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 The A nalytic Ne twork Process Decision Making with Dependence and Feedback 
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Tutorial_4_Changing from AHP to ANP thinking.ppt

Jul 05, 2018

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Page 1: Tutorial_4_Changing from AHP to ANP thinking.ppt

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The Analytic Network Process

Decision Making with Dependence and Feedback 

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Changing from a Hierarchical

Model to a Network Model

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Network Models do not have

Goals

A network model has a

criteria cluster and an

alternatives cluster, but

no goal.

Step 1. Compare the

alternatives with respect

to each criterion (like we

do in AHP models).

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Establishing Priorities for Criteria

 In AHP one answers the question: “Howimportant is Criterion1 compared to

Criterion2 with respect to the goal?”Sometimes too abstract a question to answer.

 In ANP criteria are prioritied !" as#ing

how important the" are in the alternati$es

 !eing considered% Usually an easier question to

answer when looking at actual alternatives that have the

 properties in question.

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Cars compared with respect to Prestige

(and similarly for the other criteria)

.707

.070

.!

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Feedback: Link Alternatives to Criteria and

compare for Preference

For eample! a pairwise "#estion wo#ld be: $For the Ac#ra %L! which do

yo# like better! its prestige or its price&' $ts prestige or its *P+&' etc,

."##

.0$$

.07#

.!%%

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*ake Comparisons

 &or each car pairwise compare thecriteria 'or importance to esta!lish the

 priorities o' the criteria in that car:

Prestige( Price( )P*( and Com'ort+his results in a $ector o' priorities o'

the criteria 'or each car%

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Pairwise Compare Criteria for each Car

,esults 'or Acura +-

,esults 'or +o"ota Camr"

Acura +-

+o"ota Camr"

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Pairwise Compare Criteria wrt Cars

,esults 'or Honda Ci$icHonda Ci$ic

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+he .nweighted /upermatri0

&or e'am(le) the (riorities in the last col*mn come fromcom(aring the criteria with res(ect to the Honda Civic. That

is) what we val*e most a+o*t the Honda is its (rice.

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+he limit supermatri0 is o!tained !" raising the weighted supermatri0 to powers

until it con$erges in this case all columns are the same( though not alwa"s true%

-imit /upermatri0

  A-P  A.P

Ac#ra %L ,/01  ,2//

%oyota Camry ,312  ,455

.onda Civic ,267  ,/00

+he 'inal answer( the

s"nthesis( !elow is

o!tained !" normaliing

the raw $alues 'or the

alternati$es 'rom the limitsupermatri0:

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8hy are the res#lts different&

3n A.P 4 the user( going top5down ma#ing

comparisons( when as#ed without re'erring

to actual alternati$es( o$er5estimated the

importance o' cost%

3n A-P 4 the user learned through

'eed!ac# comparisons that his6her priorit"

'or Cost is not nearl" as high as originall"

thought when as#ed the question a!stractl"(

while Prestige gets more weight%

This is why a husband and wife, setting out to the dealership to buy a “sensible” low

 priced car, walked out with something entirely different and much more costly.

When they saw the shiny cars on the showroom floor they reised their priorities.

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nner and 9#ter ependence

39#ter dependence 5 the parent node andthe nodes to !e compared are in di''erent

clusters% A directed lin# appears from the

 parent node cluster to the other cluster%

3nner dependence 5 the parent node and

the nodes to !e compared are in the same

cluster% +he cluster is lin#ed to itsel' and aloop lin# appears%

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7e can add some inner dependence: eg( price

is in'luenced !" prestige and com'ort8 and

 prestige is in'luenced !" price and com'ort%

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An 90ample o' an Inner ependent

Comparison is shown !elow:

,hich infl*ences Price more) Prestige or Comfort-

,hich infl*ences Prestige more) Price or Comfort-

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+he results with the interdependencies in

 place are:

  A-P  A-P  A.P

  (8ith inner (-o inner

  dependencies) dependence)

Ac#ra %L ,/;1  ,/01  ,2//

%oyota Camry ,3;4  ,312  ,455

.onda Civic ,223  ,267  ,/00