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The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020

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Page 1: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

The Man(ager) Who Knew Too Much

SnehalBanerjeeUCSanDiego

JesseDavisUNCChapelHill

NaveenGondhiINSEAD

March2020

Page 2: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Asymmetric Information and Communication within firms Firmsoperateunderincompleteandasymmetricinformation:

- “ontheground”employeesproduceinformationaboutfundamentalse.g.,productdemand,operationalconstraints,newtechnologies

- decisionsaremade“atthetop”byexecutivesandowners

Communicationofinformationiskeytoperformance

Economicsresearchhasfocusedonhowmisalignedincentives,contractualincompletenessandorganizationalfrictionslimiteffectivecommunication…

Page 3: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

…buthasignoredaubiquitousregularityexhibitedbyindividuals,withprivilegedinformation:

“thecurseofknowledge”

Page 4: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

The Curse of Knowledge Informedexhibitthe“curseofknowledge”whenforecastingothers’beliefs

Hardtoaccountforthefactthatyoudon’tknowwhatIknow

Examples:– Evaluatingforecastersafteracrisis:“shouldhaveseenitcoming”– Ineffectiveteacherswhobelieve“simple”topicsshouldbe“obvious”

Pervasiveandrobustcognitivebiasinforminghigher-orderbeliefs

Expertsarepoorcommunicators:assumeconclusionsare“trivial”

– Teachers,Politicians,Scientists,Economists

Page 5: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

What we do Modelofintra-firmcommunicationtostudyhow“curseofknowledge”affects

(i)communication,(ii)informationproduction,and(iii)controlrights

Firmconsistsofprincipalandmanager(agent)

– Managerexertsefforttoacquireinfoaboutprojectproductivity– Managerwantstoover-investinprojectandexhibitscurseofknowledge– Managercommunicateswithprincipal– Principalchoosesinvestment

Communication:costlycommunicationvscheaptalk

ControlRights:Delegationvs.Communication

Page 6: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

What we find (1)Thecurseofknowledgehamperscommunication

Lesseffortforcostlycommunication,lessinformativecheaptalk

(2)Thecurseofknowledgeimprovesinformationproduction

Moreeffortexertedforinformationacquisition

(3)Acursedmanagercanincreasefirmvalue

• Especially,whenincentivesarewell-aligned,andcurseisnottoolarge• Themanagercanproducerelevantinformation(e.g.,R&D,consultants)• Theprincipalmaychoosetodelegatetoacursedmanager,butnottoanunbiasedone

Page 7: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Background and Motivation

Page 8: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Perspective taking and the Curse of Knowledge Perspectivetakingor“puttingyourselfinsomeoneelse’sshoes”

Perceivingasituation/understandingaconceptfromanotherperspective

- Perceptual:Visual,Auditory- Conceptual:Thoughts,feelings,attitudes

Criticalforsocialinteractionsandcommunication

Whenforminghigher-orderbeliefs,peoplefinditdifficultto:

• Imagineothersknowwhatwedon’tknow(canleadto“winner’scurse”)• Imagineothersdon’tknowwhatweknowi.e.,“curseofknowledge”CurseofKnowledge≈“hindsightbias”and“knewitallalongeffect”

Page 9: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Examples

ForthoseofuswhorememberlifebeforeGoogleMaps!

Page 10: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Curse of knowledge is ubiquitous and difficult to correct Evaluatingforecasts/diagnoses/liability• Hastie,Schkade,andPayne(1999):Jurorsdecidingnegligence• Anderson,Jennings,Lowe,andReckers(1997):Judgesevaluatingauditors• Arkes,Wortmann,Saville,&Harkness(1981):Physiciansover-estimatelikelihoodof“known”outcome,eventhoughex-anteunlikely

• Kennedy(1995):Auditorsover-estimateabilitytopredictbankruptcyex-post

Expertsarepoorcommunications• Politicians,Scientists,Economists• Teacherswho“movetoofast”“skipsteps”“toomuchmaterial”“notclearenough”• Over-estimatemessageinformativeness/abilityofaudience• Particularlybadatforecastingperformanceofnovices(e.g.,Hinds,1999)

Debiasing:Warnings,Feedback,Accountabilityhavelimitedimpact

Page 11: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

(Some) Related Literature Relativelysmallliteratureineconomicsexploring“curseofknowledge”

Camerer,LoewensteinandWeber(1993):exploreeffectsinamarketsetting

• ArgueCoKcanalleviatethelemon’sproblem,andenhancetradeMadarasz(2011):Considerssettingwherereceiverhascurseofknowledge

• Receiverevaluatesexpertsusingex-postinfo,underestimatesability• Communication:Biasedexpertspeakstoorarelyandtechnically,underestimatesabilityofaudience

Broadlyrelatedtolargeliteratureonstrategiccommunication,disclosure,delegation

Page 12: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

The Model

Page 13: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

A definition SupposeAdamisbetterinformedthanBethi.e.,info.set𝐼!isfinerthan𝐼"

AdamexhibitsthecurseofknowledgeifhispredictionofBeth’sforecastis

𝐸!"𝐸"[𝑋]& = (𝟏 − 𝝎)𝐸[𝑋|𝐼"] + 𝝎𝐸[𝑋|𝐼!]

i.e.,Adamcannotignoretheinformationheknows(butBethdoesn’t)

Parameter𝜔 ∈ (0,1)characterizesthedegreeofthecurseofknowledge

Note:LawofIteratedExpectations:𝜔 = 0 ⇔𝐸![𝐸"[𝑋]] = 𝐸[𝑋|𝐼"]

Page 14: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Setup 1 – Firm Payoffs Firmvalueis

𝑉(𝑅, 𝑘) = 𝑅𝑘 −12𝑘#

where𝑘representsinvestment(capital)and𝑅reflectsproductivity:

Productivityisgivenby𝑅 = 𝜇 + 𝜃:

– 𝜇ispriorexpectedproductivity(known)and

– 𝜃 ∼ 𝑈 <− $#, $#=isalearnableshocktoproductivity

Giveninformationset𝐼,optimalactionisconditionalexpectationof𝑅i.e.,

𝑘∗ = 𝐸[𝑅|𝐼] = 𝜇 + 𝐸[𝜃|𝐼]

Page 15: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Setup 2 – Preferences and Beliefs (1)Principal(𝑝)wantstochooseinvestmenttomaximizefirmvaluei.e.,

𝑚𝑎𝑥&𝐸B𝑉(𝑅, 𝑘)C𝐼'D ⇒ 𝑘' = 𝜇 + 𝐸[𝜃|𝐼']

(2)Manager(𝑚)hasabias𝒃 ≥ 𝟎towardsover-investmenti.e.,

𝑚𝑎𝑥&𝐸[𝑉(𝑅, 𝑘) + 𝑏𝑘|𝐼(] ⇒ 𝑘( = 𝜇 + 𝐸[𝜃|𝐼(] + 𝑏

andexhibitscurseofknowledgei.e.,if𝐼(isfinerthan𝐼',then

𝐸'B𝐸([𝜃]D = (𝟏 − 𝝎)𝐸B𝜃C𝐼'D + 𝝎𝐸[𝜃|𝐼(]

Page 16: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Setup 3 – Information Managercanpayacost𝑐(𝑝)toproduceasignal𝑥ofprecision𝑝:

𝑥 = M𝜃, withprobability𝑝𝜉, withprobability1 − 𝑝

where𝜉 ∼ 𝑈 <− $#, $#=isindependentof𝜃 “truthornoisesignal”

Note:Weareabstractingawayfromcommonlyknowninformation

Communication:Managersendsmessage𝑑(𝑥),principalinvests𝑘'(𝑑)

- Costlycommunicationwithcommitment- Cheaptalk

Delegation:Managerinvests𝑘((𝑥)

Page 17: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Costly Communication

Themanagercommitsex-antetoanoisymessage𝑦about𝑥withmessage

precision𝜌bypayingacost𝜅(𝜌):

𝑦 = M𝑥, withprobability𝜌𝜂, withprobability1 − 𝜌

where𝜂 ∼ 𝑈 <− $#, $#=isindependentof𝑥

Managerchoosesmessageprecision𝝆andinformationprecision𝒑tomaximize:

𝑚𝑎𝑥),'

𝐸([𝑉(𝑅, 𝑘'(𝑦)) + 𝑏𝑘'(𝑦)] − 𝑐(𝑝) − 𝜅(𝜌)

Page 18: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Cheap talk Managercannotcommittoasignalex-ante

Instead,hesendsacheaptalkmessage𝒅(𝒙)tomaximize:

𝑢((𝑥) ≡ 𝑚𝑎𝑥+𝐸([𝑉(𝑅, 𝑘'(𝑥)) + 𝑏𝑘'(𝑑)|𝑥]

andoptimallychoosesinformationprecision𝒑tomaximize:

𝑚𝑎𝑥'𝐸'[𝑢((𝑥)] − 𝑐(𝑝)

Page 19: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Modelisstylizedfortractability(signalstructure,linear-quadraticvalue,…)

Interpretation:

1.Managerandprincipalstartwithsomecommoninformation(context)

2.Managerproducescostly,incremental,privateinformation𝑥withprecision𝑝

3.Managersendsareport𝑑(𝑥)

• Costlycommunication:𝜌reflectshowwellreport“makesthecase”• “showingthesteps”/makingthecaserequireseffort

• Cheaptalk:credibilityofthereport

CurseofKnowledge:

Managerover-estimateshow“obvious”𝑥is,givencontext/commoninfo

Page 20: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Communication

Page 21: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Costly Communication Optimalmessageprecision𝜌maximizes𝑢e((𝜌) − 𝜅(𝜌),where

𝑢e((𝜌, 𝑝) ≡ 𝐸(B𝑉f𝑅, 𝑘'(-)g + 𝑏𝑘'(𝑝)D@

= 𝑏𝜇 +𝜇#

2 +𝜎#

24𝑝#f1 − (1 − 𝜔)#(1 − 𝜌#)g

• Utilityincreasesinmessageprecision𝜌andinformationprecision𝑝

Moreprecisecommunication/informationincreasesfirmvalue

• MarginalutilityofcommunicationincreasesinsignalprecisionCommunicationandinformationacquisitionarecomplements

Page 22: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result 1: Under-investment in Communication Result:Cursedmanagerunder-investsincommunicationprecision𝜌

𝜕#𝑢e(𝜕𝜌𝜕𝜔

=𝜕𝑀𝑈(𝜌)𝜕𝜔

< 0

Intuition:

“therightanswerisobviousfromcontext,

soIdon’tneedtoexplainitbetter!”

Consistentwithnarrativeaboutexpertsbeingpoorcommunicators

- Reportsandpresentationsareunclearandfilledwithjargon- Assumethataudiencehas“readthepaper/textbook”

Page 23: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Communication without costs

Curseofknowledgehamperscostlycommunication,butwhatiftalkischeap?

Page 24: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

WeshowthereisaCrawford-Sobel,partitionequilibrium:

Apartitionequilibriumwith𝑁partitionsconsistsofcutoffs−𝜎/2 = 𝑠! < 𝑠" <. . . 𝑠# =

𝜎/2,suchthatforall𝑥 ∈ [𝑠$%", 𝑠$],(i)themanagersendsthesamemessage𝑑(𝑛),and

(ii)theprincipaltakesthesameaction𝑘&(𝑛) = 𝜇 + 𝐸[𝜃|𝑥 ∈ [𝑠$%", 𝑠$]]

Result:Thereexistsapositiveinteger

𝑁!"# = ceil#−12+12(1 + 2

𝝈𝒑(𝟏 −𝝎)𝒃 0

suchthatforevery1 ≤ 𝑁 ≤ 𝑁(/0 ,thereexistsapartitionequilibriumwith𝑁

partitions.

Note:When 𝒃𝝈𝒑(𝟏5𝝎)

> 78,thentheonlyequilibriumisuninformative.

Page 25: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Credibility and the effective bias

Credibility/Informativenessdependsoneffectivebias:

𝒃𝝈𝒑(𝟏 − 𝝎)

Theeffectivebias:

• Increasesinover-investmentbias𝒃 strongerincentivestomislead

• Decreasesinprioruncertainty𝝈 communicationismorevaluable

• Decreasesininformationprecision𝒑 cheaptalkhasmorecontent

Page 26: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result 2: Curse of knowledge ⇒ Less credible cheap talk

Result:Theeffectivebiasincreaseswiththecurseofknowledge𝜔

Intuition:

“therightanswerisobviousfromcontext,soIhaveastrongerincentivetodistortmyreport!”

Curseofknowledgeharmscommunication,evenwhenitrequiresnoeffort!

Page 27: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Increasein𝜔reducesinformativenessthroughchannels:

(i)Decreasesthemaximalnumberofpartitions

𝑁'() = ceil9−12 +

12;1 + 2

𝜎𝑝(1 − 𝜔)𝑏 ?

(ii)Forafixednumberofpartitions𝑁,inducesmorepoolingatthetop

Page 28: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Information Production

Sincecurseofknowledgehamperscommunication,doesitmakeinformation

lessvaluable?

Page 29: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result 3: Over-investment in information acquisition Withcostlycommunication,expectedutilityis:

𝑢e( ≡ 𝑏𝜇 +𝜇#

2 +𝜎#

24𝑝#f1 − (1 − 𝜔)#(1 − 𝜌#)g

Result:Cursedmanagersover-investininformationproductioni.e.,

𝜕#𝑢e(𝜕𝑝𝜕𝜔

=𝜕𝑀𝑈(𝑝)𝜕𝜔

> 0

Intuition:

“SinceI’mgoodatconveyingtherightanswer,

doingresearchismorevaluable”

Over-estimateabilitytocommunicate⇒strongerincentivetoexerteffort

Page 30: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result 4: A cursed manager can increase firm value Expectedvalueincreasesinbothinformationprecisionandmessageprecision

𝐸[𝑉(𝑅, 𝑘&)] =𝜇*

2 +𝜎*

24𝑝*𝜌*

• Fixedinformationprecision𝑝:cursedmanagersdecreasevalue• Endogenousinfo.precision𝑝:cursedmanagerscanincreasevalue

when𝜔isnottoohigh

Figure 3: Optimal communication and information acquisition under costly communicationThe figure plots the choice of message precision ⇢ (dashed), acquired information precisionp (solid), and expected value of the firm E [V (R, k)] under costly communication, as afunction of the degree of cursedness. The manager optimally chooses message precision ⇢subject to a cost (⇢) = 0

⇢2

1�⇢ and chooses acquired information precision p subject to a

cost c (p) = c0p2

1�p . The other parameters of the model are set to: µ = 1, b = 0.02, � = 1and c0 = 0.01 and 0 = 0.001.

0.2 0.4 0.6 0.8 1.0

0.1

0.2

0.3

0.4

0.5

0.6

0.2 0.4 0.6 0.8 1.00.500

0.501

0.502

0.503

0.504

(a) ⇢ (dashed) and p (solid) vs ! (b) Expected value vs !

c (p) = c0p2

1�p , as before, we assume that the manager’s cost of communicating with precision

⇢ is give by (⇢) = 0⇢2

1�⇢ . The figure plots the optimal choice for both precisions, ⇢ (dashed)

and p (solid), and the expected firm value as a function of the manager’s curse of knowledge,

!.

In this example, there is very little information acquisition or communication when the

manager is rational (i.e., when ! = 0). As ! increases, the marginal utility from information

acquisition increases and the marginal utility from message precision decreases. When ! is

su�ciently large (when ! ⇡ 0.1 in panel (a)), however, this is o↵set by the complementarity

across precisions and so both p and ⇢ quickly increase with !. As panel (b) illustrates, this

leads to an increase in the firm’s expected value since the manager is now acquiring and

communicating more precise information. Notably, this suggests that small changes in the

manager’s bias can lead to large changes in informational e�ciency and firm value.

As the curse of knowledge increases further, the o↵setting impact of the complementarity

begins to dissipate: eventually (when ! ⇡ 0.2) this leads to (i) higher information acquisition,

and (ii) lower message precision. Note that, even in this region, the expected value of the

firm continues to rise, until the rate at which the message precision decreases outweighs the

informativeness of the manager’s signal (near ! ⇡ 0.3). Eventually, the manager’s curse

of knowledge is su�ciently high to drive the optimal choice of message precision to nearly

zero. From this point forward, the expected value of the firm remains relatively insensitive

21

Figure 3: Optimal communication and information acquisition under costly communicationThe figure plots the choice of message precision ⇢ (dashed), acquired information precisionp (solid), and expected value of the firm E [V (R, k)] under costly communication, as afunction of the degree of cursedness. The manager optimally chooses message precision ⇢subject to a cost (⇢) = 0

⇢2

1�⇢ and chooses acquired information precision p subject to a

cost c (p) = c0p2

1�p . The other parameters of the model are set to: µ = 1, b = 0.02, � = 1and c0 = 0.01 and 0 = 0.001.

(a) ⇢ (dashed) and p (solid) vs ! (b) Expected value vs !

c (p) = c0p2

1�p , as before, we assume that the manager’s cost of communicating with precision

⇢ is give by (⇢) = 0⇢2

1�⇢ . The figure plots the optimal choice for both precisions, ⇢ (dashed)

and p (solid), and the expected firm value as a function of the manager’s curse of knowledge,

!.

In this example, there is very little information acquisition or communication when the

manager is rational (i.e., when ! = 0). As ! increases, the marginal utility from information

acquisition increases and the marginal utility from message precision decreases. When ! is

su�ciently large (when ! ⇡ 0.1 in panel (a)), however, this is o↵set by the complementarity

across precisions and so both p and ⇢ quickly increase with !. As panel (b) illustrates, this

leads to an increase in the firm’s expected value since the manager is now acquiring and

communicating more precise information. Notably, this suggests that small changes in the

manager’s bias can lead to large changes in informational e�ciency and firm value.

As the curse of knowledge increases further, the o↵setting impact of the complementarity

begins to dissipate: eventually (when ! ⇡ 0.2) this leads to (i) higher information acquisition,

and (ii) lower message precision. Note that, even in this region, the expected value of the

firm continues to rise, until the rate at which the message precision decreases outweighs the

informativeness of the manager’s signal (near ! ⇡ 0.3). Eventually, the manager’s curse

of knowledge is su�ciently high to drive the optimal choice of message precision to nearly

zero. From this point forward, the expected value of the firm remains relatively insensitive

21

Page 31: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result 3’: Cursed managers acquire more information

Withcheaptalk,expectedutilityis:

𝑢e( ≡12𝐸(

r𝐸([𝑅 + 𝑏|𝑥]# − s𝐸([𝑅 + 𝑏|𝑥] − 𝑘'f𝑑(𝑥)gt#u

Result:Holding𝑏fixed,utilityincreasesinperceivedinformativenessof𝑑

• Utilityincreasesintrueinformativenessi.e.,𝑁and𝑝

• Utilityincreaseswithcurseofknowledge𝜔

• Forafixed𝑁,acursedmanageracquiresmoreinformationi.e.,

𝜕#𝑢(𝜕𝑝𝜕𝜔

=𝜕𝑀𝑈(𝑝)𝜕𝜔

> 0

Page 32: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Fixing𝑁,asmallincreasein𝜔 ⇒increaseinformationprecision

But,sufficientlylargeincreasein𝜔 ⇒decreasein𝑁(/0andinfo.precision

Sawtoothpatterninoptimallychoseninformationprecision

Page 33: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result 4’: Cursed managers can increase firm value Expectedvalueincreaseswithinformativenessandcommunication:

𝐸[𝑉(𝑅, 𝑘'(𝑑)] = 7#(𝜇# + var(𝜃) − 𝐸[var(𝜃|𝑑)])

• Decreaseswithcurseofknowledgewithexogenousinformationprecision• Canincreasewithcursewheninfoprecisionisendogenous

Whencurse𝜔isnottoolarge,infoproductiondominatescommunication

Page 34: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Delegation versus Communication

Page 35: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result 5: Delegate if and only if bias is sufficiently small

Result:Withexogenousinformation/messageprecision,thedelegation

decisiondoesnotdependonthecurseofknowledge

• Costlycommunication:Delegateiff

𝑏# <𝑝#𝜎#(1 − 𝜌#)

12

• Cheaptalk:Delegateiff

𝑏# <𝑝#𝜎#

12

Note:Commitmentimprovescomm⇒Delegatemoreoftenwithcheaptalk

Page 36: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Withendogenousprecisionchoice,curseofknowledgeaffectsinfoprecision,andsoaffectsdelegationdecision

Theprincipalmayretaincontrolwitharationalmanager(𝜔 = 0),butdelegatetoacursedone

CostlyCommunication CheapTalk

Otherparameters:𝜇 = 1, 𝜎 = 1, 𝑐! = 0.02, 𝜅! = 0.001

Page 37: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Extension: Verifiable Disclosure “Intermediatecase”betweencheaptalkandcommitment

Result:“Disclosureontop”versus“Disclosureatextremes”

(i) When 9$'(75:)

≥ 𝐵∗,managerdisclosesiff𝑥 ≥ 𝑥

(ii) When 9$'(75:)

< 𝐵∗,managerdisclosesiff𝑥 ≥ 𝑥OR𝑥 ≤ 𝑥

Result:Forfixedprecision𝒑,curseofknowledgereducesdisclosureandvalue

Result:Fixingequilibrium,higher𝝎canleadtohigherinformationacquisition,sovalueisnon-monotonicwith𝝎

Result*:Delegationdecisiondependson𝝎,evenwithfixed𝒑,fordisclosureatextremesequilibrium

Page 38: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Conclusions

Isthecurseofknowledgereallyacurseorablessing?

Page 39: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Asymmetricinformation⇒Curseofknowledge

Cursedmanagersreducefirmvaluewhen:

– Informationprecisionisexogenous(e.g.,reporting,riskmanagement?)

– Incentivesaremisaligned

– Informalinternalcommunication(lackofcommitment)

Cursedmanagerscanincreasefirmvaluewhen:

– Informationprecisionisendogenous(e.g.,marketresearch,R&D)

– Curseisnottoolargeandincentivesarewell-aligned

– Formalcommunicationsystems(abilitytocommit)

Enhancing/fosteringcommunicationencourageseffortinexpertise

Page 40: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Cursed Academics? Curseofknowledgeprovidesoneexplanationforwhymanyexcellentresearchersarealsobadatteaching– Morecursedacademicsoverinvestinexpertise,underinvestinteaching

Analysishighlightscomplementarityinresearchandteaching

Justlikeyourdeansays!

Encouragingbetterteachingpracticesmayimproveresearchproductivity

– Whatdoesn’twork:Warnings,feedback,experience– Whatdoeswork:Consideringalternativeoutcomes(counter-explanation)

Page 41: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Thank you!

Page 42: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Extension: Verifiable Disclosure

Page 43: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Suppose𝑚observes𝑥withprobability𝑞,andnothingotherwise

– Aninformedmanagercanchoosewhethertodisclosenothing(𝑑 = ∅)or

disclosethesignalperfectly(𝑑 = 𝑥)

– Anuninformedmanagercannotverifiablydiscloseheisuninformed

Denotetheequilibriumbeliefafterno-disclosureby𝜇∅ = 𝐸[𝜃|𝑑 = ∅].

Optimalinvestmentbyprincipal:

𝑘'(𝑑) = M𝜇 + 𝑝𝑥, if𝑑 = 𝑥𝜇 + 𝜇∅, if𝑑 = ∅

But,manager’scurseofknowledgeimplies:

𝐸([𝑘'(𝑑)|𝑥, 𝑑 = ∅] = 𝜇 + (1 − 𝜔)𝜇∅ + 𝜔𝑥

Page 44: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result:Let𝐵∗ = %&'('(&'()+(

.

1. If ,-.(&'/)

> 𝐵∗,thenthereexists𝑥 ∈ (−𝜎/2, 𝜎/2),suchthatthemanagerdisclosesiff𝑥 ≥ 𝑥

(“disclosureontop”)

2. If ,-.(&'/)

≤ 𝐵∗,thenthereexists�̄� < 𝑥 ∈ (−𝜎/2, 𝜎/2),suchthatthemanagerdisclosesiff𝑥 ≥

𝑥or𝑥 ≤ �̄�(“disclosureatextremes”)

Intuition:

benefitsfromhigherinvestment(nodisclosure)

vs.

highervaluefrommoreinformedinvestment(disclosure)

Whenbiasissufficientlylarge,wehavestandard,disclosureontop

Whenbiasissmall,wehavedisclosureatextremes

• Smallerbias⇒informedinvestmentdominateswhenxissufficientlylow

• Smallerbias⇒Disclosureismoreinformative(fulldisclosurewhenb=0)

Page 45: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Impact of curse of knowledge on expected value

Effectivebias 9$'(75:)

increaseswiththecurseofknowledge

• Whenbiassufficientlylow,disclosureregiondecreaseswith𝜔• Whenbiassufficientlyhigh,disclosureregionisinsensitiveto𝜔

Intuition:Cursedmanagerbelievesrightansweris“obvious”

⇒incentivetowithholddisclosureishigher

Result:Forfixedsignalprecision𝒑,expectedvalueis:

• unaffectedby𝜔for“disclosureontop”equilibrium• decreasingin𝜔for“disclosureatextremes”equilibrium

Page 46: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Endogenous Information Acquisition

Result:Holding𝒃fixed,utilityincreasesinperceivedinformativenessof𝒅:

• Utilityincreaseswithtrueinformativeness(i.e,𝑝)

• Utilityincreaseswiththecurseofknowledge𝜔

Forfixedequilibrium,utilityincreasesmorewith𝑝forcursedmanageri.e.,

𝜕#𝑢(𝜕𝑝𝜕𝜔

> 0

Moreover,utilityishigherforthedisclosureatextremesequilibrium

Aswithcheaptalk,anincreaseincursednesscanincreasefirmvalueby

increasinginformationacquisition!

Page 47: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Cursed managers may increase firm value Forfixedprecision,curse(weakly)decreasesvaluevialessdisclosure

But,italsoincreasesmarginalvalueofinformationprecision,whichcanincreaseinformationproduction

Page 48: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information

Result:Delegationdecisiondependsoninformativenessofequilibriumdisclosure:

(i) Forthe“disclosureontop”(lessinformative)equilibrium,delegateiffover-investmentbiasissufficientlysmall

(ii) Forthe“disclosureatextremes”(moreinformative)equilibrium,delegateiffcurseofknowledgeissufficientlylarge

Disclosureontopequilibrium:

Forfixed𝑝,delegationdecisionindependentofcurseofknowledge𝜔(likecheaptalk)

Disclosureatextremesequilibrium:

Forfixed𝑝,disclosureintervaldependson𝜔,andsodoesdelegationdecision

Page 49: The Man(ager) Who Knew Too Much · The Man(ager) Who Knew Too Much Snehal Banerjee UC San Diego Jesse Davis UNC Chapel Hill Naveen Gondhi INSEAD March 2020 Asymmetric Information