Portfolio Decision Analysis: Improved Methods for Resource Allocation

426 Pages·2011·5.45 MB·English

by  Ahti Salo

International Series in Operations Research
& Management Science
Volume 162
SeriesEditor
FrederickS.Hillier
StanfordUniversity,CA,USA
SpecialEditorialConsultant
CamilleC.Price
StephenF.AustinStateUniversity,TX,USA
Forfurthervolumes:
http://www.springer.com/series/6161

Ahti Salo (cid:2) Jeffrey Keisler (cid:2) Alec Morton
Editors
Portfolio Decision Analysis
Improved Methods for Resource Allocation
123
Editors
AhtiSalo JeffreyKeisler
AaltoUniversitySchoolofScience DepartmentofManagementScience
SystemsAnalysisLaboratory andInformationSystems
POBox11100 UniversityofMassachusetts,Boston
00076Aalto MorrisseyBoulevard100
Finland Boston,MA02125
ahti.salo@tkk.fi USA
[email protected]
AlecMorton
DepartmentofManagement
LondonSchoolofEconomics
HoughtonStreet
WC2A2AELondon
UnitedKingdom
[email protected]
ISSN0884-8289
ISBN978-1-4419-9942-9 e-ISBN978-1-4419-9943-6
DOI10.1007/978-1-4419-9943-6
SpringerNewYorkDordrechtHeidelbergLondon
LibraryofCongressControlNumber:2011932226
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Foreword
Resource allocation problems are ubiquitous in business and government orga-
nizations. Stated quite simply, we typically have more good ideas for projects
and programs than funds, capacity, or time to pursue them. These projects and
programsrequiresignificantinitialinvestmentsinthepresent,withtheanticipation
of future benefits. This necessitates balancing the promised return on investment
against the risk that the benefits do not materialize. An added complication is
thatorganizationsoftenhavecomplexandpoorlyarticulatedobjectivesandlacka
consistentmethodologyfordetermininghowwellalternativeinvestmentsmeasure
upagainstthoseobjectives.
The field of decision analysis (or DA) has recognized the ubiquity of resource
allocation problems for three or four decades. The uncertainty of future benefits
clearly calls for application of standard approaches for modelling uncertainties –
such as decision trees, influence diagrams, and Monte Carlo simulation, and for
using utility functionsto modeldecision makers’ risk preference.The problemof
manyobjectivesisanopportunitytoapplymulti-attributeutilityorvaluemodels–
or similar techniques – to decompose a difficult multidimensional problem into
a series of relatively simple judgments about one-dimensional preferences and
relativetrade-offweights.Applicationof thesemodelsinreal-worldorganizations
alsorequiresstate-of-the-arttechniquesandprocessesforelicitingprobabilitiesand
preferences, performing sensitivity analyses, and presenting clear and compelling
resultsandrecommendations.Assuch,theseproblemsclearlyfallwithinthemain-
streamofDAmethodsandpractice.
However,incontrasttotraditionalDA,itisimportanttorecognizethatresource
allocationis a portfolioproblem,where the decisionmakersmustchoose the best
subset of possible projects or investments subject to resource constraints. Several
other disciplines have also focused on the portfolioaspects of resource allocation
problems, particularly operations research (OR) and finance. OR techniques such
as mathematical programming are directly relevant to finding optimal portfolios
of projectssubjectto resourceconstraints. However,mathematicaloptimizationis
not typically considered part of routine DA practice, and combining the best of
v
vi Foreword
decisionanalyticmodelswithmathematicalprogramminghasreceivedonlylimited
attention. DA practitioners commonly solve portfolio problems with heuristic
approaches,suchasprioritizationbasedonbenefit–costratios,despitefoundational
workdonebyDonaldKeefer,CraigKirkwood,andtheircolleaguesinthe1970s.In
finance,theconsiderablebodyofworkonapplicationsofORtofinancialportfolios
is relevant to understanding trade-offs of risk and return. However, finance and
DA use different approaches and make different assumptions for assessing and
modellingriskpreferences.
PortfolioDA also posessignificantpracticalchallenges.In largeorganizations,
hundredsor thousands of projects and programsmay be considered for inclusion
in a portfolio.Assessing multipleevaluationcriteriaacrosshundredsor thousands
of projects can be daunting. If probability assessments are required, then good
DA practice typically emphasizes careful and rigorous assessment and modelling
of the uncertainties. This amounts to building hundreds of models and assessing
probability distributions for many thousands of uncertain parameters. In my own
experience working on resource allocation in hospitals, healthcare systems, and
largemanufacturingfirms,theseorganizationssometimesstruggletodevelopeven
straightforwarddeterministicfinancialmodelsfortheirprojects.Conductingafull-
blown decision analysis for each project is simply infeasible. Stated quite simply,
themostcriticalscarceresourcesaretheresourcesrequiredtoobtainandevaluate
data, assess probabilities, construct models, and evaluate alternative portfolios of
projects.Decisionmakerstypicallystartwithlimitedinformationabouteachproject
andportfolio,andneedguidanceaboutwhichprojectstoanalyze,andatwhatlevel
ofdetail.
Given the ubiquity of portfolio problems, the rich intersection of multiple
relevant disciplines, and the very real practical challenges associated with these
problems,onewouldnaturallyexpecttoseedecisionanalystsrisetothechallenge.
The applied side of the decision analysis profession appears to have responded –
particularly in oil and gas production,pharmaceuticals, and military applications.
Anumberofvendorshavealsorespondedbyprovidingcommercialsoftwaretools
specificallydesignedtosupportportfolioDA.
Despite the apparent success of portfolio DA in practice, there has been
surprisingly limited attention to portfolio theory and methods in academic DA.
With only a few notable exceptions, introductoryDA textbookseither provide no
discussion of portfolio methodsand problems, or at best, a cursory overviewof a
few applications. Nor has the theory and methods of portfolio DA received more
than limited attention in the research literature. In recent years, in part to address
thisrelativeneglect,AhtiSalo,JeffreyKeisler,andAlecMortonorganizedaseries
ofinvitedsessionsatvariousnationalandinternationalconferences.Thesesessions
brought together researchers and practitioners with a shared interest in portfolio
decision analysis. I believe these sessions succeeded in encouraging academic-
practitionercommunication,and perhapshave persuadedmore academic decision
analyststofocustheirattentionofportfolioproblems,issues,andchallenges.
This volume, Portfolio Decision Analysis: Improved Methods for Resource
Allocation,isthenextlogicalstepinSalo,Keisler,andMorton’seffortstofostera
Foreword vii
richandproductiveinteractionoftheory,method,andapplicationinportfolioDA.I
commendandapplaudtheeditors’objectives,andIampleasedtoseethattheyhave
succeeded in attracting a diverse collection of contributions from a distinguished
set of authors. The editors do an excellent job of providing an overview of these
contributionsin their introductorychapter. I simply concludeby encouragingyou
to take up where these authors leave off, either by furthering the developmentof
newtheoryandmethods,orbyputtingthesetheoriesandmethodstothetestinreal
organizationsstrugglingtomakethebestuseofscarceresources.
StrataDecisionTechnology,L.L.C. DonN.Kleinmuntz,Ph.D.
Champaign,IL,USA ExecutiveVicePresident

Acknowledgments
Thisbookistheresultofthecollectiveeffortsofmanypeople.Chapterauthorshave
contributed in the most directly and tangible way; but there are many others who
deservemention,notleastforhavingconvincedusoftheneedforadedicatedbook
onPortfolioDecisionAnalysisandalsoforhavingprovideduswiththeintellectual
foundationandthefinancialmeanstobringsuchabooktofruition.
In particular, we wish to thank the panelists in the series of panel discussions
that we organized at the INFORMS and EURO conferences in 2008–2010: Jose´
Figueira, Don Kleinmuntz, Jack Kloeber, Jim Matheson, Larry Phillips, Martin
Schilling, Theo Stewart, Christian Stummer, and Alexis Tsoukia`s (in alphabetical
order).Thesepanelistspresentedthrough-provokingideasandengagedprominent
scholars and practitioners like Carlos Bana e Costa, Robert Dyson, Ron Howard,
and Carl Spetzler in lively discussion. We are pleased to note that echoes of
thesediscussions–whichwerethusenabledbyEUROandINFORMSconference
organizers–arereflectedinmanyplacesofthisbook.
The three of us are indebted to our educators who instilled in us a deep and
abidinginterestindecisionanalysisandwhohavementoredusinourprofessional
lives.Inparticular,AhtiSalowishestothankRaimoP.Ha¨ma¨la¨inenforanexcellent
research environment and numerous occasions for fruitful collaboration; Alec
MortonacknowledgesthesupportandguidanceofLarryPhillipsandCarlosBana
e Costa, who introduced him to this area, and thanks them for many long and
insightfuldiscussions. JeffreyKeisler thankshisformeremployersandcolleagues
at General Motors Corp., Argonne National Laboratory and Strategic Decisions
Group for opportunitiesto work on, experimentwith, and inquire about portfolio
applications.Healsothanksthemanyotherdecisionanalysisfriendsandcolleagues
(including a substantial number of the contributors to this book) who have been
engagedinthisdiscussionovertheyears.JeffreyalsothanksElanaElsteinforher
supportduringthedevelopmentofthisbook.
We have received financial support from several sources. The Academy of
FinlandprovidedthegrantwhichallowedAhtiSalotodevoteasubstantialshareof
histimetothedevelopmentofthisbook,whiletheGameChangersprojectmadeit
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