Decison Support System

8/8/2019 Decison Support System

1/148

DecisionSupportSystems

MarekJ.

DruzdzelandRogerR.Flynn

DecisionSystemsLaboratorySchoolof

InformationSciencesandIntelligentSystemsProgram

UniversityofPittsburghPittsburgh,PA

15260

fmarek,[email protected]://www.sis.pitt.edu/dsl

ToappearinEncyclopediaof

LibraryandInformationScience,SecondEdition,AllenKent(ed.),NewYork:MarcelDekker,

Inc.,2002


2/148

1


3/148

Contents

Introduction3

DecisionsandDecisionModeling4TypesofDecisions........................................4HumanJudgmentandDecisionMaking............................4

ModelingDecisions........................................5ComponentsofDecisionModels.................................5

DecisionSupportSystems6

NormativeSystems7NormativeandDescriptiveApproaches.............................7Decision-AnalyticDecisionSupportSystems..........................8Equation-BasedandMixedSystems..............................10

UserInterfacestoDecisionSupportSystems11SupportforModelConstructionandModelAnalysis.....

.

.

.


4/148

.

.

.

.

.

.

.

.

.

.

.

.

.11SupportforReasoningaboutthe

ProblemStructureinAdditiontoNumericalCalculations11SupportforBothChoiceand

OptimizationofDecisionVariables..............12GraphicalInterface........................................12

Summary

12


5/148

2


6/148

Introduction

Makingdecisionsconcerning

complexsystems(e.g.,themanagementoforganizationaloperations,industrialprocesses,orinvestment

portfolios;thecommandandcontrolofmilitaryunits;orthecontrolofnuclear

powerplants)oftenstrainsourcognitivecapabilities.Eventhoughindividualinteractionsamongasystem'svariablesmaybewellunderstood,predictinghowthesystemwillreact

toanexternal


7/148

manipulationsuchasapolicydecisionis

oftendicult.Whatwillbe,forexample,theeectofintroducingthe

thirdshiftonafactoryoor?Onemightexpectthatthiswillincrease

theplant'soutputbyroughly50percent.Factorssuchasadditionalwages,machineweardown,maintenancebreaks,rawmaterialusage,supplylogistics,andfuturedemandneed

alsobeconsidered,


8/148

however,astheyallwillimpactthe

total


9/148

nancialoutcomeofthisdecision.Manyvariables

areinvolvedincomplexandoftensubtleinterdependenciesandpredictingthetotal

outcomemaybedaunting.

Thereisasubstantialamountofempirical

evidencethathumanintuitivejudgmentanddecisionmakingcanbefarfromoptimal,anditdeterioratesevenfurtherwithcomplexityandstress.Becausein

manysituationsthe


10/148

qualityofdecisionsisimportant,aidingthe

de


11/148

cienciesofhumanjudgmentanddecisionmaking

hasbeenamajorfocusofsciencethroughouthistory.Disciplinessuchas

statistics,economics,andoperationsresearchdevelopedvariousmethodsformakingrationalchoices.More

recently,thesemethods,oftenenhancedbyavarietyoftechniquesoriginatingfrominformationscience,cognitivepsychology,andarti


12/148

cialintelligence,havebeenimplementedinthe

formofcomputerprograms,eitherasstand-alonetoolsorasintegratedcomputing

environmentsforcomplexdecisionmaking.Suchenvironmentsareoftengiventhecommonname

ofdecisionsupportsystems(DSSs).TheconceptofDSSisextremelybroad,anditsde


13/148

nitionsvary,dependingontheauthor'spoint

ofview.Toavoidexclusionofanyoftheexistingtypesof

DSSs,wewillde


14/148

nethemroughlyasinteractivecomputer-basedsystems

thataidusersinjudgmentandchoiceactivities.Anothernamesometimes

usedasasynonymforDSSisknowledge-basedsystems,whichreferstotheir

attempttoformalizedomainknowledgesothatitisamenabletomechanizedreasoning.

Decisionsupportsystemsaregaininganincreasedpopularityinvarious

domains,includingbusin


15/148

ess,engineering,themilitary,andmedicine.They

areespeciallyvaluableinsituationsinwhichtheamountofavailableinformation

isprohibitivefortheintuitionofanunaidedhumandecisionmakerandin

whichprecisionandoptimalityareofimportance.Decisionsupportsystemscanaidhumancognitivede


16/148

cienciesbyintegratingvarioussourcesofinformation,

providingintelligentaccesstorelevantknowledge,andaidingtheprocessofstructuring

decisions.Theycanalsosupportchoiceamongwell-de


17/148

nedalternativesandbuildonformalapproaches,

suchasthemethodsofengineeringeconomics,operationsresearch,statistics,anddecision

theory.Theycanalsoemployarti


18/148

cialintelligencemethodstoaddressheuristically

problemsthatareintractablebyformaltechniques.Properapplicationofdecision-makingtools

increasesproductivity,eciency,andeectivenessandgivesmanybusinessesacomparativeadvantageover

theircompetitors,allowingthemtomakeoptimalchoicesfortechnologicalprocessesandtheirparameters,planningbusinessoperations,logistics,orinvestments.

Whileitis

diculttooverestimate


19/148


20/148

esbestalternatives.Wewillbrieydiscuss

thecharacteristicsofdecisionproblemsandhowdecisionmakingcanbesupported

bycomputerprograms.WethencovervariouscomponentsofDSSsandtherole

thattheyplayindecisionsupport.Wewillalsointroduceanemergentclassofnormativesystems(i.e.,DSSsbasedonsoundtheoreticalprinciples),andin

particular,decision-analytic


21/148

DSSs.Finally,wewillreviewissuesrelated

touserinterfacestoDSSsandstresstheimportanceofuserinterfaces

totheultimatequalityofdecisionsaidedbycomputerprograms.

3


22/148

DecisionsandDecisionModeling

TypesofDecisions

Asimpleviewofdecisionmakingis

thatitisaproblemofchoiceamongseveralalternatives.Asomewhatmore

sophisticatedviewincludestheprocessofconstructingthealternatives(i.e.,givenaproblemstatement,developingalistofchoiceoptions).Acompletepictureincludesa

searchforopportunities


23/148


24/148

nedproblemforwhichshedesignscreative

decisionoptions(e.g.,howtomarketanewproductsothatthe

pro


25/148

tsaremaximized).Finally,shemaywork

inalessreactivefashionandviewdecisionproblemsasopportunitiesthat

havetobediscoveredbystudyingtheoperationsofhercompanyandits

surroundingenvironment(e.g.,howcanshemaketheproductionprocessmoreecient).Thereismuchanecdotalandsomeempiricalevidencethatstructuringdecisionproblemsand

identifyingcreativedecision


26/148

alternativesdeterminetheultimatequalityofdecisions.

Decisionsupportsystemsaimmainlyatthisbroadesttypeofdecisionmaking,

andinadditiontosupportingchoice,theyaidinmodelingandanalyzingsystems

(suchascomplexorganizations),identifyingdecisionopportunities,andstructuringdecisionproblems.

HumanJudgmentandDecisionMaking

Theoreticalstudiesonrationaldecision

making,notablythat


27/148

inthecontextofprobabilitytheoryand

decisiontheory,havebeenaccompaniedbyempiricalresearchonwhetherhumanbehavior

complieswiththetheory.Ithasbeenratherconvincinglydemonstratedinnumerousempirical

studiesthathumanjudgmentanddecisionmakingisbasedonintuitivestrategiesasopposedtotheoreticallysoundreasoningrules.Theseintuitivestrategies,referredtoas

judgmentalheuristicsin


28/148

thecontextofdecisionmaking,helpus

inreducingthecognitiveload,butalasattheexpenseofoptimal

decisionmaking.Eectively,ourunaidedjudgmentandchoiceexhibitsystematicviolationsofprobability

axioms(referredtoasbiases).Formaldiscussionofthemostimportantresearchresultsalongwithexperimentaldatacanbefoundinananthologyeditedby

Kahneman,Slovic,and


29/148

Tversky[16].Dawes[2]providesanaccessible

introductiontowhatisknownaboutpeople'sdecision-makingperformance.

One

mighthopethatpeoplewhohaveachievedexpertiseinadomainwillnot

besubjecttojudgmentalbiasesandwillapproachoptimalityindecisionmaking.Whileempiricalevidenceshowsthatexpertsindeedaremoreaccuratethannoviceswithin

theirareaof


30/148

expertise,italsoshowsthattheyalso

areliabletothesamejudgmentalbiasesasnovicesanddemonstrateapparent

errorsandinconsistenciesintheirjudgment.Professionalssuchaspracticingphysiciansuseessentially

thesamejudgmentalheuristicsandarepronetothesamebiases,althoughthedegreeofdeparturefromthenormativelyprescribedjudgmentseemstodecreasewith

experience.Inaddition


31/148

tolaboratoryevidence,thereareseveralstudies

ofexpertperformanceinrealisticsettings,showingthatitisinferioreven

tosimplelinearmodels(aninformalreviewoftheavailableevidenceandpointers

toliteraturecanbefoundinthebookbyDawes[2]).Forexample,predictionsoffutureviolentbehaviorofpsychiatricpatientsmadebyapanel

ofpsychiatristswho


32/148

hadaccesstopatientrecordsandinterviewed

thepatientswerefoundtobeinferiortoasimplemodelthat

includedonlythepastincidenceofviolentbehavior.Predictionsofmarriagecounselorsconcerning

maritalhappinesswereshowntobeinferiortoasimplemodelthatjustsubtractedtherateof


33/148

ghtingfromtherateofsexualintercourse

(again,themarriagecounselorshadaccesstoalldata,includinginterviewswith

thecouples).Studiesyieldingsimilarresultshavebeenconductedwithbankloanocers,

physicians,universityadmissioncommittees,andsoon.

4


34/148

ModelingDecisions

Thesuperiority

ofevensimplelinearmodelsoverhumanintuitivejudgmentsuggeststhatone

waytoimprovethequalityofdecisionsistodecomposeadecisionproblem

intosimplercomponentsthatarewellde


35/148

nedandwellunderstood.Studyingacomplex

systembuiltoutofsuchcomponentscanbesubsequentlyaidedbya

formal,theoreticallysoundtechnique.Theprocessofdecomposingandformalizingaproblemis

oftencalledmodeling.Modelingamountsto


36/148

ndinganabstractrepresentationofareal-world

systemthatsimpli


37/148

esandassumesasmuchaspossible

aboutthesystem,andwhileretainingthesystem'sessentialrelationships,omitsunnecessary

detail.Buildingamodelofadecisionproblem,asopposedtoreasoningabout

aprobleminaholisticway,allowsforapplyingscienti


38/148

cknowledgethatcanbetransferredacross

problemsandoftenacrossdomains.Itallowsforanalyzing,explaining,andarguing

aboutadecisionproblem.

Thedesiretoimprovehumandecisionmaking

providedmotivationforthedevelopmentofavarietyofmodelingtoolsindisciplinesofeconomics,operationsresearch,decisiontheory,decisionanalysis,andstatistics.Ineach

ofthesemodeling


39/148

tools,knowledgeaboutasystemisrepresented

bymeansofalgebraic,logical,orstatisticalvariables.Interactionsamongthesevariables

areexpressedbyequationsorlogicalrules,possiblyenhancedwithanexplicitrepresentation

ofuncertainty.Whenthefunctionalformofaninteractionisunknown,itissometimesdescribedinpurelyprobabilisticterms;forexample,byaconditionalprobability

distribution.Oncea


40/148

modelhasbeenformulated,avarietyof

mathematicalmethodscanbeusedtoanalyzeit.Decisionmakingundercertainty

hasbeenaddressedbyeconomicandoperationsresearchmethods,suchascashow

analysis,break-evenanalysis,scenarioanalysis,mathematicalprogramming,inventorytechniques,andavarietyofoptimizationalgorithmsforschedulingandlogistics.Decisionmakingunderuncertaintyenhances

theabovemethods


41/148

withstatisticalapproaches,suchasreliabilityanalysis,

simulation,andstatisticaldecisionmaking.Mostofthesemethodshavemadeit

intocollegecurriculaandcanbefoundinmanagementtextbooks.Duetospace

constraints,wewillnotdiscusstheirdetailsfurther.

ComponentsofDecisionModels

Whilemathematicallyamodelconsistsofvariablesanda

speci


42/148

cationofinteractionsamongthem,fromthe

pointofviewofdecisionmakingamodelanditsvariablesrepresent

thefollowingthreecomponents:ameasureofpreferencesoverdecisionobjectives,availabledecision

options,andameasureofuncertaintyovervariablesinuencingthedecisionandtheoutcomes.

Preferenceiswidelyviewedasthemostimportantconcept

indecisionmaking.


43/148

Outcomesofadecisionprocessarenot

allequallyattractiveanditiscrucialforadecisionmakerto

examinetheseoutcomesintermsoftheirdesirability.Preferencescanbeordinal(e.g.,

moreincomeispreferredtolessincome),butitisconvenientandoftennecessarytorepresentthemasnumericalquantities,especiallyiftheoutcomeof

thedecisionprocess


44/148

consistsofmultipleattributesthatneedto

becomparedonacommonscale.Evenwhentheyconsistofjust

asingleattributebutthechoiceismadeunderuncertainty,expressingpreferencesnumerically

allowsfortrade-osbetweendesirabilityandrisk.

Thesecondcomponentofdecisionproblemsisavailabledecisionoptions.Oftentheseoptionscanbeenumerated

(e.g.,alist


45/148

ofpossiblesuppliers),butsometimestheyare

continuousvaluesofspeci


46/148


47/148


48/148


49/148

touncertaintythereisnoguaranteethat

theresultoftheactionwillbetheoneintended,andthe

bestonecanhopeforistomaximizethechanceofadesirable

outcome.Theprocessrestsontheassumptionthatagooddecisionisonethatresultsfromagooddecision-makingprocessthatconsidersallimportantfactors

andisexplicit


50/148

aboutdecisionalternatives,preferences,anduncertainty.

Itisimportanttodistinguishbetweengooddecisionsandgoodoutcomes.

Byastrokeofgoodluckapoordecisioncanleadtoa

verygoodoutcome.Similarly,averygooddecisioncanbefollowedbyabadoutcome.Supportingdecisionsmeanssupportingthedecision-makingprocesssothatbetter

decisionsaremade.


51/148

Betterdecisionscanbeexpectedtolead

tobetteroutcomes.

DecisionSupportSystems

Decisionsupport

systemsareinteractive,computer-basedsystemsthataidusersinjudgmentandchoiceactivities.

Theyprovidedatastorageandretrievalbutenhancethetraditionalinformationaccessandretrievalfunctionswithsupportformodelbuildingandmodel-basedreasoning.Theysupport

framing,modeling,and


52/148


53/148

thinkingthroughandmodelingtheproblempays

offgenerouslyinthelongrun.

Therearethreefundamental

componentsofDSSs[22].

Databasemanagementsystem(DBMS).ADBMS

servesasadatabankfortheDSS.Itstoreslargequantitiesofdatathatarerelevanttotheclassofproblemsforwhichthe

DSShasbeen


54/148

designedandprovideslogicaldatastructures(as

opposedtothephysicaldatastructures)withwhichtheusersinteract.A

DBMSseparatestheusersfromthephysicalaspectsofthedatabasestructureand

processing.Itshouldalsobecapableofinformingtheuserofthetypesofdatathatareavailableandhowtogainaccesstothem.

Model-basemanagement


55/148

system(MBMS).TheroleofMBMSis

analogoustothatofaDBMS.Itsprimaryfunctionisprovidingindependence

betweenspeci


56/148

cmodelsthatareusedina

DSSfromtheapplicationsthatusethem.ThepurposeofanMBMS

istotransformdatafromtheDBMSintoinformationthatisusefulin

decisionmaking.SincemanyproblemsthattheuserofaDSSwillcopewithmaybeunstructured,theMBMSshouldalsobecapableofassisting

theuserin


57/148

modelbuilding.Dialoggenerationandmanagement

system(DGMS).ThemainproductofaninteractionwithaDSSis

insight.Astheirusersareoftenmanagerswhoarenotcomputer-trained,DSSsneed

tobeequippedwithintuitiveandeasy-to-useinterfaces.Theseinterfacesaidinmodel1AsBenjaminFranklinexpresseditin1789inaletterto

hisfriendM.


58/148

LeRoy,\inthisworldnothingcan

saidtobecertain,exceptdeathandtaxes(TheCompleteWorksof

BenjaminFranklin,JohnBigelow(ed),NewYorkandLondon:G.P.Putnam'sSons,1887,

Vol.10,page170).

6


59/148

building,butalsoininteractionwith

themodel,suchasgaininginsightandrecommendationsfromit.Theprimary

responsibilityofaDGMSistoenhancetheabilityofthesystemuser

toutilizeandbene


60/148

tfromtheDSS.Intheremainder

ofthisarticle,wewillusethebroadertermuserinterfacerather

thanDGMS.

WhileavarietyofDSSsexists,theabovethree

componentscanbefoundinmanyDSSarchitecturesandplayaprominentroleintheirstructure.InteractionamongthemisillustratedinFig.1.Essentially,

theuserinteracts


61/148

withtheDSSthroughtheDGMS.This

communicateswiththeDBMS

ModelBaseDatabaseMBMSDBMSDGMSDSSUser

Figure1:ThearchitectureofaDSSs(afterSage,Ref.[22]).

andMBMS,whichscreentheuserandtheuserinterfacefromthephysicaldetailsofthemodelbaseanddatabaseimplementation.

NormativeSystems

Normative


62/148

andDescriptiveApproaches

Whetheror

notonetruststhequalityofhumanintuitivereasoningstrategieshasa

profoundimpactonone'sviewofthephilosophicalandtechnicalfoundationsof

DSSs.Therearetwodistinctapproachestosupportingdecisionmaking.The


63/148

rstaimsatbuildingsupportproceduresor

systemsthatimitatehumanexperts.Themostprominentmemberofthisclass

ofDSSsareexpertsystems,computerprogramsbasedonruleselicitedfromhuman

domainexpertsthatimitatereasoningofahumanexpertinagivendomain.Expertsystemsareoftencapableofsupportingdecisionmakinginthatdomain

atalevel


64/148

comparabletohumanexperts.Whiletheyare

exibleandoftenabletoaddresscomplexdecisionproblems,theyarebased

onintuitivehumanreasoningandlacksoundnessandformalguaranteeswithrespectto

thetheoreticalreliabilityoftheirresults.Thedangeroftheexpertsystemapproach,increasinglyappreciatedbyDSSbuilders,isthatalongwithimitatinghumanthinking

anditsecient


65/148

heuristicprinciples,wemayalsoimitateits

undesirableaws[13].

Thesecondapproachisbasedonthe

assumptionthatthemostreliablemethodofdealingwithcomplexdecisionsisthrough

asmallsetofnormativelysoundprinciplesofhowdecisionsshouldbemade.Whileheuristicmethodsandadhocreasoningschemesthatimitatehumancognition

mayinmany


66/148

domainsperformwell,mostdecisionmakerswill

bereluctanttorelyonthemwheneverthecostofmakingan

errorishigh.Togiveanextremeexample,fewpeoplewouldchooseto

yairplanesbuiltusingheuristicprinciplesoverairplanesbuiltusingthelawsofaerodynamicsenhancedwithprobabilisticreliabilityanalysis.ApplicationofformalmethodsinDSSs

makesthesesystems


67/148

7


68/148

philosophicallydistinctfromthosebasedon

adhocheuristicarti


69/148

cialintelligencemethods,suchasrule-basedsystems.

ThegoalofaDSS,accordingtothisview,istosupport

unaidedhumanintuition,justasthegoalofusingacalculatoristo

aidhuman'slimitedcapacityformentalarithmetic.

Decision-AnalyticDecisionSupportSystems

AnemergentclassofDSSsknownasdecision-analyticDSSsapplies

theprinciplesof


70/148

decisiontheory,probabilitytheory,anddecisionanalysis

totheirdecisionmodels.Decisiontheoryisanaxiomatictheoryofdecision

makingthatisbuiltonasmallsetofaxiomsofrationaldecision

making.Itexpressesuncertaintyintermsofprobabilitiesandpreferencesintermsofutilities.Thesearecombinedusingtheoperationofmathematicalexpectation.The

attractivenessofprobability


71/148

theory,asaformalismforhandlinguncertainty

inDSSs,liesinitssoundnessanditsguaranteesconcerninglong-termperformance.

Probabilitytheoryisoftenviewedasthegoldstandardforrationalityinreasoning

underuncertainty.Followingitsaxiomsoersprotectionfromsomeelementaryinconsistencies.Theirviolation,ontheotherhand,canbedemonstratedtoleadtosure

losses[23].Decision


72/148

analysisistheartandscienceof

applyingdecisiontheorytoreal-worldproblems.Itincludesawealthoftechniques

formodelconstruction,suchasmethodsforelicitationofmodelstructureandprobability

distributionsthatallowminimizationofhumanbias,methodsforcheckingthesensitivityofamodeltoimprecisioninthedata,computingthevalueofobtaining

additionalinformation,and


73/148

presentationofresults.(See,forexample,Ref.

[27]forabasicreviewoftheavailabletechniques.)Thesemethodshave

beenundercontinuousscrutinybypsychologistsworkinginthedomainofbehavioral

decisiontheoryandhaveproventocopereasonablywellwiththedangersrelatedtohumanjudgmentalbiases.

Normativesystemsareusuallybasedon

graphicalprobabilisticmodels,


74/148

whicharerepresentationsofthejointprobability

distributionoveramodel'svariablesintermsofdirectedgraphs.Directedgraphs,

suchastheoneinFig.2,areknownasBayesiannetworks(BNs)

orcausalnetworks[19].Bayesiannetworksoeracompactrepresentationofjointprobabilitydistributionsandarecapableofpracticalrepresentationoflargemodels,consistingof

tensorhundreds


75/148

ofvariables.Bayesiannetworkscanbeeasily

extendedwithdecisionandvaluevariablesformodelingdecisionproblems.Theformer

denotevariablesthatareunderthedecisionmaker'scontrolandcanbedirectly

manipulated,andthelatterencodeuserspreferencesovervariousoutcomesofthedecisionprocess.Suchamendedgraphsareknownasinuencediagrams[15].Both

thestructureand


76/148

thenumericalprobabilitydistributionsinaBN

canbeelicitedfromahumanexpertandareareectionof

theexpert'ssubjectiveviewofareal-worldsystem.Ifavailable,scienti


77/148

cknowledgeaboutthesystem,bothin

termsofthestructureandfrequencydata,canbeeasilyincorporatedin

themodel.Onceamodelhasbeencreated,itisoptimizedusingformal

decision-theoreticalgorithms.Decisionanalysisisbasedontheempiricallytestedparadigmthatpeopleareabletoreliablystoreandretrievetheirpersonalbeliefsabout

uncertaintyandpreferences


78/148

fordierentoutcomes,butaremuchless

reliableinaggregatingthesefragmentsintoaglobalinference.Whilehumanexperts

areexcellentinstructuringaproblem,determiningthecomponentsthatarerelevantto

itandprovidinglocalestimatesofprobabilitiesandpreferences,theyarenotreliableincombiningmanysimplefactorsintoanoptimaldecision.Theroleof

adecision-analyticDSS


79/148


80/148

assumptions,isevenmoreimportantthanthe

actualrecommendation.

Decision-analyticDSSshavebeensuccessfullyappliedtopractical

systemsinmedicine,business,

8


81/148

Figure2:ExampleofaBayesian

networkmodelingteachingexpendituresinuniversityoperations.

andengineering.2As

thesesystemstendtonaturallyevolveintothreenotnecessarilydistinctclasses,it

maybeinterestingtocomparetheirstructureandarchitecturalorganization.

Systemswithstaticdomainmodels.Inthisclassofsystems,aprobabilistic

domainisrepr


82/148

esentedbyalargenetworkencodingthe

domain'sstructureanditsnumericalparameters.Thenetworkcomprisingthedomainmodel

isnormallybuiltbydecisionanalystsanddomainexperts.Anexamplemightbe

amedicaldiagnosticsystemcoveringacertainclassofdisorders.Queriesinsuchasystemareansweredbyassigningvaluestothosenodesof

thenetworkthat


83/148

constitutetheobservationsforaparticularcase

andpropagatingtheimpactoftheobservationthroughthenetworkin

orderto


84/148

ndtheprobabilitydistributionofsomeselected

nodesofinterest(e.g.,nodesthatrepresentdiseases).Suchanetworkcan,

onacase-by-casebasis,beextendedwithdecisionnodesandvaluenodesto

supportdecisions.Systemswithstaticdomainmodelsareconceptuallysimilartorule-basedexpertsystemscoveringanareaofexpertise.Systemswithcustomizeddecisionmodels.

Themainidea


85/148


86/148

Themainmotivationforthisapproachis

thepremisethateverydecisionisuniqueandneedstobelooked

atindividually;aninuencediagramneedstobetailoredtoindividualneeds[14].

2SomeexamplesofapplicationsaredescribedinaspecialissueofCommunicationsoftheACMonpracticalapplicationsofdecision-theoreticmethods(vol.38,no.

3,March1995).


87/148

ThereaderscanexperimentwithGeNIe[7],

adevelopmentsystemfordecision-analyticDSSsdevelopedattheDecisionSystemsLaboratory,

UniversityofPittsburgh,availableathttp://www2.sis.pitt.edu/genie.

9


88/148

Systemscapableoflearninga

modelfromdata.Thethirdclassofsystemsemployscomputer-intensivestatistical

methodsforlearningmodelsfromdata[1,11,12,21,26].Wheneverthere

aresucientdataavailable,thesystemscanliterallylearnagraphicalmodelfromthesedata.Thismodelcanbesubsequentlyusedtosupportdecisionswithin

thesamedomain.


89/148

The


90/148

rsttwoapproachesaresuitedforslightly

dierentapplications.Thecustomizedmodelgenerationapproachisanattemptto

automatethemostlaboriouspartofdecisionmaking,structuringaproblem,sofar

donewithsigni


91/148

cantassistancefromtraineddecisionanalysts.A

sessionwiththeprogramthatassiststhedecisionmakerinbuildingan

inuencediagramislaborious.Thismakesthecustomizedmodelgenerationapproachparticularlysuitable

fordecisionproblemsthatareinfrequentandseriousenoughtobetreatedindividually.Becauseinthestaticdomainmodelapproachanexistingdomainmodel

needstobe


92/148

customizedbythecasedataonly,the

decision-makingcycleisrathershort.Thismakesitparticularlysuitableforthose

decisionsthatarehighlyrepetitiveandneedtobemadeundertimeconstraints.

Apracticalsystemcancombinethethreeapproaches.Astaticdomainmodelcanbeslightlycustomizedforacasethatneedsindividualtreatment.

Oncecompleted,a


93/148

customizedmodelcanbeblendedintothe

largestaticmodel.Learningsystemscansupportboththestaticandthe

customizedmodelapproach.Ontheotherhand,thelearningprocesscanbegreatly

enhancedbypriorknowledgefromdomainexpertsorbyapriormodel.

Equation-BasedandMixedSystems

Inmanybusinessandengineering

problems,interactionsamong


94/148

modelvariablescanbedescribedbyequations

which,whensolvedsimultaneously,canbeusedtopredicttheeectof

decisionsonthesystem,andhencesupportdecisionmaking.Onespecialtypeof

simultaneousequationmodelisknownasthestructuralequationmodel(SEM),whichhasbeenapopularmethodofrepresentingsystemsineconometrics.Anequationis

structuralifit


95/148

describesaunique,independentcausalmechanismacting

inthesystem.Structuralequationsarebasedonexpertknowledgeofthe

systemcombinedwiththeoreticalconsiderations.Structuralequationsallowforanatural,modulardescription

ofasystemeachequationrepresentsitsindividualcomponent,aseparableandindependentmechanismactinginthesystemyet,themainadvantageofhavingastructuralmodel

is,asexplicated


96/148

bySimon[24],thatitincludescausal

informationandaidspredictionsoftheeectsofexternalinterventions.Inaddition,

thecausalstructureofastructuralequationmodelcanberepresentedgraphically[24],

whichallowsforcombiningthemwithdecision-analyticgraphicalmodelsinpracticalsystems[9,20].

Structuralequationmodelsoersigni


97/148

cantadvantagesforpolicymaking.Oftena

decisionmakerconfrontedwithacomplexsystemneedstodecidenotonly

thevaluesofpolicyvariablesbutalsowhichvariablesshouldbemanipulated.A

changeinthesetofpolicyvariableshasaprofoundimpactonthestructureoftheproblemandonhowtheirvalueswillpropagatethrough

thesystem.The


98/148

userdetermineswhichvariablesarepolicyvariables

andwhicharedeterminedwithinthemodel.AchangeintheSEMs

orthesetofpolicyvariablescanbereectedbyarapidrestructuring

ofthemodelandpredictionsinvolvingthisnewstructure[25].

Ourlong-termproject,theEnvironmentforStrategicPlanning(ESP)[6],isbasedon

ahybridgraphical


99/148

modelingtoolthatcombinesSEMswithdecision-analytic

principles.ESPiscapableofrepresentingbothdiscreteandcontinuousvariablesinvolved

indeterministicandprobabilisticrelationships.ThepowerfulfeaturesofSEMsallowESPto

actasagraphicalspreadsheetintegratingnumericalandsymbolicmethodsandallowingtheindependentvariablestobeselectedatwillwithouthavingtoreformulatethe

modeleachtime.


100/148

Thisprovidesanimmenseexibilitythatis

notaorded

10


101/148

byordinaryspreadsheetsinevaluatingalternate

policyoptions.

UserInterfacestoDecisionSupportSystems

Whilethequalityandreliabilityofmodelingtoolsandtheinternalarchitecturesof

DSSsareimportant,themostcrucialaspectofDSSsis,byfar,theiruserinterface.Systemswithuserinterfacesthatarecumbersomeorunclear

orthatrequire


102/148

unusualskillsarerarelyusefulandaccepted

inpractice.ThemostimportantresultofasessionwithaDSS

isinsightintothedecisionproblem.Inaddition,whenthesystemisbased

onnormativeprinciples,itcanplayatutoringrole;onemighthopethatuserswilllearnthedomainmodelandhowtoreasonwithit

overtime,and


103/148

improvetheirownthinking.

A

gooduserinterfacetoDSSsshouldsupportmodelconstructionandmodelanalysis,

reasoningabouttheproblemstructureinadditiontonumericalcalculationsandbothchoice

andoptimizationofdecisionvariables.Wewilldiscusstheseinthefollowingsections.

SupportforModelConstructionandModelAnalysis

User

interfaceisthe


104/148

vehicleforbothmodelconstruction(ormodel

choice)andforinvestigatingtheresults.Evenifasystemisbased

onatheoreticallysoundreasoningscheme,itsrecommendationswillbeasgoodas

themodeltheyarebasedon.Furthermore,evenifthemodelisaverygoodapproximationofrealityanditsrecommendationsarecorrect,theywill

notbefollowed


105/148

iftheyarenotunderstood.Withoutunderstanding,

theusersmayacceptorrejectasystem'sadviceforthewrong

reasonsandthecombineddecision-makingperformancemaydeteriorateevenbelowunaidedperformance[17].

Agooduserinterfaceshouldmakethemodelonwhichthesystem'sreasoningisbasedtransparenttotheuser.

Modelingisrarelya

one-shotprocess,and


106/148

goodmodelsareusuallyre


107/148

nedandenhancedastheirusersgather

practicalexperienceswiththesystemrecommendations.Itisimportanttostrikea

carefulbalancebetweenprecisionandmodelingeorts;somepartsofamodelneed

tobeveryprecisewhileothersdonot.Agooduserinterfaceshouldincludetoolsforexaminingthemodelandidentifyingitsmostsensitiveparts,

whichcanbe


108/148

subsequentlyelaboratedon.Systemsemployedinpractice

willneedtheirmodelsre


109/148

ned,andagooduserinterfaceshould

makeiteasytoaccess,examine,andre


110/148


111/148

anditsmodelarecomplexitis

insightfulforthedecisionmakertorealizehowthesystemvariablesare

interrelated.Thisishelpfulindesigningcreativedecisionoptionsbutalsoinunderstanding

howapolicydecisionwillimpacttheobjective.

Graphicalmodels,suchasthoseusedindecisionanalysisorinequation-basedandhybridsyst

ems,areparticularly


112/148

suitableforreasoningaboutstructure.Undercertain

assumptions,adirectedgraphicalmodelcanbegivenacausalinterpretation.This

isespeciallyconvenientinsituationswheretheDSSautonomicallysuggestsdecisionoptions;given

acausalinterpretationofitsmodel,

11


113/148

itiscapableofpredictingeects

ofinterventions.Acausalgraphfacilitatesbuildinganeectiveuserinterface.The

systemcanrefertocausalinteractionsduringitsdialoguewiththeuser,which

isknowntoenhanceuserinsight[3].

SupportforBothChoiceandOptimizationofDecisionVariables

ManyDSSshaveaninexible

structureinthe


114/148

sensethatthevariablesthatwillbe

manipulatedaredeterminedatthemodel-buildingstage.Thisisnotverysuitable

forplanningofthestrategictypewhentheobjectofthedecision-makingprocess

isidentifyingboththeobjectivesandthemethodsofachievingthem.Forexample,changingpolicyvariablesinaspreadsheet-basedmodeloftenrequiresthattheentire

spreadsheetberebuilt.


115/148

Ifthereisnosupportforthat,

fewuserswillconsideritasanoption.Thisclosestheworld

ofpossibilitiesforexiblereframingofadecisionproblemintheexploratoryprocess

ofsearchingforopportunities.SupportforbothchoiceandoptimizationofdecisionvariablesshouldbeaninherentpartofDSSs.

GraphicalInterface

Insightinto


116/148

amodelcanbeincreasedgreatlyat

theuserinterfacelevelbyadiagramrepresentingtheinteractionsamongits

components;forexample,adrawingofagraphonwhichamodelis

based,suchasinFig.2.Thisgraphisaqualitative,structuralexplanationofhowinformationowsfromtheindependentvariablestothedependentvariables

ofinterest.As


117/148


118/148

inFig.2isanexpandedversion

oftheTeachingExpendituressubmodelinFig.3.Theusercannavigate

throughthehierarchyoftheentiremodelinherquestforinsight,opening

andclosingsubmodelsondemand.Somepointerstoworkonuserinterfacesofdecision-analyticsystemscanbefoundin[4,5,28].

Summary

Decision


119/148

supportsystemsarepowerfultoolsintegratingscienti


120/148

cmethodsforsupportingcomplexdecisionswith

techniquesdevelopedininformationscience,andaregaininganincreasedpopularityin

manydomains.Theyareespeciallyvaluableinsituationsinwhichtheamountof

availableinformationisprohibitivefortheintuitionofanunaidedhumandecisionmakerandinwhichprecisionandoptimalityareofimportance.Decisionsupport

systemsaidhuman


121/148

cognitivede


122/148

cienciesbyintegratingvarioussourcesofinformation,

providingintelligentaccesstorelevantknowledge,aidingtheprocessofstructuring,and

optimizingdecisions.

NormativeDSSsoeratheoreticallycorrectandappealingway

ofhandlinguncertaintyandpreferencesindecisionproblems.Theyarebasedoncarefullystudiedempiricalprinciplesunderlyingthedisciplineofdecisionanalysisandtheyhave

beensuccessfullyapplied


123/148

inmanypracticalsystems.Webelievethat

theyoerseveralattractivefeaturesthatarelikelytoprevailinthe

longrunasfarasthetechnicaldevelopmentsareconcerned.

Because

DSSsdonotreplacehumansbutratheraugmenttheirlimitedcapacitytodealwithcomplexproblems,theiruserinterfacesarecritical.Theuserinterfacedetermines

whetheraDSS


124/148

12


125/148

Figure3:Asubmodel-levelviewof

adecisionmodel.

willbeusedatallandif

so,whethertheultimatequalityofdecisionswillbehigherthanthatof

anunaideddecisionmaker.

Acknowledgments

WorkonthisarticlewassupportedbytheNationalScienceFoundationunderFacultyEarlyCareerDevelopment

(CAREER)Program,grant


126/148

IRI{9624629,bytheAirForceOceof

Scienti


127/148

cResearchundergrantsF49620{97{1{0225andF49620{00{1{0112,

andbytheUniversityofPittsburghCentralResearchDevelopmentFund.Figures2

and3aresnapshotsofGeNIe,ageneralpurposedevelopmentenvironmentforgraphical

decisionsupportsystemsdevelopedbytheDecisionSystemsLaboratory,UniversityofPittsburghandavailableathttp://www.sis.pitt.edu/genie.WewouldliketothankMs.NanetteYurcikfor

herassistancewith


128/148

technicalediting.

References

[1]GregoryF.CooperandEdwardHerskovits.ABayesianmethodforthe

inductionofprobabilisticnetworksfromdata.MachineLearning,9(4):309{347,1992.[2]RobynM.

Dawes.RationalChoiceinanUncertainWorld.HartcourtBraceJovanovich,Publishers,1988.[3]MarekJ.Druzdzel.ProbabilisticReasoninginDecisionSupportSystems:FromComputation

toCommonSense.


129/148

PhDthesis,DepartmentofEngineeringandPublic

Policy,CarnegieMellonUniversity,Pittsburgh,PA,December1992.13


130/148

[4]MarekJ.Druzdzel.Explanationin

probabilisticsystems:Isitfeasible?willitwork?InProceedingsofthe

FifthInternationalWorkshoponIntelligentInformationSystems(WIS{96),pages12{24,Deblin,Poland,2{5

JUne1996.[5]MarekJ.Druzdzel.Fiveusefulpropertiesofprobabilisticknowledgerepresentationsfromthepointofviewofintelligentsystems.FundamentaInformatic,Specialissue

onKnowledgeRepresentation


131/148

andMachineLearning,30(3/4):241{254,June1997.[6]

MarekJ.Druzdzel.ESP:Amixedinitiativedecision-theoreticdecisionmodelingsystem.In

WorkingNotesoftheAAAI{99WorkshoponMixed-initiativeIntelligence,pages99{106,Orlando,

FL,18July1999.[7]MarekJ.Druzdzel.SMILE:StructuralModeling,Inference,andLearningEngineandGeNIe:Adevelopmentenvironmentforgraphicaldecision-theoreticmodels.In

Proceedingsofthe


132/148

SixteenthNationalConferenceonArti


133/148

cialIntelligence(AAAI{99),pages902{903,Orlando,FL,

July18{221999.[8]MarekJ.DruzdzelandF.JavierDez.Criteria

forcombiningknowledgefromdierentsourcesinprobabilisticmodels.InWorkingNotesof

theworkshoponFusionofDomainKnowledgewithDataforDecisionSupport,SixteenthAnnualConferenceonUncertaintyinArti


134/148

cialIntelligence(UAI{2000),pages23{29,Stanford,

CA,30June2000.[9]MarekJ.DruzdzelandHerbertA.Simon.

CausalityinBayesianbeliefnetworks.InProceedingsoftheNinthAnnualConferenceon

UncertaintyinArti


135/148

cialIntelligence(UAI{93),pages3{11,SanFrancisco,

CA,1993.MorganKaufmannPublishers,Inc.[10]MarekJ.DruzdzelandLinda

C.vanderGaag.Buildingprobabilisticnetworks:\Wheredothenumberscomefrom?

guesteditorsintroduction.IEEETransactionsonKnowledgeandDataEngineering,12(4):481{486,July{August2000.[11]ClarkGlymourandGregoryF.Cooper,editors.Computation,Causation,andDiscovery.

AAAIPress,Menlo


136/148

Park,CA,1999.[12]DavidE.Heckerman,

DanGeiger,andDavidM.Chickering.LearningBayesiannetworks:Thecombinationof

knowledgeandstatisticaldata.MachineLearning,20(3):197{243,1995.[13]MaxHenrion,JohnS.

Breese,andEricJ.Horvitz.DecisionAnalysisandExpertSystems.AIMagazine,12(4):64{91,Winter1991.[14]SamuelHoltzman.IntelligentDecisionSystems.Addison-Wesley,Reading,MA,1989.

[15]RonaldA.


137/148

HowardandJamesE.Matheson.Inuencediagrams.

InRonaldA.HowardandJamesE.Matheson,editors,ThePrinciplesand

ApplicationsofDecisionAnalysis,pages719762.StrategicDecisionsGroup,MenloPark,CA,

1984.[16]DanielKahneman,PaulSlovic,andAmosTversky,editors.JudgmentUnderUncertainty:HeuristicsandBiases.CambridgeUniversityPress,Cambridge,1982.[17]PaulE.Lehner,

TheresaM.Mullin,


138/148

andMarvinS.Cohen.Aprobabilityanalysis

oftheusefulnessofdecisionaids.InM.Henrion,R.D.Shachter,L.N.

Kanal,andJ.F.Lemmer,editors,UncertaintyinArti


139/148

cialIntelligence5,pages427{436.ElsevierScience

PublishersB.V.(NorthHolland),1990.[18]Tsai-ChingLu,MarekJ.Druzdzel,and

Tze-YunLeong.Causalmechanism-basedmodelconstruction.InProceedingsoftheSixteenthAnnual

ConferenceonUncertaintyinArti


140/148

cialIntelligence(UAI{2000),pages353{362,San

Francisco,CA,2000.MorganKaufmannPublishers,Inc.14


141/148

[19]JudeaPearl.ProbabilisticReasoningin

IntelligentSystems:NetworksofPlausibleInference.MorganKaufmannPublishers,Inc.,SanMateo,

CA,1988.[20]JudeaPearl.Causality:Models,Reasoning,andInference.CambridgeUniversityPress,

Cambridge,UK,2000.[21]JudeaPearlandThomasS.Verma.Atheoryofinferredcausation.InJ.A.Allen,R.Fikes,andE.Sandewall,editors,

KR{91,Principlesof


142/148

KnowledgeRepresentationandReasoning:Proceedingsofthe

SecondInternationalConference,pages441{452,Cambridge,MA,1991.MorganKaufmannPublishers,Inc.,

SanMateo,CA.[22]AndrewP.Sage.DecisionSupportSystemsEngineering.JohnWiley

&Sons,Inc.,NewYork,1991.[23]LeonardJ.Savage.TheFoundationsofStatistics(SecondRevisedEdition).DoverPublications,NewYork,NY,1972.[24]Herbert

A.Simon.Causal


143/148

orderingandidenti


144/148

ability.InWilliamC.HoodandTjalling

C.Koopmans,editors,StudiesinEconometricMethod.CowlesCommissionforResearchin

Economics.MonographNo.14,chapterIII,pages49{74.JohnWiley&Sons,

Inc.,NewYork,NY,1953.[25]HerbertA.Simon,JayantR.Kalagnanam,andMarekJ.Druzdzel.Performancebudgetplanning:Thecaseofaresearch

university.Inpreparation,


145/148

2000.[26]PeterSpirtes,ClarkGlymour,and

RichardScheines.Causation,Prediction,andSearch.SpringerVerlag,NewYork,1993.[27]

DetlofvonWinterfeldtandWardEdwards.DecisionAnalysisandBehavioralResearch.Cambridge

UniversityPress,Cambridge,1988.[28]HaiqinWangandMarekJ.Druzdzel.Userinterfacetoolsfornavigationinconditionalprobabilitytablesandelicitationofprobabilities

inBayesiannetworks.


146/148

InProceedingsoftheSixteenthAnnualConference

onUncertaintyinArti


147/148


148/148

Decison Support System

Documents

Transcript of Decison Support System