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Chapter12LimitedcognitionandorganisationGeorgeHendrikseEconomicsandManagementofOrganisations:Co-ordination,MotivationandStrategy
Chapter12EconomicsandManageFieldsBehaviouralaccountingbehaviouralfinanceEconomicpsychology/consumerbehaviourOrganisationalbehaviourStrategicdecisionmakingFieldsBehaviouralaccountingFigureVI.1:Positioningofboundedrationalityapproaches
Behaviouralhypothesis
Opportunistic
Selfinterested
Idealistic
Complete
Rationality
Limited
X
Procedural
X
X
X
FigureVI.1:PositioningofboFirmfromanevolutionaryperspective
DeelV:TreeFirmfromanevolutionarypersMakingmistakes
Forgetting
LimitedreasoningcapabilitiesMakingmistakes
Forgetting
LimDegreeofrationalityRatioofthecognitivecapacitiesofthedecisionmakerandthecomplexityoftheproblem.DegreeofrationalityRatioofTypesofrationalityComplete:ratiois1Bounded:ratiobetween0and1Procedural:ratiois(almost)0TypesofrationalityComplete:Ifthedegreeofrationalityissmallerthan1,thentherewillbeabiasinbehaviour(comparedtothecompleterationalitycase).IfthedegreeofrationalityiIncreaseratiobyincreasingcognitivecapacities,cationdecreasingthecomplexityoftheproblem,e.g.bysplittinguptheproblem,usingcomputersIncreaseratiobyTwotypesofboundedrationalityDeductiveInductiveTwotypesofboundedrationaliPartitioningPartitioningDeductiveboundedrationalityHowtooptimallyallocatealimitednumberofcognitiveunitsinacomplexproblem?DeductiveboundedrationalityHHowtomake(deductive)boundedrationalityoperational?Numberofpartitionsofthesetofpossibleevents/states.Howtomake(deductive)boundeCognitivecapacitiesofapersonThecognitivecapacitiesofapersonarethenumberofpartitionsapersonisabletomakeinresponsetoaparticularproblem.CognitivecapacitiesofapersComplexityofaproblemThecomplexityofaproblemisthenumberofpartitionsthatisneededtodistinguishallaspects/states/eventsofaproblem.ComplexityofaproblemThecomExample:
ColourrecognitionproblemPossiblestates: R:Red G:Green W:White D:DarkExample:
ColourrecognitionpComplexityofthecolourrecognitionproblemR|G|W|DConclusion:3partitionsimpliescomplexity3.ComplexityofthecolourrecogFigure12.1:ColourrecognitioncapacitiesofdifferentdecisionmakersDecisionmaker
Partitioningofsetofstates
Degreeofrationality
Human
{(R),(G),(W),(D)}
3/3=1
Pussycat
{(R,G),(W),(D)}
2/3
Mole
{(R,G,W),(D)}
1/3
Spoon
{(R,G,W,D)}
0
Figure12.1:ColourrecognitioExample:
OrganisationalstructureFunctionalDivisionalExample:
OrganisationalstrucFunctionalstructure
Product1productionProduct2productionProduct1salesProduct2salesProductionSalesLocalmanagerOpportunitiesforimprovement?DivisionmanagerInformationCEOFunctionalstructure
Product1Divisionalstructure
Product1productionProduct1salesProduct2productionProduct2salesProduct1Product2LocalmanagerOpportunitiesforimprovement?DivisionmanagerIncreasedprofit?opportunities?CEODivisionalstructure
Product1Informationcompressionfromemployeestothebossisnecessaryduetolimitedcognitivecapacitiesoftheboss.
However,informationcompressionisnotneutral.Everystructureofinformationchannelsleadsinevitablytoacertainbiasintheprovisionofinformation.
InformationcompressionfromeExampleOrganisationconsistsoftwodivisionsEachdivisionconsistsoftwomanagersCEOonlyusesadviceofeachdivisionDivisionsbasetheiradviceoninformationofthelocalmanagersExampleOrganisationconsistsoInformationonlocalmanagersProductionmanager1(2)indicatesalways(never)thattherearepossibilitiesforcostreductionsMarketingmanager1(2)isalwaysoptimistic(pessimistic)regardingadditionalsalesinthefutureInformationonlocalmanagersPInformationaggregationAdivisionreportspositivelyonlywhenbothlocalmanagersarepositiveAdivisionreportsdoubtfulwhenthereportsofthelocalmanagersaremixedAdivisionreportsnegativelyonlywhenbothlocalmanagersarenegativeInformationaggregationAdivisInferencesinafunctionalstructure
Product1productionProduct2productionProduct1salesProduct2salesYesNoYesNoProductionAmbiguousAmbiguousSalesLocalmanagerOpportunitiesforimprovement?DivisionmanagerInformationCEOInferencesinafunctionalstrCEOTheCEOinafunctionalstructuredecidestodonothing.CEOTheCEOinafunctionalstrInferencesinadivisionalstructure
Product1productionProduct1salesProduct2productionProduct2salesYesYesNoNoProduct1Product2LocalmanagerOpportunitiesforimprovement?DivisionmanagerIncreasedprofit?opportunities?CEOYesNoInferencesinadivisionalstrCEOTheCEOinadivisionalstructuredecidestoallocateasmanymeansaspossibletodivision1inthefuture.CEOTheCEOinadivisionalstrConclusionThestructureofthelearningenvironmentseemstobeatleastasimportantasthemeaningofthings.ConclusionThestructureoftheDifferentbiasesAfunctionalstructurecreatesanaggregationbiastowardsthegenerationofproduct-relateddata.Adivisionalstructuremeansanaggregationbiasregardingthegenerationoffunctionallyrelateddata.DifferentbiasesAfunctionalsIfthedegreeofrationalityissmallerthan1,theneachpartitioningentailsacertainbias.IfthedegreeofrationalityiComplexityand
self-organisationComplexityand
self-organisatInductiveboundedrationalityDecisionsarebasedonlimited,localinformation.InductiveboundedrationalityDMissinginformationisdealtwithbymakinganalogiesusingheuristicrulesofthumbconstructingplausible,simplerrepresentationsoftheproblemMissinginformationisdealtwIngredientsoftheorylearningbasedonfeedbackadjustrulesofthumbbasedonnaturalselectionIngredientsoftheorylearningHowdoyoumake(inductive)boundedrationalityoperational?Specifysimplebehaviouralrules.Howdoyoumake(inductive)boAtransitionrulespecifiesthenextstateofacellbyitscurrentstateandthelocalenvironment.AtransitionrulespecifiesthResearchquestionWhichsimplerulesdrivebehaviour?ResearchquestionWhichsimpleHowtoproceed?Trialanderrorbycomputersimulations.Howtoproceed?TrialanderrorHowdoyoumodelalocalenvironment?HowdoyoumodelalocalenvirFigure12.5:(a)vonNeumanenvironment,(b)Mooreenvironment
Figure12.5:(a)vonNeumanenExample:
SegregationGhettosExample:
SegregationGhettosSuppose
0:blue
X:greenSuppose
0:blue
X:greenFigure12.6:Startingposition
Figure12.6:StartingpositionTransitionrulesDonotmovewhenatleasthalfofthepersonsintheMoore-environmentisofthesamecolur.MovetothemostcloselocationwhereatleasthalfofthepersonsintheMoore-environmentisofthesamecolourwhenlessthanhalfofthepersonsinthecurrentMoore-environmentisofthesamecolour.TransitionrulesDonotmovewhFigure12.7:Stationarysituation
Figure12.7:StationarysituatExample:
FinanceInductive,boundedrationaldecisionmakersExample:
FinanceInductive,boTransitionrule/buy-saleofcomputerprogramPutmore(less)inriskystockswhenreturnswerepositive(negative)intherecentpast.Transitionrule/buy-saleofUnderlyingvaluePriceValuetImplicationNotanefficientmarketUnderlyingvaluePricetImplicatArchitecturechoice Vacancy Annualreportaccountant Lawinparliament IssueinUnitedNations Scientificjournal Possibilitiesofappeal Innovationprojectinfirm MarketsystemArchitecturechoice VacancyArchitectureRuleforaggregatinglocaldecisionsintoanorganisationdecision.ArchitectureRuleforaggregatiFigure12.12:Firmasacollectionofbureaus
Collectionofbureaus
Figure12.12:FirmasacollecTwoarchitecturesHierarchyPolyarchyTwoarchitecturesHierarchyHierarchyOrganisationonlyacceptsaprojectwhennobodyrejectsit.HierarchyOrganisationonlyaccFigure12.14:Hierarchy
Project
Office1
Office2
Accepted
no
1-p
no
1-p
Rejected
Rejected
p
yes
yes
p
Figure12.14:HierarchyProjeDecision-makingauthorityisconcentratedLocal/IndividualdecisionmakershavevetopowerAcceptancerequiresunanimityPropertiesDecision-makingauthorityiscPolyarchy Organisationonlyrejectsaprojectwheneverybodyrejectsit.Polyarchy OrganisationonlyreDecision-makingauthorityisnotconcentratedEverydecisionmakerhasthepowertoacceptaprojectPropertiesDecision-makingauthorityisnFigure12.15:Polyarchy
Project
Office1
Office2
Accept
no
1-p
no
1-p
Reject
p
yes
yes
p
Accept
Figure12.15:PolyarchyProjeConclusionApolyarchyisgoodatacceptingprojects,whereashierarchiesaregoodatrejectingprojects.ConclusionApolyarchyisgoodWhichorganisationalchoiceminimiseserrorsofjudgement?WhichorganisationalchoicemiTherearetwotypesofmistakes:TypeIerrorsTypeIIerrorsTherearetwotypesofmistakeFigure12.13:type-Iversustype-IIerrors
?
accept
reject
accept
?
reject
good
project
bad
project
?
desirable
decision
type-
I
error
type-
II
error
desirable
decision
Figure12.13:type-IversustyChooseapolyarchywhentype-Ierrorsarerelativelyexpensive.Ahierarchyisdesirablewhentype-IIerrorsarerelativelyexpensive.Results
Chooseapolyarchywhentype-IEvolutionaryapproachesEvolutionaryapproachesEvolutionarypsychologyClaimsregardingthecognitivecapacitiesofpeoplehavetobebasedinevolutionarybiology.EvolutionarypsychologyClaimsResult1:
GlobalrationalityItisunlikelythatglobalrationalityemergesoutofanevolutionaryprocess.Result1:
GlobalrationalityIReason1:Adaptiveoroptimalbehaviourdependstoalargeextentonthespecificsituation.Reason2:Addingmoredimensionspreventsthatevenlimitedgeneralsystemswillfunctionwell.Thisisduetocombinatorialexplosion.Reason1:Thisresultsin:modularityhierarchyparallellisationThisresultsin:Reason3:Generalsystemsdonotperformwellinspecificsituationsbecausecrucialdetailsarenottakenintoaccount.Reason3:Result2:
formfollowsfunctionThepropertiesofanevolvedsystem/mechanism/formreflectthestructureoftheproblemthathastobedealtwith.Thenatureoftheproblemdirectsthereforethekindofsolutionthatisformulated.Result2:
formfollowsfunctiExample1:
Structurefollowsstrategy(Chandler,1962)Example1:
StructurefollowsNaturalselectionresultsinmechanismsgearedtowardsusinginformationintheformitispresented.NaturalselectionresultsinmChapter12LimitedcognitionandorganisationGeorgeHendrikseEconomicsandManagementofOrganisations:Co-ordination,MotivationandStrategy
Chapter12EconomicsandManageFieldsBehaviouralaccountingbehaviouralfinanceEconomicpsychology/consumerbehaviourOrganisationalbehaviourStrategicdecisionmakingFieldsBehaviouralaccountingFigureVI.1:Positioningofboundedrationalityapproaches
Behaviouralhypothesis
Opportunistic
Selfinterested
Idealistic
Complete
Rationality
Limited
X
Procedural
X
X
X
FigureVI.1:PositioningofboFirmfromanevolutionaryperspective
DeelV:TreeFirmfromanevolutionarypersMakingmistakes
Forgetting
LimitedreasoningcapabilitiesMakingmistakes
Forgetting
LimDegreeofrationalityRatioofthecognitivecapacitiesofthedecisionmakerandthecomplexityoftheproblem.DegreeofrationalityRatioofTypesofrationalityComplete:ratiois1Bounded:ratiobetween0and1Procedural:ratiois(almost)0TypesofrationalityComplete:Ifthedegreeofrationalityissmallerthan1,thentherewillbeabiasinbehaviour(comparedtothecompleterationalitycase).IfthedegreeofrationalityiIncreaseratiobyincreasingcognitivecapacities,cationdecreasingthecomplexityoftheproblem,e.g.bysplittinguptheproblem,usingcomputersIncreaseratiobyTwotypesofboundedrationalityDeductiveInductiveTwotypesofboundedrationaliPartitioningPartitioningDeductiveboundedrationalityHowtooptimallyallocatealimitednumberofcognitiveunitsinacomplexproblem?DeductiveboundedrationalityHHowtomake(deductive)boundedrationalityoperational?Numberofpartitionsofthesetofpossibleevents/states.Howtomake(deductive)boundeCognitivecapacitiesofapersonThecognitivecapacitiesofapersonarethenumberofpartitionsapersonisabletomakeinresponsetoaparticularproblem.CognitivecapacitiesofapersComplexityofaproblemThecomplexityofaproblemisthenumberofpartitionsthatisneededtodistinguishallaspects/states/eventsofaproblem.ComplexityofaproblemThecomExample:
ColourrecognitionproblemPossiblestates: R:Red G:Green W:White D:DarkExample:
ColourrecognitionpComplexityofthecolourrecognitionproblemR|G|W|DConclusion:3partitionsimpliescomplexity3.ComplexityofthecolourrecogFigure12.1:ColourrecognitioncapacitiesofdifferentdecisionmakersDecisionmaker
Partitioningofsetofstates
Degreeofrationality
Human
{(R),(G),(W),(D)}
3/3=1
Pussycat
{(R,G),(W),(D)}
2/3
Mole
{(R,G,W),(D)}
1/3
Spoon
{(R,G,W,D)}
0
Figure12.1:ColourrecognitioExample:
OrganisationalstructureFunctionalDivisionalExample:
OrganisationalstrucFunctionalstructure
Product1productionProduct2productionProduct1salesProduct2salesProductionSalesLocalmanagerOpportunitiesforimprovement?DivisionmanagerInformationCEOFunctionalstructure
Product1Divisionalstructure
Product1productionProduct1salesProduct2productionProduct2salesProduct1Product2LocalmanagerOpportunitiesforimprovement?DivisionmanagerIncreasedprofit?opportunities?CEODivisionalstructure
Product1Informationcompressionfromemployeestothebossisnecessaryduetolimitedcognitivecapacitiesoftheboss.
However,informationcompressionisnotneutral.Everystructureofinformationchannelsleadsinevitablytoacertainbiasintheprovisionofinformation.
InformationcompressionfromeExampleOrganisationconsistsoftwodivisionsEachdivisionconsistsoftwomanagersCEOonlyusesadviceofeachdivisionDivisionsbasetheiradviceoninformationofthelocalmanagersExampleOrganisationconsistsoInformationonlocalmanagersProductionmanager1(2)indicatesalways(never)thattherearepossibilitiesforcostreductionsMarketingmanager1(2)isalwaysoptimistic(pessimistic)regardingadditionalsalesinthefutureInformationonlocalmanagersPInformationaggregationAdivisionreportspositivelyonlywhenbothlocalmanagersarepositiveAdivisionreportsdoubtfulwhenthereportsofthelocalmanagersaremixedAdivisionreportsnegativelyonlywhenbothlocalmanagersarenegativeInformationaggregationAdivisInferencesinafunctionalstructure
Product1productionProduct2productionProduct1salesProduct2salesYesNoYesNoProductionAmbiguousAmbiguousSalesLocalmanagerOpportunitiesforimprovement?DivisionmanagerInformationCEOInferencesinafunctionalstrCEOTheCEOinafunctionalstructuredecidestodonothing.CEOTheCEOinafunctionalstrInferencesinadivisionalstructure
Product1productionProduct1salesProduct2productionProduct2salesYesYesNoNoProduct1Product2LocalmanagerOpportunitiesforimprovement?DivisionmanagerIncreasedprofit?opportunities?CEOYesNoInferencesinadivisionalstrCEOTheCEOinadivisionalstructuredecidestoallocateasmanymeansaspossibletodivision1inthefuture.CEOTheCEOinadivisionalstrConclusionThestructureofthelearningenvironmentseemstobeatleastasimportantasthemeaningofthings.ConclusionThestructureoftheDifferentbiasesAfunctionalstructurecreatesanaggregationbiastowardsthegenerationofproduct-relateddata.Adivisionalstructuremeansanaggregationbiasregardingthegenerationoffunctionallyrelateddata.DifferentbiasesAfunctionalsIfthedegreeofrationalityissmallerthan1,theneachpartitioningentailsacertainbias.IfthedegreeofrationalityiComplexityand
self-organisationComplexityand
self-organisatInductiveboundedrationalityDecisionsarebasedonlimited,localinformation.InductiveboundedrationalityDMissinginformationisdealtwithbymakinganalogiesusingheuristicrulesofthumbconstructingplausible,simplerrepresentationsoftheproblemMissinginformationisdealtwIngredientsoftheorylearningbasedonfeedbackadjustrulesofthumbbasedonnaturalselectionIngredientsoftheorylearningHowdoyoumake(inductive)boundedrationalityoperational?Specifysimplebehaviouralrules.Howdoyoumake(inductive)boAtransitionrulespecifiesthenextstateofacellbyitscurrentstateandthelocalenvironment.AtransitionrulespecifiesthResearchquestionWhichsimplerulesdrivebehaviour?ResearchquestionWhichsimpleHowtoproceed?Trialanderrorbycomputersimulations.Howtoproceed?TrialanderrorHowdoyoumodelalocalenvironment?HowdoyoumodelalocalenvirFigure12.5:(a)vonNeumanenvironment,(b)Mooreenvironment
Figure12.5:(a)vonNeumanenExample:
SegregationGhettosExample:
SegregationGhettosSuppose
0:blue
X:greenSuppose
0:blue
X:greenFigure12.6:Startingposition
Figure12.6:StartingpositionTransitionrulesDonotmovewhenatleasthalfofthepersonsintheMoore-environmentisofthesamecolur.MovetothemostcloselocationwhereatleasthalfofthepersonsintheMoore-environmentisofthesamecolourwhenlessthanhalfofthepersonsinthecurrentMoore-environmentisofthesamecolour.TransitionrulesDonotmovewhFigure12.7:Stationarysituation
Figure12.7:StationarysituatExample:
FinanceInductive,boundedrationaldecisionmakersExample:
FinanceInductive,boTransitionrule/buy-saleofcomputerprogramPutmore(less)inriskystockswhenreturnswerepositive(negative)intherecentpast.Transitionrule/buy-saleofUnderlyingvaluePriceValuetImplicationNotanefficientmarketUnderlyingvaluePricetImplicatArchitecturechoice Vacancy Annualreportaccountant Lawinparliament IssueinUnitedNations Scientificjournal Possibilitiesofappeal Innovationprojectinfirm MarketsystemArchitecturechoice VacancyArchitectureRuleforaggregatinglocaldecisionsintoanorganisationdecision.ArchitectureRuleforaggregatiFigure12.12:Firmasacollectionofbureaus
Collectionofbureaus
Figure12.12:FirmasacollecTwoarchitecturesHierarchyPolyarchyTwoarchitecturesHierarchyHierarchyOrganisationonlyacceptsaprojectwhennobodyrejectsit.HierarchyOrganisationonlyaccFigure12.14:Hierarchy
Project
Office1
Office2
Accepted
no
1-p
no
1-p
Rejected
Rejected
p
yes
yes
p
Figure12.14:HierarchyProjeDecision-makingauthorityisconcentratedLocal/IndividualdecisionmakershavevetopowerAcceptancerequiresunanimityPropertiesDecision-makingauthorityiscPolyarchy Organisationonlyrejectsaprojectwheneverybodyrejectsit.Polyarchy OrganisationonlyreDecision-makingauthorityisnotconcentratedEverydecisionmakerhasthepow
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