o precisely predicting the uture consequences o alternatives.

While it would be magnicent i it were otherwise, complexity is inherent

to the business environment, so it cannot be avoided.

Such complexity makes it incredibly dicult to make important

decisions inormally in a deensible manner. And in todays high-stakes

business environment, managers need to be able to justiy and deendtheir decisions to a variety o impacted groups, including shareholders,

bosses, co-workers, the public, and themselves. Since inormal analysis is

likely to be insucient or most key business decisions, proessionals need

a ormal methodology and set o tools they can use to make and justiy

their decisions.

What Constitutes a Decision?

Since multiobjective decision analysis is a methodology that helps people

make inormed decisions, it is important to rst understand what a decision

is. A decision is an opportunity to make a choice between at least two


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2 MULTI-OBJECTIVE DECISION ANALYSIS

dierent things.1 Tat is, or a decision to exist there must be at least two

alternatives. Not having any alternatives may be a problem, but its not

a decision problem. And, o course, the attractiveness o the alternatives

matters. It can be very rustrating to ace a decision situation in which there

seem to be nogoodalternatives.

Many important decisions involve alternatives that lead to dierent

consequences. Consider the decision o whether to build a new

manuacturing plant. Te consequences o building the new acility are

likely to be signicantly dierent rom the consequences o not build-

ing the acility. Tis makes the decision meaningul. I the potential

consequences are not dierent, that is, i the alternatives result in the same

consequences, then the choice between alternatives isnt very meaningul.

O course, one o the most challenging aspects o any decision situa-

tion is deciding on the actors that are important or evaluating the con-

sequences o the alternatives, that is, selecting the values, objectives, and

evaluation measures.2 Consider, once again, the decision o whether to

build a new manuacturing plant. What actors wouldyou use to evaluate

whether or not to build the acility? A ew actors you may consider areexpected revenue, market share, product mix, time to completion, and

cost. Which actors should you use to make the decision? Fortunately,

though sometimes rustratingly, there is no one-and-only-one correct

answer to this question. As the decision maker, you should include all

o the actors you consider important or evaluating the alternatives in

the given decision context. Since generating and structuring the values,

objectives, and evaluation measures is a challenging, though incred-ibly valuable, step in any decision-making process, these activities are

discussed in more detail in the next chapter.

Finally, many important decisions involve uncertainty about the

consequences o the alternatives. Tat is, in many situations, we must

make a decision now without the benet o knowing with certainty what

will be the uture result or outcome o our decision. Consider, or one

nal time, the decision o whether to build a new manuacturing plant.As stated earlier, one o the actors we may use to evaluate the alternatives

is the amount o revenue to be generated by the new acility. Despite

our best eorts at estimation, orecasting, and simulation, the new acil-

ity doesnt even exist yet, so we cannot know with certainty the amount


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INTrODUCTION TO MULTIOBJECTIVE DECISION ANALYSIS 3

o revenue that will be generated i we build the new acility. Since

quantiying and dealing explicitly with uncertainty is a tremendously

important step in any decision-making process, the procedures or doing

so are discussed in chapters our and ve o this book.

What Are the Challenges of Decision Making?

Many important business decisions are extremely challenging to make.

Oten the challenge stems rom the complexity o the decision itsel or the

environment in which its being made.3 Since multiobjective decision anal-

ysis is meant to help decision makers deal with complexity systematically,

it is useul to describe the actors that contribute to decision complexity.

Keeney lists several actors that make decisions complex and challenging:4

Multiple objectivesOne actor that makes decisions complex and chal-

lenging is when there are multiple, conficting objectives. With multiple,

competing objectives, it isnt possible to maximize all o the objectives

with a single alternative.For example, suppose a business owner wants to purchase a commercial

real estate property rom a list o properties. Te business person would

certainly have and want to do as well as possible on several objectives,

including maximizing the total square ootage obtained and minimiz-

ing the purchase price. However, these two objectives are likely to be in

confict, or competing, with one another. Tat is, the property with the

greatest amount o square ootage is likely to have the highest price, ratherthan the lowest. Since a property with both the most square ootage and

the lowest price is unlikely to exist, the business person will need to decide

on the choice strategy he or she will use to evaluate the alternatives.

Some choice strategies are noncompensatory, meaning they do not

allow or trade-os among the objectives.5 An example o a noncompensatory

strategy would be i the business person chose to ignore purchase price and

then used the square ootage values to choose a property. Other choice strat-egies, such as multiobjective decision analysis, are compensatory, meaning

they do allow or trade-os among the objectives. With a compensatory

strategy, the business person would evaluate trade-os between the square

ootage and purchase price values in order to choose a property.


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Using a compensatory choice strategy such as multiobjective decision

analysis requires more mental eort and data than using a noncompensa-

tory strategy; however, doing so is worthwhile because it enables decision

makers to incorporate all o their objectives into the decision and make

value trade-os among their objectives.

Good alternativesSome o the most rustrating decision situations are

when it is dicult to identiygoodalternatives. Imagine a situation in

which your existing alternatives are ring one-third o your employees,

reducing everyones pay, or scaling back services. Even though these are

alternatives, they arent good, so it eels like there arent any alternatives.Te challenge in a situation like this is one o imagination and creativity.

It takes a lot o mental eort to think o alternatives that perorm well

on the decision objectives. Te challenge is compounded when there isnt

sucient time or brainstorming additional alternatives.

IntangiblesSome decisions are challenging because they involve

dicult-to-measure intangibles such as consumer satisaction, employee

morale, and product quality. Incorporating intangibles like these intoa decision can be dicult because it takes additional time and mental

eort to decide how to quantiy these actors. As will be discussed in

urther detail in the next chapter, to be meaningul, the denitions and

measurement scales used to quantiy these actors should be unambiguous,

comprehensive, direct, operational, and understandable.6

Long time horizonsFor many decisions, the eects o the decisions

occur over a long time period rather than immediately. Research and

development and policy decisions oten have this characteristicthe

R&D project or policy decision must be made today, but the decisions

consequences will not be known or months, years, or even decades.

Because o this lag between the decision and its eects, it can be very

dicult to understand, or even recognize, the underlying associations or

causal mechanisms between the alternatives and their consequences. Tis

makes it very dicult to predict the uture implications o the alternatives,and it also impacts the decision makers ability to learn rom experience.

Sequential nature o decisionsIn many cases, todays decisions aect

tomorrows decisions by altering the set o alternatives available in the uture


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and by altering the attractiveness o those alternatives. For example, install-

ing a new inormation technology system throughout the organization

means that certain projects will be possible and others will not be possible.

Furthermore, the new system may make certain projects more easible or

attractive relative to other projects. Sequential decisions are challenging

because it can be dicult to analyze the impact o todays decision on

uture opportunities and decisions and incorporate those eects into the

analysis o todays decision beore having to make the decision.

Interdisciplinary substanceMany important business decisions require

inormation rom several areas o expertise. For example, launching a newproduct line may require inormation about legal matters, inormation

technology, operations, marketing, nance, and sales. Many executives

are not qualied in all o these areas, even though they are tasked with

making decisions based on inormation rom these areas; thereore, exec-

utives receive inormation rom proessionals who have expertise in each

o these areas. In these situations, the interdisciplinary substance o the

decisions is challenging because it aects question raming, inormationexchange, interpretation, coordination, and understanding.

Uncertainty and riskUncertainty about the uture is oten one o

the greatest challenges to decision making. Should I expand my product

line? Will the demand be there? What are the chances o demand alling?

Much o the doubt, ear, and anxiety people eel when making impor-

tant decisions stems rom not being able to precisely predict the uture

consequences o the decisions. While it is usually impossible to preciselypredict the uture, oten there is inormation that can be used to quantiy

the uncertainty in the decision. Tis inormation may come rom his-

torical data, expert judgments, or even personal judgments. Quantiying

uncertainty enables decision makers to communicate their judgments

about it clearly and to incorporate the uncertainty systematically into

their decision-making process.7

Attitude toward riskAlternatives requently have dierent levels o

risk, the likelihood o loss i the uture turns out unavorably. Generally,

low-risk alternatives are associated with lower returns; whereas high-risk

alternatives are associated with higher returns. For example, investing


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in a U.S. reasury bond carries one level o risk and return and invest-

ing in a corporate bond carries another, higher level o risk and return.

People dier in their tolerance or risk, so dierent alternatives appeal to

dierent people. Te challenge is in acknowledging and incorporating

that sentiment into the decision-making process to ensure the decision is

consistent with the decision makers risk preerences.8

Value trade-ofsMany important decisions involve multiple objectives,

and part o the challenge o making decisions with multiple objectives

is in expressing the value trade-os among the objectives. Tese value

trade-os indicate which objectives are relatively more important to thedecision maker. For example, imagine a decision in which two objectives

are decrease production costs and increase product quality. Does the

decision maker preer these objectives equally, or is one objective relatively

more important than the other? How much more important is it? Depend-

ing on the circumstances, it can be quite challenging to recognize and think

deeply about these preerences.9 At the same time, incorporating them into

the decision-making process is tremendously valuable because it ensures

the decision is based on, and consistent with, the decision makers values.

Multiple decision makersOten a group o people, not just an

individual, is responsible or making a particular decision. In these

situations, it is requently necessary to reach a consensus, or at least

a majority, opinion in order to make the decision. Tese situations

are complicated because people have dierent types o training and

background, levels o understanding, motivations, and opinions. Evenwhen a great deal o actual inormation is available, important decisions

usually involve values, judgments, and trade-os that cant be resolved with

additional actual inormation. In these circumstances, it is important to

be explicit and clear about all o the acts, values, judgments, and trade-

os being used so that everyone has the inormation and understanding

needed to make a well-inormed decision.10

What Is Multiobjective Decision Analysis?

In general, multiobjective decision analysis (MODA) is a structured

approach to making inormed decisions. More specically, it is a philosophy,


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methodology, and collection o systematic procedures or evaluating decision

alternatives in the ace o multiple, conficting objectives and uncertainty.11

According to MODA, a decision maker should choose the best

alternative based on an evaluation o two actors: (1) the likelihood o the

possible consequences or each alternative and (2) the decision makers

preerences or the possible consequences or each alternative.12

As a methodology, the key term in the phrase MODA is the word analysis,

which reers to decomposing something into separable components. In this

case, it reers to breaking down the complex decision problem into a set o

smaller, and hopeully, more manageable problems. Tese smaller problems

involve the assessment o the decision makers objectives, alternatives, and

preerences, as well as his or her judgments about relevant uncertainties.

Ater the decision maker has assessed each o these smaller problems sepa-

rately, MODA provides a ormal mechanism that the decision maker can

use to combine all o the inormation to identiy the preerred alternative.13

Te procedures o a MODA will be outlined later in this chapter, and will be

described in greater detail in the remaining chapters o this book.

One o the distinctive eatures o this methodology is that it separatesthe analysis o uncertainty rom the analysis o preerences (i.e., values or

utilities). Analysis o uncertainty reers to an assessment o the likelihood

o the potential consequences; whereas analysis o preerences reers to

an assessment o the attractivenesso the potential consequences to the

decision maker.

Uncertainty analysis relies on probability theory, subjective

probability judgments, as well as historical and experimental data toassess the likelihood o experiencing the various uncertain consequences

or outcomes.14 Conducting an uncertainty analysis is signicantly bet-

ter than using vague terms like airly sure and pretty condent to

describe the degree o uncertainty, because those terms mean dierent

things to dierent people. By clariying and making explicit the degree

o uncertainty, an uncertainty analysis ensures everyone uses the same

denition o uncertainty and understands the degree o uncertainty beingexpressed. An uncertainty analysis also improves upon the use o simple

summary measures, such as the mean, because it enables the decision

maker to see and understand the whole distribution and likelihood o

potential consequences.


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Preerence (i.e., value or utility) analysis relies on a decision makers

unique set o values and preerences to assess how attractive the various

consequences or outcomes are to the decision maker.15 Tis analysis has

two components. Te rst component consists o speciying a preerence

ordering over the range o outcomes or a single evaluation criterion. For

example, i one criterion is revenue, measured in dollars, and the potential

outcomes or revenue range rom $100 million to $200 million, then the

decision maker must speciy a value unction that indicates the value o

any dollar amount in this range to the decision maker.

Te second component consists o speciying a preerence ordering

over all o the evaluation measures; that is, making trade-os among the

measures. For example, i one evaluation measure is cost and another

measure is time to completion, then the decision maker must think about

whether cost is more, equally, or less preerred to time to completion,

given their ranges, and be able to speciy the degree o that preerence.

Terminology

Up to this point, many terms have been used to describe the components

o decisions and MODA. Some o the terms that have been used are

values, objectives, evaluation measures, and goals.

Tere are no universal denitions o the terms values, objectives,

evaluation measures, or goals. For example, some authors have reerred to

a specic concept as an objective while others have reerred to the same

concept as a goal.16 For clarity o communication, this section describeshow these terms are used in this book. For example, in this book, the

words objective and goal reer to two dierent concepts. Te denitions

used in this book are consistent with those presented by Keeney and

Raia11 and Kirkwood19:

Values are the areas o concern, considerations, or matters the

decision maker thinks are important enough to be taken intoaccount when evaluating alternatives. For example, values or a

company considering alternative ways o rolling out an initiative

may be ease o implementation, company image, and prot.


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Objectives augment values by speciying the preerred direction

o movement. Tus, a company considering alternative ways

o rolling out an initiative would nd an alternative that is

easierto implement moredesirable. Similarly, an initiative that

increases company image or increases prot is more desirable.

Evaluation measures (a.k.a. criteria or attributes) are metrics

or scales or quantiying an objective and assessing the extent

to which it is achieved. For example, a company may use

annual prot in dollars as the evaluation measure or the

objective increase prot.

Goals are thresholds or evaluation measures that alternatives

either do or do not achieve. For example, a company might

have a goal o implementing an initiative within eight

weeks. For a given alternative, this goal may or may not be

achievable.

Figure 1.1 shows a diagram o the MODA process. Te components

o the process are described in greater detail as ollows.

1. Understand the decision contextTe rst step, or component, in

the iterative process is to understand, or rame, the decision context.

Understanding and raming the decision context includes identiying

the decision maker(s); gathering dierent perspectives on the decision

situation; assessing initial reerence points, opinions, and assumptions;

and determining the time rame or making the decision.17

2. Identiy the objectivesTe second step involves identiying and

structuring the values and objectives the decision maker intends

to use to assess the alternatives, as well as the associated evalua-

tion measures. Tis includes brainstorming relevant objectives,

separating undamental rom means objectives, and developing

evaluation measures to quantiy the objectives.18 Because o the need

to be creative and think deeply about the decision and the relevantobjectives and evaluation measures, this step is oten one o the most

challenging. When done well, it is also one o the most valuable steps

in the decision-making process.


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3. Model preerencesTe third step involves modeling the decision

makers preerences over the evaluation measures. Tis includes

speciying value or utility unctions or each o the evaluation

measures (value unctions i there is no uncertainty; utility unctionsi the decision involves uncertainty) and expressing preerence

weights or each o the evaluation measures.19

4. Identiy the alternativesTe ourth step involves identiying rel-

evant alternatives, given the particular decision context. In practice,

Understand decision context

Identify alternatives

Model preferences

Score alternatives

Conduct sensitivity analyses

Does anycomponent need

to be refined?

Choose best alternative

No

Yes

Identify objectives

Learn from experience

Model uncertainty

Identify best alternative

Assess results

Figure 1.1. The multiobjective decision analysis (MODA) process.


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identiying the evaluation measures and the alternatives is an iterative

process; however, identiying alternatives is listed as the ourth step

in the process, rather than being included in the second step, to

emphasize that the decision makers ocus should be on speciying

values and objectives. Only by understanding ones objectives can

one hope to act in a directed ashion to achieve them.

5. Score the alternativesTe th step is to score the alternatives. Tis

involves assessing the consequences o each alternative with respect to

each o the objectives and assigning a score to each alternativeevaluation

measure pair that refects the degree to which the consequences o each

alternative achieve each o the associated objectives.

6. Model uncertaintyTe sixth step involves modeling uncertainty. I

any o an alternatives consequences are uncertain, then it is important

to assess the probabilities associated with the uncertain consequences.

A ew sources o probabilities include historical data, data rom analo-

gous situations, simulations and other stochastic analyses, and expert

judgments.20 By enabling a decision maker to quantiy the uncer-

tainty in the decision, this step o the MODA process also adds sig-nicant value over and above inormal decision-making processes.

7. Conduct sensitivity analysesAter the proceeding steps deter-

mine the initially preerred alternative, the seventh step involves

conducting sensitivity analyses to test the robustness o the preerred

alternative to the models inputs. By changing many o the models

inputs, including the value unctions over the evaluation measures,

the preerence weights associated with the evaluation measures, theprobabilities associated with the uncertain consequences, and even

the scores given or each alternativeevaluation measure pair, the

decision maker can quickly assess the sensitivity o the preerred

alternative to the models inputs.21

8. Choose the best alternativeTe eighth step is simply selecting the

alternative with the highest overall weighted value or utility. By selecting

the alternative with the highest overall weighted value or utility, the deci-sion maker maximizes the value o the decision, given the uncertainties

involved and the decision makers preerences or the dierent objectives.

9.Assess results and learn rom experienceTe nal two steps are

assessing the results o the decision and learning rom experience. Tese


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steps involve reviewing the analysis that led to the nal decision and

action(s) to understand the reasons or selecting a particular course

o action. Once the outcomes, or consequences, o interest have been

realized, it is possible to evaluate the relationship between the analy-

sis, the decision, and the results to draw lessons rom the experience

that can be applied to uture decisions.

Te preceding section has presented the MODA process as a relatively

linear process with a series o steps; however, it is important to

remember that the process includes several eedback loops and is actu-

ally very iterative.22 For example, this book emphasizes speciying andstructuring the objectives beore considering the alternatives, but it is

entirely possible to identiy alternatives rst or to do both in parallel.

In practice, it is common to move among the steps relatively fu-

idly and iteratively as new ideas emerge, decision makers rene aspects

o the decision, and additional inormation is collected. While subse-

quent chapters discuss the steps o the process in turn, keep in mind

that the MODA methodology is actually an iterative process.

Prescriptive, not Descriptive

Te process described earlier appears to be common sense; however, ew

people ollow the process systematically, even or important decisions.

In this sense, the process is not descriptive, because it does not describe

how people usually make dicult decisions under uncertainty. Te pro-cess is also not normative, because it is not an idealized theory about

how super-rational beings with unbounded memory and intellect should

make decisions under uncertainty. Instead, the process is prescriptive,

because it sets orth an approach that anyone can use to think deeply

and systematically about real, complex problems in an uncertain world.23

In 1772, Joseph Priestly aced an important decision and asked

Benjamin Franklin or advice. Rather than recommend whathe shoulddo, Benjamin Franklin advised Joseph Priestly on howhe should go about

making the decision.24 As you read Benjamin Franklins letter, think about

the similarities between his approach and the one discussed in this book,

including listing evaluation measures, weighing them, and deciding based

on the balance o the analysis.


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o Joseph Priestley

London, September 19, 1772

Dear Sir,

In the aair o so much importance to you, wherein you ask my

advice, I cannot or want o sucient premises, advise you what

to determine, but i you please I will tell you how.

When these dicult cases occur, they are dicult chiefy

because while we have them under consideration all the reasons

pro and con are not present to the mind at the same time; butsometimes one set present themselves, and at other times another,

the rst being out o sight. Hence the various purposes or inclina-

tions that alternately prevail, and the uncertainty that perplexes us.

o get over this, my way is, to divide hal a sheet o paper by

a line into two columns, writing over the one pro, and over the

other con. Ten during three or our days consideration I put down

under the dierent heads short hints o the dierent motives that atdierent times occur to me or or against the measure. When I have

thus got them all together in one view, I endeavour to estimate their

respective weights; and where I nd two, one on each side, that

seem equal, I strike them both out: I I nd a reason pro equal to

some two reasons con, I strike out the three. I I judge some two rea-

sons con equal to some three reasons pro, I strike out the ve; and

thus proceeding I nd at length where the balance lies; and i ater

a day or two o urther consideration nothing new that is o impor-

tance occurs on either side, I come to a determination accordingly.

And tho the weight o reasons cannot be taken with the

precision o algebraic quantities, yet when each is thus considered

separately and comparatively, and the whole lies beore me, I think

I can judge better, and am less likely to take a rash step; and in act

I have ound great advantage rom this kind o equation, in what

may be called moral or prudential algebra.

Wishing sincerely that you may determine or the best, I am

ever, my dear riend,

Yours most aectionately

B. Franklin


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As Ben Franklins decision-making process illustrates, decision makers

can adjust the level o detail with which they analyze a decision problem

based on the complexity and importance o the decision. At the same

time, its important to remember that or complex, consequential deci-

sions, ollowing a structured decision-making process systematically can

result in better decisions, communication, and implementation, and,

ultimately, better results.

Why Is Multiobjective Decision Analysis Important?

Promotes clear thinkingOne o the advantages o using MODA to

make decisions is that it promotes clear thinking. Without this meth-

odology, the thought process can remain muddledobjectives entangled

with alternatives entangled with uncertainties entangled with values. By

decomposing decisions into these separate components and addressing

them individually, decision makers gain a greater understanding o the

decision situation and can use their time more eectively to identiy the

best course o action.25

Increases comprehension and insightsIn act, the methodology

can lead to comprehension and insights that wouldnt be apparent rom

an intuitive, gut reaction decision-making process. For example, the

brainstorming and creativity the methodology requires can result in the

generation o better evaluation measures and alternatives.26 Moreover,

the methodology may show that there is a dierence between the alterna-

tive the decision maker intuitively preers and the alternative that should

be preerred based on the analysis. When this is the case, the decision

maker can explore the reasons or the discrepancy to achieve an even

better grasp o the decision situation and reasons or preerring one

alternative to the others.

Explains decision rationaleIn addition, because the methodology

orces a decision maker to be clear and explicit about the data and

judgments used to make a decision, it is possible to reer back to the

analysis and model inputs to understand why the decision maker chose a

particular course o action.27 Te act that this methodology is traceable

means that it can be used to support and deend the rationale used to make


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a decision. Tis can be valuable when the decision needs to be justied to

others, including bosses, co-workers, regulators, and the general public.

Facilitates communication and understandingAnother advantage oMODA is that it acilitates communication and understanding among

multiple stakeholders.28 Even i there is initial disagreement among the

stakeholders, this methodology can be used to elucidate each persons

position so everyone gains a greater understanding o the issues involved

and the reasons or the confict. It may even show that, despite dierent

stances on an issue, the issue is not worth debating because it does not

aect which alternative should be chosen.

Enhances commitment to actionFinally, one additional advantage o

MODA is that, by enabling multiple stakeholders to participate in the

decision-making process, it helps them develop a shared understanding

o the decision situation. When there are initial disagreements, a shared

understanding o a situation usually decreases the dierences o opinion

among the people addressing the problem, or at least the degree or strength

o their views.29 Tese characteristics o the methodology, encouraginginvolvement in the process and promoting a shared understanding o the

decision, increase the likelihood that a group will be committed to the

preerred course o action.

The Focus of This Book

Tis book is about making multiobjective decisions under uncertainty.It describes how to structure and solve these types o problems using

spreadsheets. Te techniques and examples used in this book cover the

use o multiple evaluation measures, discrete and continuous value and

utility unctions, preerence weights, and expected value and utility

calculations.

Given the ocus o this book, there is little emphasis on the development

o decision trees to represent decision situations; however, this structure

still underlies these decisions. For more inormation about decision trees,

please seeMaking Hard Decisions: An Introduction to Decision Analysisby

Robert Clemen or Decision Analysis for Management Judgment by Paul

Goodwin and George Wright.


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How to Read This Book

Tis book explains how to conduct a MODA under uncertainty

using spreadsheets. Chapter 2 presents procedures or developing andstructuring values, objectives, and evaluation measures. Te chapter also

discusses how to identiy alternatives and deal with situations in which

there are too many or too ew alternatives.

Chapter 3 presents procedures or speciying value unctions over each

o the evaluation measures. Piecewise linear and exponential unctions

are shown to handle discrete and continuous evaluation measures,

respectively. Te chapter also presents procedures or articulating thepreerence weights associated with evaluation measures.

Chapter 4 introduces the use o probability to quantiy uncer-

tainty, explains how to determine discrete and continuous probability

distributions, and shows how to use those distributions to calculate

expected values.

Chapter 5 introduces the concept o risk tolerance, explains how to

determine utility unctions, and shows how to use certainty equivalentsto identiy the preerred alternative.

Chapter 6 synthesizes all o the material explored in the preceding

chapters by demonstrating how to conduct a multiobjective decision

analysis under uncertainty using a spreadsheet.

Finally, Chapter 7 concludes the book by presenting extensions to the

methodology developed in this book and oering reerences to additional

resources on those topics.

Key Points

Proessionals need a systematic methodology and set o tools

they can use to make and justiy their decisions because

complexity and uncertainty make it incredibly dicult to

make important decisions inormally in a deensible manner.

Multiobjective decision analysis is a methodology and

collection o systematic procedures or evaluating decisions

in the ace o multiple, conficting objectives and uncertainty.

Te methodology involves breaking down a complex decision


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problem into a set o smaller, and hopeully, more manageable

problems. Ater the decision maker has assessed each o these

smaller problems separately, multiobjective decision analysis

provides a ormal mechanism the decision maker can use

to combine all o the inormation to identiy the preerred

alternative.

Multiobjective decision analysis promotes clear thinking,

leads to comprehension and insights that wouldnt be

apparent rom an intuitive, gut reaction decision-making

process, creates an audit trail that can be used to support

and deend the rationale used to make a decision, acilitates

communication and understanding among multiple

stakeholders, and increases the likelihood that a group will be

committed to the preerred course o action by promoting a

shared understanding o the decision situation.


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29/32

Index

AAlternatives, 70, 129

certainty equivalents and, 102108determining overall values or,

6371developing good, 3844ully eatured product, developing, 70

good, 4hal-eatured product, developing, 70identiying, with creative thinking,

3941strategy generation tables, 4041value-ocused thinking, 3940

innite number o, 133136low-eatured product, developing, 70reducing number o, 4142status quo, maintaining, 70

techniques or identiying, 39under uncertainty, developing,

4244

CCertainty equivalents

and alternatives, 102108calculating, 102118, 121130relationship between expected

utility and, 102and spreadsheet, 102, 104,

124123Certainty equivalent value, 121123,

130, 138139Challenges, o decision making

attitude toward risk, 56good alternatives, 4intangibles, 4interdisciplinary substance, 5

long time horizons, 4multiple decision makers, 6multiple objectives, 34sequential nature o decisions, 45uncertainty and risk, 5value trade-os, 6

Common business decisions, 84, 140Company image (CI), 5356

and increment, 5356Constructed evaluation measures,

3135developing, 3235

picture scale, 34

proxy evaluation measures, 3435scale with dened levels, 3233

weighted scale, 33or perception o company image,

3132Continuous probability, 8592Cumulative distribution unction

(CDF), 8889

D

Decision raming, 1920Decision making

challenges o, 36Discrete Probability, 7885

EEliciting probability, 7882Evaluation measures, 9, 3038,

6769, 123, 128. See alsoUtility unction

constructed, 3135cost, second, 67customer service, third, 67decreasing preerence, 4951desirable properties o, 3538

measurable, 3536operational, 3637understandable, 3738natural, 3031

preerence weights, 5153proxy, 3435single-dimensional value

unctions, 4651importance o swinging, 62, 63increasing preerence, 4951, 58


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158 INDEx

natural, 3031prot, rst, 67single-dimensional value unctions, 62

time-to-manuacture, nal, 6869Expected utility, 85, 9697, 99,102103, 116, 117, 119

Expected value, 115116and alternative, 104105alternative way to calculate, 91calculating, 8385and certainty equivalent, 122o manuacturing costs, 92probability distributions and,

7393and uncertainties,137138Exponential constant, 5761Exponential value unctions, 5051

determining, 5662Exponential utility unctions, 97103Extended Pearsonukey

Approximation, 85, 9092, 104

F

Framing, decision, 1920

GGoals, 9Good alternatives, 3844

HHedging, 43

I

Identiying objectives, 2125considering problems and

shortcomings, 22determining generic objectives,

2425determining strategic objectives, 25developing wish list, 21identiying alternatives, 21identiying goals, constraints, and

guidelines, 23predicting consequences, 2223

Incrementand company image (CI), 5356

Insuring, 44Intangibles, 4

Interdisciplinary substance, 5Interdependent uncertainties,

136139

LLong time horizons, 4

MManuacturing costs, 8692Multiple objectives, 34

and uncertainity, 111116Multiobjective decision analysis

(MODA)

dened, 67extension to, 133143importance o, 1415process o, 912

Multiple decision makers, 6

NNatural evaluation measures, 3031Net prot, 103110, 136138, 141Net present value (NPV), 141

Normalized exponential constants(nec), 59, 60

Normalized mid-value (nmv), 59, 60

OObjectives, 6, 9, 27, 128

brainstorming, 2025structuring, 2530

separating undamental rommeans objectives, 2627

stopping the structuring process,2930

structuring undamentalobjectives hierarchies, 2729

techniques or identiying, 2125considering problems and

shortcomings, 22determining generic objectives,

2425determining strategic objectives, 25developing wish list, 21identiying alternatives, 21identiying goals, constraints,

and guidelines, 23predicting consequences, 2223


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INDEx 159

Overall certainty equivalent values, 130Overall Weighted Values, 7071

PPearsonukey Approximation,extended, 85, 9092, 104

Picture scale, 34Piecewise linear single-dimensional

value unctions, 4950determining, 5356

Portolio Decision Analysis, 135Power-additive utility unction,

116118

Preerence analysis, 78Preerence weights, 5153, 69,128129

determining, 6163Present value (PV) ormula, 140Probabilistic independence, 121Probability, 129

anchoring and adjustment, 7778assuming certainty, 76availability, 7677

continuous, determining, 8592determining discrete, 7885elicit, preparing to, 7980eliciting, 8082expected value, 8285misunderstanding, 75relying on heuristics, 76representativeness, 77verbal descriptions, using, 7576veriying, 8285

Product manager, 8690, 96Property o expected values, 122, 138Proxy evaluation measures, 3435

RRisk

averse, 9599, 101, 105, 108, 112,114, 116, 118, 122, 138

aversion, 9597, 100, 107108,111, 134135, 138

attitude toward, 56neutral, 9599, 105, 116, 118, 122seeking, 9698, 118tolerence, 98102uncertainty and, 5

Risk aversion, 9597, 100, 107108,111, 134135, 142

Risk-averse decision makers,

9698, 118Risk-neutral decision makers, 97, 98,105, 114

Risk-seeking decision makers,9698, 114

Risk sharing, 43Risk tolerence, 98102. See also

Utility unctiondecision makers, 98101determining multiobjective,

118121determining, 98102Risky alternative, 95, 9799,

103104, 108, 114118

SScale, with dened levels, 3233Scores. SeeLevelsScreening criteria,

4142

Sequencing, 43Sequential nature o decisions, 45Single-dimensional value unctions,

4651, 6162, 70, 111,117, 128

exponential, 5051piecewise linear, 4950

Spreadsheet, 123123and certainty equivalent,

122124

layoutmultiobjective decisionanalysis (no uncertainty), 64,114, 120

Strategic Decision Making, 136Strategy generation table,

4041Structuring objectives, 2530

separating undamental rom meansobjectives, 2627

stopping the structuring process,2930

structuring undamental objectiveshierarchies, 2729

Swinging evaluation measure,62, 63


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160 INDEx

UUncertainty, 4244

hedging, 43

insuring, 44and risk, 5risk sharing, 43sequencing, 43

Uncertainty analysis, 7Utility

expected, 85, 9697, 99, 102103,116, 117, 119

unction, 9798Utility unction

determining, 9798exponential, 97103exponential multiobjective, 117118power-additive, 116118

VValue-ocused thinking,

3940

Value unctions, 6769,123, 128Values, 8

brainstorming,2025

Value trade-os, 6Veriying probability, 8285

WWeak Law o Large Numbers, 83

Weighted scale, 33Weighted single-dimensional certaintyequivalents, 129130

Weighted single-dimensional values, 70

Documents

Brownley Chap One