组织的经济学与管理学(英文)ch12-Limited-cognition-and-organisat课件

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