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Omega

journalhomepage:www.elsevier.com/locate/omega

Inconsistency thresholds for incomplete pairwise comparison matrices R

Kolos Csaba Ágoston

a

, László Csató

b,c,

aCorvinus University of Budapest (BCE), Department of Operations Research and Actuarial Sciences, Budapest, Hungary

bInstitute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Laboratory on Engineering and Management Intelligence, Research Group of Operations Research and Decision Systems, Budapest, Hungary

cCorvinus University of Budapest (BCE), Department of Operations Research and Actuarial Sciences, Budapest, Hungary

a rt i c l e i nf o

Article history:

Received 18 June 2021 Accepted 16 November 2021 Available online 19 November 2021 MSC:

90B50 91B08 C44 D71 Keywords:

Analytic hierarchy process (AHP) Decision analysis

Inconsistency threshold Incomplete pairwise comparisons Multi-criteria decision-making

a b s t r a c t

Pairwisecomparisonmatricesareincreasinglyusedinsettingswheresomepairsaremissing.However, there existfew inconsistency indicesforsimilar incompletedatasets and noreasonablemeasurehas anassociatedthreshold.Thispapergeneralisesthefamousruleofthumbfortheacceptablelevelofin- consistency,proposedbySaaty,toincompletepairwisecomparisonmatrices.Theextensionisbasedon choosingthemissingelementssuchthatthemaximaleigenvalueoftheincompletematrixisminimised.

Consequently,thewell-establishedvaluesoftherandomindexcannotbeadopted:theinconsistencyof randommatricesisfoundtobethefunctionofmatrixsizeandthenumberofmissingelements,witha nearlylineardependenceinthecaseofthelattervariable.Ourresultscanbedirectlybuiltintodecision- makingsoftwareandusedbypractitionersasastatisticalcriterionforacceptingorrejectinganincom- pletepairwisecomparisonmatrix.

© 2021TheAuthor(s).PublishedbyElsevierLtd.

ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/)

‘‘Mathematicsisthepartofphysicswhereexperimentsarecheap.1 (VladimirIgorevichArnold:Onteachingmathematics) 1. Introduction

Pairwisecomparisonsformanessentialpartofmanydecision- making techniques,especially since the appearance of the popu- lar Analytic Hierarchy Process (AHP) methodology [31,32]. Despite simplifyingtheissuetoevaluatingobjectspairbypair,thetool of pairwise comparisons presentssomechallenges dueto thepossi- ble lack of consistency: if alternative A is two times better than alternative Bandalternative Bisthree timesbetterthan alterna- tive C, then alternative A isnot necessarily sixtimesbetter than alternativeC.Theoriginofsimilarinconsistenciesresidesinasking seemingly“redundant” questions.Nonetheless,additionalinforma- tionisoftenrequiredtoincreaserobustness[13],andinconsistency

R Area: Decision Analysis and Preference-Driven Analytics This manuscript was processed by Associate Editor Luis Dias.

Corresponding author.

E-mail addresses: kolos.agoston@uni-corvinus.hu (K.C. Ágoston), laszlo.csato@sztaki.hu (L. Csató).

1Source: [5, p. 229] .

usuallydoesnotcauseaseriousproblemuntilitremainsatamod- eratelevel.

Inconsistentpreferencescallforquantifying thelevelofincon- sistency.Thefirstandbyfarthe mostextensivelyusedindexhas beenproposedbythefounderoftheAHP,ThomasL.Saaty[31].He hasalsoprovidedasharpthresholdto decidewhetherapairwise comparisonmatrixhasanacceptablelevelofinconsistencyornot.

Thiswidelyacceptedruleofinconsistencyhasbeenconstructed for the case when all comparisons are known. However, there areatleastthreeargumentswhyincompletepairwisecomparisons shouldbeconsideredindecision-makingmodels[22]:

in the case of a large number n of alternatives, completing all n(n−1)/2 pairwise comparisons is resource-intensive and mightrequiremucheffortfromexpertssufferingfromalackof time;

unwillingnesstomakeadirectcomparisonbetweentwoalter- nativesforethical,moral,orpsychologicalreasons;

the decision-makers may be unsure of some of the compar- isons,forinstance,duetolimitedknowledgeontheparticular issue.

Incertain settings,both incompleteness andinconsistency are inherent features of the data. The beating relation in sports is https://doi.org/10.1016/j.omega.2021.102576

0305-0483/© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

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rarelytransitiveandsomeplayers/teamshaveneverplayedagainst each other[7,15–17,29].Analogously,thereexists noguaranteefor consistencywhenthepairwisecomparisonsaregivenbythebilat- eral remittancesbetweencountries[28],orby thepreferences of studentsbetweenuniversities[19].

Finally, note that pairwise comparison matrices are usually filled sequentially by the decision-makers, see e.g. the empirical research conductedbyBozóki etal.[8].Ifthedegree ofinconsis- tencyismonitoredcontinuouslyduringthisprocess,thedecision- maker might be warned immediately afterthe appearance of an unexpected value [9].Consequently,there isahigherchance that theproblemcanbesolvedeasilycomparedtotheusualcasewhen thesupervisionofthecomparisonsisaskedonlyafterallpairwise comparisonsaregiven.Thisisespeciallyimportantasthesevalues areoftenprovidedbyexpertswhosufferfromalackoftime.

Letusseeanexample,wherethemissingelementsaredenoted by∗:

A=

⎢ ⎣

1 2 ∗ 4

1/2 1 2 ∗

∗ 1/2 1 2 1/4 ∗ 1/2 1

⎥ ⎦

.

Pairwise comparisonmatrix Ais inconsistentbecause a12×a23× a34=2×2×2=8=4=a14. But it remains unknown whether thisdeviationcanbetoleratedornot.

The currentpaper aims to provide thresholds of acceptability for pairwise comparison matriceswithmissing entries.We want to follow the concept of Saaty as closely as possible. Therefore, the unknown elements are considered as variables to be chosen to reduce the inconsistency of the parametric complete pairwise comparison matrix, that is, to minimise its maximal eigenvalue assuggestedby Shiraishietal.[35] andShiraishiandObata[34]. Themainchallengeresidesinthecalculationoftherandomindex, a key componentofSaaty’s threshold:theoptimal completion of each randomly generatedincompletepairwise comparisonmatrix shouldbefoundseparatelyinordertoobtaintheminimalvalueof thePerronrootofthecompletedmatrix[11].

Inconsistencyindicesarethoroughlyresearchedintheliterature [14].ThereexistseveralattemptstocalculatethresholdsforSaaty’s indexunderdifferentassumptions [3,12,27],aswellasforvarious inconsistencyindicessuchasthegeometricconsistencyindex[2], ortheSalo–Hamalainenindex[4].Liangetal.[24]proposeconsis- tencythresholdsfortheBestWorstMethod(BWM).

On the other hand, the studyof inconsistency indices for in- complete pairwise comparisons has been started only recently.

Szybowski et al. [36] introduce two new inconsistency measures basedonspanningtrees.KułakowskiandTalaga[23]adaptseveral existingindicestoanalyseincompletedatasetsbutdonotprovide anythreshold.Toconclude,withoutthe presentcontribution,one cannot decidewhethertheinconsistencyoftheaboveincomplete pairwise comparisonmatrix Aisexcessive ornot.Thus ourwork fillsasubstantialresearchgap.

EventhoughForman[21]computesrandomindicesforincom- plete pairwise comparison matrices, his solution is basedon the proposal of Harker [22]. That introduces an auxiliary matrix for anyincompletepairwisecomparisonmatrixinsteadoffillingitby optimising an objectivefunctionaswedo.Ourapproach isprob- ablyclosertoSaaty’sconceptsincetheauxiliarymatrixofHarker [22]isnotapairwisecomparisonmatrix.

The paper is structured as follows. Section 2 presents the fundamentals of pairwise comparison matrices and inconsis- tency measures. Incomplete pairwise comparison matrices and the eigenvalueminimisationproblemare introducedinSection 3. Section 4 discusses the details of computing the random index.

The inconsistencythresholds are reportedinSection 5.Anumer- ical example is provided in Section 6, and a real life application

inSection7.Finally,Section8offersasummaryanddirectionsfor futureresearch.

2. Pairwisecomparisonmatricesandinconsistency

Thepairwisecomparisonsofthealternativesarecollectedinto amatrixA=

ai j suchthattheentryai j isthenumericalanswer tothequestion“Howmanytimesalternativeiisbetterthanalter- native j?” LetR+ denotethe set ofpositive numbers,Rn+ denote the set of positive vectors of size n and Rn+×n denote the set of positive square matricesof size nwith all elementsgreater than zero,respectively.

Definition2.1. Pairwisecomparisonmatrix:MatrixA=

ai j ∈Rn+×n isapairwisecomparisonmatrixifaji=1/ai j forall1≤i,jn.

LetAdenotethesetofpairwisecomparisonmatricesandAn×n denotetheset ofpairwisecomparisonmatricesofsizen,respec- tively.

Definition 2.2. Consistency: A pairwise comparison matrix A=

ai jAn×n isconsistentifaik=ai jajk forall 1≤i,j,kn. Other- wise,itissaidtobeinconsistent.

According to the famous Perron–Frobenius theorem, for any pairwise comparisonmatrix AA,there exists a unique positive weightvectorwsatisfyingAw=

λ

max(A)wandni=1wi=1,where

λ

max(A)isthemaximalorPerroneigenvalueofmatrixA. Saaty has considered an affine transformation of this eigen- value.

Definition 2.3. Consistency index: Let AAn×n be any pairwise comparisonmatrixofsizen.Itsconsistencyindexis

CI

(

A

)

=

λ

max

(

A

)

n n−1 .

Since CI(A)=0 ⇐⇒

λ

max(A)=n, the consistency index CI is a reasonable measure of how far a pairwise comparison matrix is from a consistent one [31,32]. Aupetitand Genest [6] provide a tight upper bound forthe value ofCI whenthe entries of the pairwisecomparisonmatrixareexpressedonaboundedscale.

Saatyhas recommended using a discrete scale for the matrix elements,i.e.,forall1≤i,jn:

ai j

{

1/9,1/8,1/7,...,1/2,1,2,...,8,9

}

. (1)

Anormalisedmeasureofinconsistencycanbeobtainedassug- gestedbySaaty.

Definition 2.4. Random index: Consider the set An×n ofpairwise comparisonmatricesofsizen.ThecorrespondingrandomindexRI isprovidedbythefollowingalgorithm[3]:

Generating a large number of pairwise comparison matrices suchthateachentryabovethediagonalisdrawnindependently anduniformlyfromtheSaatyscale(1).

Calculatingtheconsistency indexCIforeach randompairwise comparisonmatrix.

Computingthemeanofthesevalues.

Severalauthorshavepublishedslightlydifferentrandomindices dependingonthesimulationmethodandthenumberofgenerated matrices involved, see Alonso and Lamata [3, Table 1]. The ran- domindicesRIn arereportedinTable1for4≤n≤10asprovided by Bozóki andRapcsák[12] andvalidated by Csató and Petróczy [18].Theseestimatesareclosetotheonesgiveninpreviousworks [3,27].BozókiandRapcsák[12,Table3]uncovershowRIndepends onthelargestelementoftheratioscale.

Definition 2.5. Consistency ratio: Let AAn×n be any pairwise comparison matrix of size n. Its consistency ratio is CR(A)= CI(A)/RIn.

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Table 1

The values of the random index for complete pairwise comparison matrices.

Matrix size 4 5 6 7 8 9 10

Random index RI n 0.884 1.109 1.249 1.341 1.404 1.451 1.486

Saaty hasproposed a thresholdforthe acceptability ofincon- sistency,too.

Definition 2.6. Acceptable level of inconsistency: Let AAn×n be anypairwisecomparisonmatrixofsizen.Itissufficientlycloseto aconsistentmatrixandthereforecanbeacceptedifCR(A)≤0.1.

Even though applying a crispdecision rule onthe fuzzy con- ceptof”largeinconsistency” isstrange[14]andthereexistsophis- ticatedstatisticalstudiestotestconsistency[25,26],itisassumed throughoutthepaperthatthe10%ruleisawell-establishedstan- dardworthgeneralisingtoincompletepairwisecomparisonmatri- ces.

3. Theeigenvalueminimisationproblemforincomplete pairwisecomparisonmatrices

Certainentriesofapairwisecomparisonmatrixaresometimes missing.

Definition 3.1. Incomplete pairwisecomparison matrix:Matrix A=

ai j is an incomplete pairwise comparison matrix if ai j∈R+

{

}

suchthatforall1≤i,jn,ai j∈R+impliesaji=1/ai j andai j=∗ impliesaji=∗.

LetAn×ndenotethesetofincompletepairwisecomparisonma- tricesofsizen.

The graph representation of incomplete pairwise comparison matrices isa convenient tool tovisualise thestructure ofknown elements.

Definition3.2. Graphrepresentation:Anincompletepairwisecom- parison matrix AAn×n can be represented by the undirected graph G=(V,E), where the vertices V=

{

1,2,...,n

}

correspond

to the alternatives and the edges in E are associated with the known matrix entries outside the diagonal, that is, ei jE ⇐⇒

ai j=∗andi=j.

To summarise, there are no edges for the missing elements (ai j=∗)aswellasfortheentriesofthediagonal(aii).

InthecaseofanincompletepairwisecomparisonmatrixA,Shi- raishi etal.[35] andShiraishi andObata [34] consider an eigen- value optimisation problem by substituting the m missing ele- ments of matrix A above the diagonal with positive values ar- rangedinthevectorx∈Rm+,whilethereciprocityconditionispre- served:

minxRm+

λ

max

(

A

(

x

) )

. (2)

Themotivationisclear,allmissingentriesshouldbechosentoget amatrixthatisasclosetoaconsistentoneaspossibleintermsof theconsistencyindexCI.

AccordingtoBozókietal.[11,Section3],(2)canbetransformed intoaconvexoptimisationproblem.Theauthorsalsogivethenec- essaryandsufficientconditionfortheuniqueness ofthesolution:

thegraphGrepresentingtheincompletepairwisecomparisonma- trixAshouldbeconnected.Thisisanintuitiveandalmostobvious requirementsincetherelationoftwoalternativescannotbeestab- lished ifthey are not compared atleast indirectly,through other alternatives.

Fig. 1. The graph representation of the pairwise comparison matrix A in Example 4.1 .

4. Thecalculationoftherandomindexforincomplete pairwisecomparisonmatrices

Consider an incomplete pairwise comparison matrix AAn×n andacompletepairwisecomparisonmatrixBAn×n,wherebi j= ai j ifai j=∗. LetA(x)An×n be theoptimal completion ofAac- cording to (2). Clearly,

λ

max(A(x))

λ

max(B), hence CI(A(x))CI(B). This implies that the value of the random index RIn, cal- culatedforcompletepairwise comparisonmatrices,cannotbe ap- pliedinthecaseofanincompletepairwisecomparisonmatrixbe- causeitsconsistency indexCIisobtainedthrough optimising(i.e.

minimising)itslevelofinconsistency.

Consequently, by adopting the numbers fromTable 1, the ra- tioofincompletepairwisecomparisonmatriceswithanacceptable level of inconsistency will exceed the concept of Saaty and this discrepancyincreases as thenumber of missingelements grows.

In theextreme casewhen graph Gis a spanningtree ofa com- pletegraphwithnnodes(thusitisaconnectedgraphconsisting ofexactly n−1 edges without cycles),the corresponding incom- pletematrixcanbefilledoutsuchthatconsistencyisachieved.

Therefore, the random indexneeds to be recomputed for in- completepairwisecomparisonmatrices,anditsvaluewillsuppos- edly bea monotonicallydecreasing functionofm,thenumberof missingelements.

Remark 1. In the view of the Saaty scale (1), there are at least threedifferentwaystochoosethemissingentriesxk,1≤km:

1. Method1: xk∈R+,namely,each missingentrycanbean arbi- trarypositivenumber;

2. Method2:1/9≤xk≤9,namely,themissingentries cannot be higher(lower)thanthetheoreticalmaximum(minimum)ofthe knownelements;

3. Method 3: xk

{

1/9,1/8,1/7,...,1/2,1,2,...,8,9

}

, namely,

eachmissingentryisdrawnfromthediscreteSaatyscale.

LetusillustratethethreeapproacheslistedinRemark1. Example4.1. Take thefollowing incompletepairwise comparison matrix:

A=

⎢ ⎣

1 ∗ 9 ∗

∗ 1 2 8

1/9 1/2 1 4

∗ 1/8 1/4 1

⎥ ⎦

.

ThecorrespondingundirectedgraphGisdepictedinFig.1.Note thatGwouldbeaspanningtreewithouttheedgebetweennodes

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2and4anda24=8=2×4=a23a34.Consequently,Acanbefilled outconsistentlyinauniqueway:

A1=

⎢ ⎣

1 9/2 9 36

2/9 1 2 8 1/9 1/2 1 4 1/36 1/8 1/4 1

⎥ ⎦

.

The firsttechnique(Method1inRemark1) resultsinA1 with

λ

max

A1

=4.

On the other hand, A1 is not valid under Method 2 in Remark1becausea114=36>9,thatis,theconsistentfillingisnot allowedasbeingoutsidetheSaatyscale(1).Theoptimalcomplete pairwisecomparisonmatrixA2isgivenbythesolutionofthecon- vexeigenvalueminimisationproblem(2)withtheadditionalcon- straints1/9≤xk≤9forall1≤kmandisasfollows:

A2=

⎢ ⎣

1 9/4 9 9

4/9 1 2 8 1/9 1/2 1 4 1/9 1/8 1/4 1

⎥ ⎦

,

where

λ

max

A2

=4.1855.

Finally, A2 is not valid under Method 3 inRemark 1because a212=9/4∈/Z,thatis,eventhoughtheoptimalfillingbyMethod2 doesnotcontainanyvalueexceedingtheboundsoftheSaatyscale (1), some ofthem are not integers orthe reciprocalsofintegers.

Hence,thebestpossiblefillingontheSaatyscale(1)is

A3=

⎢ ⎣

1 2 9 9

1/2 1 2 8 1/9 1/2 1 4 1/9 1/8 1/4 1

⎥ ⎦

,

whichleadsto

λ

max

A3

=4.1874.

Among thethreeideasinRemark1,Method1alwaysleadsto thesmallestdominanteigenvalue,followedbyMethod2,whereas Method3providesthegreatestoptimumofproblem(2)ascanbe seenfromtherestrictionsinRemark1.

We implement Method2 to calculatethe random indicesRIn. Thefirstreasonisthatthealgorithmforthe

λ

max-optimalcomple-

tion [11,Section 5] involves an exogenously giventolerancelevel to determinehowaccurate arethecoordinatesofthe eigenvector associated with the dominant eigenvalue asa stopping criterion.

Consequently, itcannot bechosen appropriately ifthematrixen- triesandtheelementsoftheweightvectorcandiffersubstantially:

the consistent completion of an incomplete pairwise comparison matrixwithnalternatives maycontain (1/9)(n1) or9(n1)asan element ifthecorresponding graphisachain.Furthermore,itre- mains questionablewhyelementsbeloworabovetheSaatyscale (1)areallowedforthemissingentriesiftheyareprohibitedinthe caseofknownelements.Ontheotherhand,Method3presentsa discreteoptimisationproblemthatismoredifficulttohandlethan itscontinuousanalogueofMethod2.Tosummarise,sincethepro- cessisbasedongeneratingalargenumberofrandomincomplete pairwisecomparisonmatricestobefilledoutoptimally,itisneces- sarytoreducethecomplexityofoptimisationproblem(2)byusing Method2.

A complete pairwise comparisonmatrix ofsize ncan be rep- resented by a complete graph wherethe degree of each node is n−1.Hence, the graph corresponding toan incomplete pairwise comparison matrix is certainly connected if mn−2, implying that thesolutionofthe

λ

max-optimalcompletion isunique.How- ever,thegraphmightbedisconnectedifmn−1,inwhichcase it makesnosense tocalculatetheconsistencyindexCI ofthein- complete pairwise comparison matrix.Furthermore, if m>n(n− 1)/2−(n−1),thentherearelessthann−1knownelements,and thegraphisalwaysdisconnected.

If the number of missing entries is exactly m=n(n−1)/2− (n−1)=(n−1)(n−2)/2,thenthegraphisconnectedifandonly if it is a spanning tree. Even though these incomplete pairwise comparisonmatricescertainlyhaveaconsistentcompletionunder Method1,thisdoesnotnecessarilyholdunderMethod2whenthe missingentriescannotbearbitrarilylarge/small.

5. Generalisedthresholdsfortheconsistencyratio

AswehavearguedinSection4,thevalueoftherandomindex RIn,m probably dependsnot onlyon the size nofthe incomplete pairwise comparisonmatrix buton thenumberofitsmissingel- ementsm, too.Thus therandom indexis computedaccordingto thefollowingprocedure(cf.Definition2.4):

1. GeneratinganincompletepairwisecomparisonmatrixAofsize nwithmmissingentriesabovethediagonalsuchthateachele- mentabovethediagonalisdrawnindependentlyanduniformly fromthe Saatyscale(1),whilethe placeoftheunknown ele- mentsabovethediagonalischosenrandomly.

2. Checking whether the graph G representing the incomplete pairwisecomparisonmatrixAisconnectedordisconnected.

3. Ifgraph Gisconnected,optimisationproblem(2)issolved by thealgorithm forthe

λ

max-optimalcompletion [11, Section 5]

withrestrictingallentriesinx∈Rm+ accordingtoMethod2in Remark1toobtaintheminimumof

λ

max(A(x))andthecorre- spondingcompletepairwisecomparisonmatrix.

4. Computing and saving the consistency index CI

ˆ

A

based on Definition2.3.

5. RepeatingSteps 1–4to get 1million random matriceswith a connected graph representation, and calculating the mean of theconsistencyindicesCIfromStep4.

OurcentralresultisreportedinTable2,whichisanextension ofTable 1 to thecase whensome pairwise comparisons are un- known.Thevaluesinthe firstrow,whichcoincidewiththeones fromTable 1,confirm the integrity of theproposed techniqueto compute the thresholds forthe consistency indexCI. The role of missingelementscannotbeignoredatallcommonlyusedsignifi- cancelevelsasreinforcedbythet-test:foranygivenn,thevalues ofRIn,marestatisticallydifferentfromeachother.

Recallthatthemaximalnumberofmissingelementsisatmost n(n−1)/2−(n−1)=(n−1)(n−2)/2ifconnectednessisnotvi- olated,andthisvalueis3ifn=4,6ifn=5,and10ifn=6.Some thresholds are lacking from Table 2—for example, the pair n=7 andm=4—duetoexcessivecomputationtime(>48hours).

However,RIn,mcan be easily predictedasfollows.Fig.2reveals that the random index is monotonically decreasing asthe func- tionofmissingvaluesm accordingto commonintuition.Further- more,thedependenceisnearlylinear,thusaplausibleestimation is provided by the belowformula, whichrequires only the “om- nipresent”Table 1:

RIn,m

1− 2m

(

n1

)(

n2

)

RIn,0. (3)

Obviously,(3)returnsRIn,0 ifthereare nomissingelements(m= 0).Ontheotherhand,m=(n−1)(n−2)/2meansthatthegraph representingtheincompletepairwise comparisonmatrixiseither unconnected, or it is a spanning tree, thus the matrix can be filledconsistentlyifthereisnorestrictiononitselements.Formula (3)immediatelyfollowsbyassumingalinearfunctionforinterme- diatevaluesofm.

According to the “case studies” in Table 3, (3) gives at least a reasonable guess of RIn,m without much effort, even though it somewhat underestimates the true value. The discrepancy is mainly caused by RIn,(n−1)(n−2)/2 being larger than zero (see Table2) asincompletepairwise comparisonmatricesrepresented

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Table 2

The values of the random index for incomplete pairwise comparison matrices.

Missing elements m

Matrix size n

4 5 6 7

0 0.884 1.109 1.249 1.341

1 0.583 (0.531) 0.925 (0.485) 1.128 (0.400) 1.256 (0.330) 2 0.306 (0.387) 0.739 (0.452) 1.007 (0.392)

3 0.053 (0.073) 0.557 (0.405) 0.883 (0.380)

4 0.379 (0.340) 0.758 (0.364)

5 0.212 (0.247) 0.634 (0.344)

6 0.059 (0.068) 0.510 (0.317)

7 0.389 (0.281)

8 0.271 (0.234)

9 0.161 (0.170)

All values are based on 1 million matrices. Standard deviations are given in parenthe- sis.

Fig. 2. The random index RI n,mas the function of the number of missing entries m .

Table 3

Approximation of the random index for incomplete pairwise comparison ma- trices according to equation (3) .

Matrix size n

Missing elements m

Value of RI n,m

Computed Approximated by formula (3)

7 4 0.998 0.983

8 5 1.088 1.070

9 6 1.158 1.140

10 7 1.215 1.197

15 4 1.519 1.514

15 8 1.453 1.445

15 12 1.387 1.375

byaspanningtreecanbemadeconsistentonlyifthemissingele- mentscanbearbitrary,butnotiftheyareboundedtotheinterval [1/9,9].

Definition 2.5 canbe modifiedstraightforwardly toderive the consistency ratioCR foranyincomplete pairwisecomparisonma- trix.

Definition 5.1. Consistencyratio: LetAAn×n be any incomplete pairwisecomparisonmatrixofsizenwithmmissingentriesabove the diagonal and be the complete pairwise comparisonmatrix given by the optimalfilling of A.The consistency ratioof the in- completematrixAisCR(A)=CI(Aˆ)/RIn,m.

The popular 10% threshold of Definition 2.6 can be adopted withoutanychanges.

IntheapplicationsoftheAHPmethodology,theoptimalnum- ber of alternatives does not exceed nine [33]. Random indices forcompletepairwisecomparisonmatriceshavebeendetermined

forn≤16 inAguarón andMoreno-Jiménez [2]andfor n≤15 in Alonsoand Lamata [3].The corresponding thresholds forincom- pletepairwisecomparisonmatricescanbe calculatedofflineby a supercomputer andbuiltintoany softwareused by practitioners.

Ifthesearenotavailable,formula(3)providesagoodapproxima- tionforanynumberofalternativesnandmissingelementsm,see Table3.

6. Anillustrativeexample

Inthissection, wehighlight theimplicationsofthe calculated thresholdsfortherandomindexbyanumericalillustration.Ithas beenchosen to be simplebut expressive.With threealternatives andone missingentry, the matrix can be filled out consistently.

Therefore,thenumberofalternatives isfour.Again,thereexists a consistent fillingif thereare three missingelements, hencetheir numberis two.Furthermore,they are indifferentrows,which is themorelikelycase.

Example 6.1. Take the following parametric incomplete pairwise comparisonmatrixofsizen=4withm=2missingelements:

A

( α

,

β )

=

⎢ ⎣

1

α

β

1/

α

1

α

∗ 1/

α

1

α

1/

β

∗ 1/

α

1

⎥ ⎦

.

Now RI4,0≈0.884 and RI4,2≈0.356 from Table 2. There are threeinstanceswheretheoptimalfillingofmatrixA(

α

,

β

)results inaconsistentpairwisecomparisonmatrix:

( α

,

β )

1

2, 1 8

;

(

1,1

)

;

(

2,8

)

.

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Table 4

Consistency indices of the parametric incomplete pairwise comparison matrix A , β) in Example 6.1 . Value

of β

Value of α

1/5 1/4 1/3 1/2 1 2 3 4 5

1/9 0.1495 0.0818 0.0253 0.0003 0.1031 0.4187 0.7338 1.0344 1.3214 1/8 0.1637 0.0921 0.0311 0 0.0921 0.3940 0.6982 0.9890 1.2671 1/7 0.1807 0.1047 0.0383 0.0004 0.0805 0.3670 0.6592 0.9393 1.2076 1/6 0.2015 0.1202 0.0477 0.0017 0.0680 0.3374 0.6160 0.8842 1.1414 1/5 0.2278 0.1401 0.0601 0.0046 0.0547 0.3042 0.5673 0.8220 1.0667 1/4 0.2624 0.1667 0.0774 0.0100 0.0404 0.2663 0.5113 0.7500 0.9801 1/3 0.3114 0.2048 0.1031 0.0201 0.0253 0.2217 0.4444 0.6637 0.8759 1/2 0.3891 0.2663 0.1462 0.0404 0.0100 0.1667 0.3599 0.5536 0.7426 1 0.5476 0.3940 0.2394 0.0921 0 0.0921 0.2394 0.3940 0.5476 2 0.7426 0.5536 0.3599 0.1667 0.0100 0.0404 0.1462 0.2663 0.3891 3 0.8759 0.6637 0.4444 0.2217 0.0253 0.0201 0.1031 0.2048 0.3114 4 0.9801 0.7500 0.5113 0.2663 0.0404 0.0100 0.0774 0.1667 0.2624 5 1.0667 0.8220 0.5673 0.3042 0.0547 0.0046 0.0601 0.1401 0.2278 6 1.1414 0.8842 0.6160 0.3374 0.0680 0.0017 0.0477 0.1202 0.2015 7 1.2076 0.9393 0.6592 0.3670 0.0805 0.0004 0.0383 0.1047 0.1807 8 1.2671 0.9890 0.6982 0.3940 0.0921 0 0.0311 0.0921 0.1637 9 1.3214 1.0344 0.7338 0.4187 0.1031 0.0003 0.0253 0.0818 0.1495 Bold numbers indicate that the consistency ratio C R ˆ

A , β)

= C I ˆ A , β)

/RI 4,2 is below the 10%

threshold.

Italic numbers indicate that CI ˆ A , β)

/RI 4,0 is below the 10% threshold but the consistency ratio C R ˆ

A , β)

= C I ˆ A , β)

/RI 4,2is above it.

Theyshouldbeacceptedunderanycircumstances.

Examine what happens if

α

=1 is fixed. Then

β

=3 implies CI

ˆ

A(1,3)

≈0.0253<0.RI4,2,whichstillcorrespondstoanac- ceptable level of inconsistency. However, CI

ˆ

A(1,4)

≈0.0404>

0.RI4,2, making it necessary to reduce inconsistency if

β

=4. Ontheotherhand,CI

ˆ

A(1,4)

≈0.0404<0.RI4,0,thustheop- timallyfilledoutincompletepairwisecomparisonmatrixmightbe accepted accordingto the “standard” thresholdforcomplete ma- trices becausethelatterdoesnot takeintoaccounttheautomatic reductionofinconsistencyduetotheoptimisationprocedure.

Table 4reportstheconsistency indexCI ofmatrixA(

α

,

β

) for some parameters

α

and

β

.

α

is restricted between 1/5 and 5 because a12(

α

,

β

)×a23(

α

,

β

)×a34(

α

,

β

)=

α

3 but a14(

α

,

β

)=

β

.

Bold numbers correspond to the cases when inconsistency can be tolerated based on the approximated thresholds of Table 2, while italic numbers show instances that can be accepted only if the optimalsolution A(x) of(2) is considered asa (complete) pairwise comparisonmatrix andthethreshold of10% isused for CI(A(x))/RI4,0.

7. Areallifeapplication:Continuousmonitoringof inconsistency

Bozóki et al. [8] carried out a controlled experiment, where universitystudentswere dividedintosubgroupstomakepairwise comparisonsfromdifferenttypesofproblems,withdifferentnum- ber of alternatives in different questioning orders. Consequently, not only the complete pairwise comparison matrices are known but their incomplete submatrices obtained aftera given number ofcomparisonswasasked.Wehavepickedoneinterestingmatrix fromthisdataset.

Example 7.1. The following pairwise comparison matrix reflects the opinion of a decision-maker on how much more a summer house isliked comparedto anothersummer houseon anumeri-

Fig. 3. The consistency ratio CR as the function of known elements in Example 7.1 .

calscale:

A=

⎢ ⎢

⎢ ⎢

1 2 7 7 7 5

1/2 1 5 7 5 2

1/7 1/5 1 1 1/5 1/5 1/7 1/7 1 1 1/3 1/3 1/7 1/5 5 3 1 3 1/5 1/2 5 3 1/3 1

⎥ ⎥

⎥ ⎥

.

The questioningorderof the15 comparisonsis a12,a64,a51, a32, a56, a13, a24, a61, a43, a52,a14, a35, a26,a45,and a36. This proce- dure,proposed byRoss[30],optimises twoobjectivefunctions:it maximisesthedistancesforthesamealternativestoreappearand aims to balance the number ofthe first and second positions in thecomparisonforeveryalternative.

Fig. 3 shows how inconsistency changes as more and more comparisons are given by the decision-maker. Following Bozókietal.[8,Figure2], thesolid red lineusesthe randomin- dexassociatedwithacomplete6×6pairwisecomparisonmatrix, whichisnotavalidapproachaccordingtoSection4.Ontheother hand,the dashed blue lineis obtainedby the values ofthe ran- domindexaccordingtoour computations,see Table2.The naïve approachindicates no problemaround inconsistency,its level re-

(7)

mains belowthe10%thresholdduringthefillinginprocess.How- ever, accountingforthenumberofmissingelementsrevealsthat inconsistencyissubstantiallyincreasedwhentheseventhcompar- ison(a24)ismade.Eventhoughthecompletepairwisecomparison matrix canbe acceptedwithrespect toinconsistency, continuous monitoringwarnsthedecision-makerthatthisparticularcompari- sonisworthreconsidering.

8. Conclusions

The paper reportsapproximatedthresholds forthe mostpop- ular measure of inconsistency, proposed by Saaty, in the case of incompletepairwisecomparisonmatrices.Theyaredeterminedby the value of the random index, that is, the average consistency ratio ofa largenumber ofrandom pairwise comparisonmatrices with missingelements. The calculation is far fromtrivialsince a separate convex optimisation problemshould be solved for each matrix to find the optimal fillingof unknown entries. Numerical results uncover that the threshold depends not only on the size ofthepairwise comparisonmatrixbutonthenumberofmissing entries,too.Aplausiblelinearestimationoftherandomindexhas alsobeenprovided.

According to Table 2 and two examples, the extended values ofthe randomindexbecome indispensableinorderto generalise Saaty’sconcepttoincompletecomparisons.Theassociatedthresh- oldscanbedirectlyprogrammedintodecision-makingsoftware.

Withthesuggestedruleofacceptability,thedecision-makercan decide for any incomplete pairwise comparison matrix whether there isa needtoreviseearlierassessments ornot.It allowsthe levelofinconsistencytobemonitoredevenbeforeallcomparisons are given, whichmayimmediatelyindicate possiblemistakesand suspicious entries.Therefore, the preference revision process can be launched as early as possible. It will be examined in future studies how thisopportunity canbe builtinto theknown incon- sistencyreductionmethods[1,10,20,37].

CRediTauthorshipcontributionstatement

KolosCsaba Ágoston: Methodology, Software, Validation, For- malanalysis, Writing– review &editing.László Csató:Conceptu- alization,Methodology,Validation,Formalanalysis,Writing– orig- inaldraft,Writing– review&editing.

Acknowledgements

WearegratefultoSándorBozóki,Lavoslav ˇCaklovic,andZsombor Szádoczkiforusefuladvice.

Four anonymous reviewers provided valuable comments and suggestionsonearlierdrafts.

Theresearch wassupportedbytheMTAPremiumPostdoctoral ResearchProgramgrantPPD2019-9/2019.

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