PERSUASION OF A PRIVATELY INFORMED RECEIVER
Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

Abstract. We study Bayesian persuasion in linear environments with a privately
informed receiver. We allow the sender to condition information provided to thereceiver on the receiver’s report about his type (private persuasion). We describe
implementable outcomes, establish equivalence between public persuasion studied
in the prior literature and private persuasion, and draw connections with the standard linear mechanism design with transfers. We also characterize optimal persuasion rules, establish monotone comparative statics, and consider several applications, such as a competitive market for a good with consumption externalities(e.g., cigarettes) in which a benevolent government designs an educational campaign about a payoff-relevant characteristic (e.g., the health risks of smoking).
JEL Classification: D81, D82, D83
Keywords: information disclosure, Bayesian persuasion, screening, mechanism
design without transfers, advertising, certification, mass media

Date: February 21, 2015.
Kolotilin: School of Economics, University of New South Wales, Sydney, NSW 2052, Australia.
E-mail: akolotilin@gmail.com.
Li: Department of Economics, Concordia University and CIREQ. E-mail: ming.li@concordia.ca.
Mylovanov : University of Pittsburgh, Department of Economics, 4714 Posvar Hall, 230 South
Bouquet Street, Pittsburgh, PA 15260, USA. E-mail: mylovanov@gmail.com.
Zapechelnyuk : Adam Smith Business School, University of Glasgow, University Avenue, Glasgow
G12 8QQ, UK. E-mail: azapech@gmail.com.
The authors would like to express their gratitude to Navik Kartik for the very helpful comments and discussion at the Boston ASSA meetings. We are also grateful to Ricardo Alonso, Dirk
Bergemann, Patrick Bolton, Alessandro Bonatti, Rahul Deb, Péter Esö, Johannes Hörner, Florian
Hoffman, Roman Inderst, Emir Kamenica, Daniel Kräehmer, Marco Ottaviani, Mallesh Pai, Ilya
Segal, Larry Samuelson, and Joel Sobel for their excellent comments.
1

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Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

1. Introduction
A sender wishes to manipulate a receiver’s beliefs about an optimal action, butthere is uncertainty about the receiver’s preferences. This is a Bayesian persuasion
environment à la Kamenica and Gentzkow (2011) and Rayo and Segal (2010),1 witha novelty that the receiver has private information (type) about his tastes. Numerous natural applications fit this environment: public educational campaigns, optimal
certification policies of financial and consumer products, media censorship regulations, informational lobbying, etc. (Some of the applications are discussed in detailin Section 6.)
By way of motivation, consider a government that would like to persuade the public
to reduce the amount of public smoking by commissioning an educational campaign
about the health risks of cigarettes. What is the optimal way to conduct the educational campaign? Should the government target different consumers by providingthem with different information or is it optimal to provide the same information to
everyone? When should the government withhold information from the public? How
does the distribution of tastes among the consumers affect the amount of information
released by the government?
In our model, the receiver has a binary choice: to act or not. His utility difference
between action and inaction depends on his private type and an uncertain state, and
it is additively separable. The sender is biased and might wish the receiver to act
in situations where a fully informed receiver would have not. The sender chooses
any mechanism that sends messages depending on the type reported by the receiverand the realized state. The receiver makes a report, observes a message from themechanism, and then takes an optimal action based on his updated beliefs about thestate. Thus, the sender can screen different types of receivers by targeting information
disclosure to the receiver’s reports. We call this model private persuasion.
The important benchmark is public persuasion in which the sender designs a public
signal about the state that is independent of the receiver’s report. The literature on
Bayesian persuasion focuses on the environments in which the receiver does not haveprivate information and, hence, private persuasion is not relevant. Public persuasion with private information is considered in Rayo and Segal (2010), Kamenica and
Gentzkow (2011), and Kolotilin (2014).
The first result of the paper is equivalence of private and public persuasion. The
result is based on an elegant connection between Mirlees integral representation of
incentive-compatibility (Mirrlees 1971) and Blackwell integral representation of garbling (Blackwell 1953). First, using the integral envelope representation of the incentive compatibility we characterize the set of receiver’s utility profiles implementableby private persuasion. It consists of every convex function bounded by the utility
profiles obtained if the sender reveals the state (full disclosure) and if the senderreveals nothing (no disclosure). Second, using Blackwell integral representation of1

We study the environment of Kamenica and Gentzkow (2011); our results do not apply to theenvironment in Rayo and Segal (2010).

PERSUASION OF A PRIVATELY INFORMED RECEIVER

3

garbling we observe that every convex function bounded by full-disclosure and nodisclosure utility profiles can be obtained by a public signal that is a garbling of the
fully revealing signal, establishing equivalence.
The equivalence result tells us that screening the receiver’s type by conditioning
messages on the receiver’s reports does not expand the set of implementable utility
profiles. The result is non-obvious because different types of the receiver have different preferences over disclosed information: The ideal signal for the receiver reveals
whether his utility difference between action and inaction is positive or negative,
which varies with the receiver’s type. The equivalence result will fail if in addition to
information disclosure a mechanism can use transfers (c.f. Bergemann, Bonatti, andSmolin (2014) and Krähmer and Mylovanov (2014)).
The equivalence has implications for optimality of public persuasion for the senderin our model. We consider the sender’s payoff to be a weighted sum of the probabilityof acting and the utility of the receiver. By incentive compatibility, the probabilityof acting is equal to receiver’s marginal utility. Hence, the sender’s payoff can be
expressed in terms of the receiver’s utility profile, which together with the equivalence
result establishes that public persuasion is optimal.
Our second result is the characterization of optimal persuasion mechanisms andcomparative statics. A public signal is called upper censorship if it sends the message
equal to the realized value of the state whenever it is below some threshold, andsends no message otherwise. In the latter case, the receiver infers only that the stateis above the threshold. Upper censorship is optimal if the probability density of thereceiver’s types is logconcave—the condition which is satisfied for most densities used
in applications.2
The structure of the optimal mechanism underscores the effect of privacy of thereceiver’s information. As an example, assume that the sender would like to maximize
the probability of action. If the receiver’s preferences were public, the optimal (public)mechanism would be a signal that reveals whether the state is above or below some
cutoff. The cutoff is designed to make the receiver indifferent to act if the news isgood (i.e., the state is above the cutoff) and strictly prefers not to act otherwise. In
this mechanism, the sender offers no value to the receiver, raising the question why
the receiver should employ the sender in the first place.3 By contrast, in our model,
privacy of the type generates information rents for the receiver.
Upper censorship provides more information than cutoff mechanisms (which are
optimal in the benchmark environment with publicly known type of the receiver). It
also resembles some of the persuasion mechanisms observed in reality. Consider, forexample, the question of optimal certification of financial products.4 Upper censorship
2

Lower censorship, defined symmetrically, is optimal if the probability density of the receiver’s
types is logconvex.
3
Since the receiver weakly prefers not to act regardless of the signal realization, he obtains thesame payoff as from ignoring the signals and taking no action. For this setting, Kolotilin (2015)
derives the comparative statics over the distributions of states of nature and public types of thereceiver.
4
For optimal certification see Lizzeri (1999) and the subsequent literature.

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Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

can be approximated by a coarse ranking, with one high grade that bunches together
good securities (the states above the threshold) and multiple lower grades (the states
below the threshold). The high grade is rather uninformative, but is nevertheless asignal of good enough quality that makes cautious investors buy the security. The
other grades signal negative news, and are pretty informative, inducing purchase froma smaller set of less risk-averse investors.
We establish a comparative statics result that the sender optimally discloses more
information if she puts a higher weight on the receiver’s utility and if economic fundamentals are more biased against the sender. As extreme cases, revealing full information is optimal if the density of the receiver’s type is increasing and revealing no
information can be optimal if the receiver type’s density is decreasing. Thus, privacyof the receiver’s tastes can have an ambiguous result on the amount of information revealed by the sender. Nevertheless, a relatively higher probability of a more skepticalreceiver will tend to force the sender to reveal more information.
In Section 6 we consider three applications to demonstrate versatility of the model.
We start with the design of optimal educational campaigns about the value of a product in competitive markets with positive or negative consumption externalities andconsumer biases. Our second application is the government’s control of the editorial
policies of media outlets. Finally, we consider optimal informational lobbying policyof a politician with uncertain stand on an issue. In these applications, we provide
conditions under which upper censorship policy is optimal and show the relationshipbetween the economic fundamentals and the amount of information revealed by thesender.
We conclude the paper with comparison of our results with those in Johnson andMyatt (2006), and with relating the problem of persuasion by cutoff mechanisms to
the problem of optimal delegation.
Two classes of results are relegated to the Appendix. We provide a structuralcharacterization of optimal public signals for general environments, beyond logconcaveand logconvex density, in Appendix A. We explore the limits of equivalence between
public and private persuasion outside of linear environments in Appendix B. Some
proofs are deferred to Appendix C.
Related literature. Our Bayesian persuasion model is a variation of Kamenica and
Gentzkow (2011), with more structure on the preferences and the action space, anda novelty that the receiver is privately informed.5 We study private persuasion inwhich information revealed to the receiver depends on her reported private information. Public persuasion, in which the information revealed must be identical for all
receiver types, has been covered in Kamenica and Gentzkow (2011, Section VI.A),
Rayo and Segal (2010), and Kolotilin (2014). Kamenica and Gentzkow (2011) provide
a methodological contribution; Rayo and Segal (2010) consider a sufficiently different
5

For recent work on persuasion by disclosure, see, for example, Che, Dessein, and Kartik (2013),
Koessler and Renault (2012), Hagenbach, Koessler, and Perez-Richet (2014), and Hoffmann, Inderst,
and Ottaviani (2014).

PERSUASION OF A PRIVATELY INFORMED RECEIVER

5model, with a more complex preference structure; and Kolotilin (2014) provides analgebraic criterion of optimality for public persuasion mechanisms.
Bayesian persuasion with a privately informed sender is considered in Rayo andSegal (2010), Perez-Richet (2014) and Alonso and Câmara (2014a).6 Competitionin Bayesian persuasion is analyzed in Gentzkow and Kamenica (2012),7 Bayesian
persuasion with costly signals is studied in Gentzkow and Kamenica (2014a), and endogenous acquisition of information in Bayesian persuasion is introduced in Gentzkow
and Kamenica (2014b). Dynamic Bayesian persuasion environments appear in Ely,
Frankel, and Kamenica (2015) and Ely (2014).8 Variations of Bayesian persuasion
with monetary transfers and selling information are studied in Hörner and Skrzypacz(2009), Bergemann and Bonatti (2013), Bergemann, Bonatti, and Smolin (2014),
and Krähmer and Mylovanov (2014). Alonso and Câmara (2014b) explore Bayesian
persuasion without common priors.
Bayesian persuasion is a single-agent (decision) problem. Informational structuresin multi-agent environments are studied in Bergemann and Morris (2013a) and Bergemann and Morris (2013b). Bergemann, Brooks, and Morris (2013) analyze limits ofprice discrimination on the set of all information structures and Bergemann, Brooks,
and Morris (2012) focus on information structures in first price auctions.9 Alonsoand Câmara (2014c) study Bayesian persuasion in voting environments. Bayesian
persuasion with multiple receivers who take a collective action is also considered in
Taneva (2014) and Wang (2013).
2. Model2.1. Setup. There are two players: the sender (she) and the receiver (he). The
receiver takes a binary action: to act or not to act. There are two payoff-relevant
random variables: state of nature ω ∈ Ω = [0, 1] and the receiver’s type r ∈ R =[0, 1]. The state of nature is uncertain, while the type is privately observed by thereceiver. Random variables ω and r are independently distributed with c.d.f. F andG, respectively, where G admits a strictly positive differentiable density g.
The receiver’s payoff is u(ω, r) = ω − r if he acts and is normalised to zero if hedoes not act. The sender’s payoff is v (ω, r) = 1 + ρ (r) u(ω, r) if the receiver acts
and is zero if the receiver does not act, where ρ(r) ∈ R. That is, the sender is biased
towards a = 1, but she also puts a (type-specific) weight ρ(r) on the receiver’s payoff.
In particular, if the weight is very large, then the sender’s and receiver’s interests
are aligned, but if the weight is zero, then the sender cares only about whether thereceiver acts or not. The utility is not transferrable; there are no monetary payments.
6

Perez-Richet and Prady (2012) is a related model with a restricted set of persuasion rules.
Boleslavsky and Cotton (2015a, 2015b) study related models with competing persuaders.
8
See also Kremer, Mansour, and Perry (2014) for an optimal disclosure policy in a dynamic
environment.
9
The literature on optimal informational structures in auctions has been initiated in Bergemann
and Pesendorfer (2007). See Eso and Szentes (2007), Bergemann and Wambach (2013), and Hao
and Shi (2015) for recent contributions.
76Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

The assumption that u(ω, r) = ω − r is without loss of generality if we consider theclass of the receiver’s payoff functions of the form u(ω, r) = b(r) + c(r)d(ω) for somefunctions b, c, and d, with c having a constant sign, as we note in Remark 2. The
boundary conditions u(ω, 0) ≥ 0 and u(ω, 1) ≤ 0 for all ω ∈ Ω allow for elegance ofpresentation; relaxing these assumptions poses no difficulty.
2.2. Private persuasion. In order to influence the decision made by the receiver,
the sender can design a test that asks the receiver to report his private informationand sends a message to the receiver conditional on his report and the realized state.
This is an environment of private persuasion because the test can depend non-triviallyon the information revealed by the receiver.
We are interested in optimal tests and adopt the mechanism design approach. A
private persuasion mechanism (test) π : Ω × R → [0, 1] asks the receiver to reportr̂ ∈ [0, 1] and then provides him with a binary message: for every ω ∈ Ω, it recommends to act (â = 1) with probability π (ω, r̂) and not to act (â = 0) with the complementary probability. A private mechanism is incentive-compatible if the receiverfinds it optimal to report his true type and follow the mechanism’s recommendation.
By the revelation principle, the focus on private incentive-compatible mechanisms
is without loss of generality in that any equilibrium outcome of any game between thesender and the receiver, in which the value of ω is disclosed in some way to the receiver,can be replicated by an incentive-compatible private mechanism. In particular, therestriction that the mechanism returns a binary recommendation about action insteadof messages about the state is without loss since action is binary.
2.3. Public persuasion. Under public persuasion, messages are independent of reports of the receiver, so all types of the receiver are informed identically. A public
persuasion mechanism σ : Ω → ∆(M ) sends to the receiver a randomized message
distributed according to measure σ(ω) for each realized state ω, where M denotes amessage space.
For a given public signal σ, each message m induces a posterior belief of the receiver
about the distribution of states, and hence the posterior value of state E[ω|m]. As
in Kamenica and Gentzkow (2011), without loss assume that the set of messages is
M = Ω = [0, 1] and that messages are direct, in the sense that they inform the receiver
about the posterior value of the state, m = E[ω|m].
Observe that every public signal σ is identical to mechanism π defined byhiπ(ω, r) = Pr m ≥ r m ∈ Supp(σ(ω)) .
(1)
2.4. Interpretation: Continuum of receivers and ex-post implementation.
In some applications, it can be useful to think about a population of heterogeneousreceivers of mass one parametrized by the privately known preference parameter r
distributed according to G. Under private persuasion, the receivers report their types
to the sender and then receive private recommendations from the mechanism. Underpublic persuasion, all receivers observe the same public message about the state.

PERSUASION OF A PRIVATELY INFORMED RECEIVER

7

By definition (c.f. (1)), an outcome of public persuasion can be achieved througha private mechanism. Furthermore, in this private mechanism, the incentive compatibility and obedience constraints are satisfied ex-post, even after the receiver learns
the entire profile of recommendations that would be made to other types. The reverse
statement is true as well: if a private mechanism is ex-post incentive-compatible andobedient, then it can be implemented through public persuasion.
3. Envelope characterization of incentive compatibility
Denote the expected payoff of a receiver of type r who reports r̂ and takes actions
a0 and a1 in {0, 1} after recommendations â = 0 and â = 1, respectively, byZ
Uπ (r, r̂, a0 , a1 ) :=(a0 (1 − π(ω, r̂)) + a1 π(ω, r̂)) (ω − r)dF (ω).
Ω

The expected payoff of the truthful (r̂ = r) and obedient (a0 = 0 and a1 = 1) receiveris equal toZπ(ω, r)(ω − r)dF (ω).

Uπ (r) := Uπ (r, r, 0, 1) =ΩWe consider mechanisms that satisfy the incentive compatibility constraint
Uπ (r) ≥ Uπ (r, r̂, a0 , a1 ) for all r, r̂ ∈ R, a0 , a1 ∈ {0, 1}.

(IC)

It is convenient to introduce the notation for the expected payoff of the obedientreceiver, who makes report r̂ and then obeys the recommendation of the mechanism:
Uπ (r, r̂) := Uπ (r, r̂, 0, 1) = pπ (r̂) − qπ (r̂) r,
whereZ
qπ (r̂) =Z
π(ω, r̂)dF (ω)

and pπ (r̂) =Ωωπ(ω, r̂)dF (ω) .
ωWith this representation of the payoff function we can draw the parallel to the standard linear mechanism design problem with transfers, where r is a private value,qπ (r̂) is the probability of transaction and pπ (r̂) is the expected monetary transfer
that depend on report r̂. The classical envelope argument yields the following lemma:
Lemma 1. A mechanism π is incentive-compatible if and only if
Z 1
Uπ (r) =qπ (s)ds,(2)r

Uπ (0) = E[ω],qπ is non-increasing.
Proof. The proof is in the Appendix.

(3)
(4)


Interestingly, the obedience constraints for the intermediate types are implied by
the obedience constraints for the boundary types and incentive compatibility, Uπ (r) ≥Uπ (r, r̂). To disobey by ignoring the recommendation, that is, to act (not to act)
irrespective of what is recommended, is the same as to pretend to be the lowest
type r̂ = 0 (the highest type r̂ = 1, respectively). To disobey by taking the opposite

8Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

action to the recommended one is never beneficial due to monotonicity of the receiver’s
utility.
In our model, there are no transfers and there are obedience constraints insteadof individual rationality constraints. These differences between our and the standard
environment with transfers translate in the following differences in characterization.
First, there are two boundary conditions, q(0) = 1 and q(1) = 0:(a) Since ω ≥ 0, we have ω − r ≥ 0 for all ω if r = 0. Hence, type 0’s payoff ismaximized by taking action for any belief about the state, implying q(0) = 1
and the payoff equal to the expected state. This is (3).
(b) Since ω ≤ 1, we have ω − r ≤ 0 for all ω if r = 1. Hence, type 1 never acts,
implying q(1) = 0 and U (1) = 0. This is (2), with r = 1.
Second, monotonicity of q’s and boundary conditions q(0) = 1 and q(1) = 1 arenot sufficient for incentive compatibility. Conditions(2) and (3) imply that the area
R1
under q must be equal to the expected state, 0 qπ (s)ds = E[ω].
Finally, not every pair (q, U ) that satisfies conditions (2)–(4) is feasible, that is, amechanism π that implements such a pair need not exist. For example,
R 1 if ω = 1/21
with certainty, then every monotonic q with q(1) = 0, q(0) = 1, and 0 q(r)dr = 2
satisfies (2)–(4). Among these functions, the unique feasible q is q(r) = 1 if r ≤ 1/2
and q(r) = 0 otherwise.
4. Equivalence of private and public persuasion
4.1. Bounds on the receiver’s payoff. Consider two simple mechanisms. The full
disclosure mechanism informs the receiver about the state, so the receiver acts iff his
type is below the realized state ω, i.e., π(ω, r) = 1 iff r ≤ ω, and the expected payoffisZ 1(ω − r) dF (ω) .
U (r) =r

The no disclosure mechanism does not convey any information to the receiver, so thereceiver acts if and only if his type r is below the ex-ante expected value of the state,i.e., π(r, ω) = 1 iff r ≤ E[ω], and the expected payoff is
U (r) = max {E[ω] − r, 0} .
Thus, U (r) is the optimal payoff of the receiver based on prior information about ω
as given by F , while U (r) is the receiver’s expected payoff if he observes ω.
Note that every mechanism π must satisfy
U (r) ≤ Uπ (r) ≤ U (r) for all r ∈ R.

(B)

The left-hand side inequality of (B) is implied by incentive compatibility: the receivercannot be better off by ignoring the sender’s recommendation. The right-hand side
inequality of (B) is the feasibility constraint: the receiver’s payoff cannot exceed thepayoff attained under full disclosure of ω.

PERSUASION OF A PRIVATELY INFORMED RECEIVER

94.2. Implementable utility profiles. We say that receiver’s utility profile U is
implementable if there exists a private persuasion mechanism π such that U (r) =Uπ (r) for all r ∈ R. We say that U is implementable by public signal if there exists a
public persuasion mechanism σ such that the receiver’s utility in this mechanism Uσsatisfies U (r) = Uσ (r) for all r ∈ R.
E[u(ω)]

U (r)
U (r)

U (r)
r

0

1

Figure 1. Set U contains every convex function between U and U .

Let U be the set of all convex functions bounded by U and U (see Fig. 1).
Theorem 1. The following statements are equivalent:(a) U is a convex function bounded by U and U ;(b) U is implementable;(c) U is implementable by public signal.
Proof. By (1), every U implementable by public signal is implementable. By Lemma
1 and (B), every implementable U belongs to U. It remains to prove that every U ∈ U
is implementable by a public signal.
Every public signal σ can be equivalently described by the c.d.f. of the probability
that type r will not act:Zhi
H(r) =Pr u(m, r) < 0 m ∈ Supp(σ(ω)) dF (ω).
(5)Ω

In words, H(r) is the ex-ante probability that the posterior expected payoff E[u(ω, r)|·]conditional on σ will be negative. Since u(ω, r) = ω − r, H(r) can be interpreted as
the probability that the posterior value E[ω|·] is below r.
Observe that the c.d.f. of the posterior value of the full disclosure signal is given
by F (r).

10Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

Let U ∈ U. Define H(r) = 1+U 0 (r), where U 0 denotes the right-derivative. Observe00that U 0 (0) = U (0) = −1 and U 0 (1) = U (1) = 0, hence H(0) = 0 and H(1) = 1.
Also, H is increasing, since U is convex. Hence, H is a c.d.f. Next, observe that
Z 1
Z 1
(1 − H(s))ds = U (r) ≤ U (r) =(1 − F (s))ds.
(6)
r

r

Furthermore, U (0) = U (0) = E[ω] implies that (6) is satisfied with equality for r = 0.
Thus, F is a mean-preserving spread of H. It follows that signal σ that induces
utility profile U can be obtained by an appropriate garbling of full disclosure signal
σ (Blackwell 1953).
Remark 1. Theorem 1 indirectly characterizes the set of implementable triples(q, U, V ), where q : R → [0, 1] is the receiver’s action profile and V : R → R is
the sender’s utility profile. Indeed, profile U uniquely pins down profiles q and V :q (r) = −U 0 (r) ,V (r) = q (r) + ρ (r) U (r) ,

(7)
(8)

where the first equality holds by Lemma 1 and the second by definitions of q, U , and
V . In particular, action profile q is implementable by a persuasion mechanism if andonly if q is non-increasing and satisfies
Z 1q(s)ds ≤ U (r) for all r ∈ R.
(9)r

Remark 2. For Theorem 1 to hold, it suffices to assume that the receiver’s payoffu(ω, r) is b(r) + c(r)d(ω) for some functions b, c, and d, with c positive. In addition,
the receiver’s utility profile U pins down the sender’s utility profile V if v (r, ω) =1 + ρ (r) u(ω, r) for some function ρ.10 Otherwise, (8) does not hold. Hence, a public
persuasion mechanism and a private persuasion mechanism that implement the same
utility profile U for the receiver may yield different payoffs to the sender. As aconsequence, optimal persuasion mechanisms may be outside of the set of public
persuasion mechanisms.
5. Optimal Mechanisms
Denote by Vπ (r) the sender’s expected payoff under mechanism π when the receiver’s type is r:ZVπ (r) =v(ω, r)π(ω, r)dF (ω).
Ω

R
The sender seeks a persuasion mechanism π that maximises R Vπ (r)dG(r). The following lemma is a useful tool for finding optimal persuasion mechanisms. It expresses
the sender’s expected payoff as a function of the receiver’s utility profile.
10The receiver’s incentive compatibility constraints do not change if his payoff function is di-vided by c (r). After this normalization, without loss of generality, we can rename the types,r̃ = −b(r)/c (r), and the states, ω̃ = d(ω). The sender’s payoff can also be expressed asv (ω̃, r̃) = 1 + ρ̃ (r̃) (ω̃ − r̃) where ρ̃ (r̃) = ρ (r) c (r).

PERSUASION OF A PRIVATELY INFORMED RECEIVER

11

Lemma 2. For every mechanism π,ZZVπ (r)dG(r) = C +Uπ (r)I(r)dr,
R

(10)

R

where
I(r) = g 0 (r) + ρ(r)g(r)andC = g(0)E[ω].

This result follows from the observation that Vπ is a function of Uπ for every π, as
we note in Remark 1. Integration by parts yields (10). The proof is in the Appendix.
By Theorem 1 the receiver’s utility profile is implementable by some persuasion
mechanism if and only if it is in U, hence the sender’s problem can be expressed as:Z
max U (r)I(r)dr.
(11)
U ∈U

R

We say that U is optimal if it solves the above problem.
5.1. Upper- and lower-censorship. A public signal is an upper-censorship if there
exists a cutoff ω ∗ ∈ Ω such that the signal truthfully reveals the state whenever it isbelow the cutoff and does not reveal the state whenever it is above the cutoff, so(ω,
if ω ≤ ω ∗ ,σ(ω) =
E[ω|ω > ω ∗ ], if ω > ω ∗ .
A lower-censorship signal is defined symmetrically, σ(ω) = ω whenever ω > ω ∗ andσ(ω) = E[ω|ω ≤ ω ∗ ] whenever ω ≤ ω ∗ for some ω ∗ ∈ Ω.

U (r)

U (r)

r
0

ω∗

1

Figure 2. Utility under upper-censorship mechanism.

12

Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

The utility profile under an upper-censorship mechanism (which pools all states oninterval [ω ∗ , 1]) is shown as a black kinked curve on Fig. 2. Since ω < ω ∗ is revealed to
the receiver, if receiver’s type r is below ω ∗ , he is informed whether ω exceeds r or not
and hence receives the highest feasible utility Ū (r) by acting iff ω > r. On the other
hand, a receiver with type r above ω ∗ is essentially informed whether ω exceeds ω ∗ ornot, hence he acts iff E[ω|ω > ω ∗ ] − r > 0. His utility is thus either E[ω|ω > ω ∗ ] − r or
zero, whichever is greater, corresponding to the two straight segments of the utility
curve.
The next result provides conditions for optimality of upper- (lower-) censorship.
Lemma 3. Upper-censorship (lower-censorship) is optimal for all F iff I crosses thehorizontal axis at most once and from above (from below).

U (r)

U (r)
U (r)
0

ω∗

r
r∗

1

Figure 3. Construction of upper-censorship mechanism under quasiconcave I.

Proof. Consider a function I that crosses the horizontal axis once and from above atsome r∗ , so I(r) is positive for r < r∗ and negative for r > r∗ . By (11) the senderwishes to design a mechanism that maximizes U (r) for all r < r∗ and minimizes it for
all r > r∗ , subject to the incentive and feasibility constraints. To prove that uppercensorship is optimal, given U , we now construct an upper-censorship function thathas exactly the same utility at r∗ , while higher utility for all r < r∗ and lower utility
for all r > r∗ , thus being preferable for the sender. The construction is illustrated by
Fig. 3. Function U is depicted by the solid dark red curve; the constructed uppercensorship function is depicted by the kinked black curve. This function coincides
with U (r) on interval [0, ω ∗ ] and then connects points (ω ∗ , U (ω ∗ ) and (r∗ , U (r∗ )) bya straight line, where ω ∗ is the tangency point. This straight line continues untilPERSUASION OF A PRIVATELY INFORMED RECEIVER

13

it hits zero utility and forms a kink at zero. This utility function is induced by an
upper-censorship mechanism with cutoff point ω ∗ , and it is superior to U by (11).
The argument for lower-censorship is symmetric.
Conversely, suppose that I does not cross the horizontal axis at most once and fromabove. Then there exist r1 < r2 < r3 such that I is negative on (r1 , r2 ) and positiveon (r2 , r3 ), because I is continuous by assumption. Therefore, lower-censorship is
optimal for any F that has support only on [r1 , r3 ]. In fact, by (11), every uppercensorship mechanism is strictly inferior. The argument for I that does not cross thehorizontal axis at most once and from below is symmetric.
Full disclosure of ω and no disclosure of any information about ω are two natural
policies that can be available to the sender even if she cannot use the entire set of
persuasion mechanisms. The privacy of the receiver’s information can protect thereceiver and force the sender to fully disclose her information. However, disclosing no
information about the state can also be optimal. From (11), full disclosure is optimal
among all persuasion mechanisms if I ≥ 0 and no disclosure is optimal if I ≤ 0. By
the same argument as in the proof of Lemma 3, the converse is also true.
Corollary 1. Full disclosure (no disclosure) is optimal for all F iff I is positive(negative).
Optimality of full disclosure or no disclosure in Corollary 1 is determined only by
the distribution of type of the receiver. In particular, full disclosure can be optimaleven when the sender is indifferent about the payoff of the receiver (ρ(r) = 0, sov(ω, r) = 1), and the ex-ante expected state E[ω] is so high that no disclosure induces
action 1 with probability close to 1.
Under the assumption of type-independent weight ρ we can obtain a stronger resultfor optimality of upper- (lower-) censorship:
Theorem 2. Let ρ(r) = ρ ∈ R. Upper censorship (lower censorship) is optimal for
all F and all ρ iff density g of the receiver’s type is logconcave (logconvex).
Proof. By Lemma 3, upper censorship (lower censorship) is optimal iff function I =g 0 (r) + ρg (r) crosses the horizontal axis at most once and from above (from below).
0 (r)This holds for all ρ ∈ R iff gg(r)is nonincreasing (nondecreasing) by Proposition 1 ofQuah and Strulovici (2012).
This result is useful, because many commonly-used density functions are logconcave
or logconvex (Tables 1 and 3 of Bagnoli and Bergstrom, 2005).
Under the assumption of type-independent weight ρ, we can also obtain a stronger
result for optimality of full disclosure and no disclosure. We say that G is a maximum
entropy distribution with parameter λ ∈ R if
g(r) = ceλr , r ∈ [0, 1],14Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

R1
where c > 0 is a constant such that 0 g(r)dr = 1.11 Note that the uniform distribution is the maximum entropy distribution with λ = 0.
Corollary 2. Let ρ(r) = ρ ∈ R. Either full disclosure or no disclosure is optimal for
all F and all ρ iff G is the maximum entropy distribution function.
Proof. The only two mechanisms that are both upper-censorship and lower-censorshipare full disclosure and no disclosure. By Theorem 2, both upper-censorship and lowercensorship are optimal for all F and all ρ iff g is both logconcave and logconvex. That
0 (r)
0 (r)
is, gg(r)is both non-increasing and non-decreasing, which holds iff gg(r)is equal to someconstant λ.
The class of maximum entropy distributions is a very special class under which the
optimal choice of the sender polarizes, in the sense that either full disclosure or no
disclosure is optimal. So, if g(r) = ceλr for some λ ∈ R, then full disclosure is optimal
whenever ρ ≥ −λ and no disclosure is optimal whenever ρ ≤ −λ (and anything is
optimal when ρ = −λ).
As an illustartion, let ρ = 0 and let G be the maximum entropy distribution withmean r0 . Then, no disclosure is optimal if and only if r0 ≤ 21 and full disclosure is
optimal if and only if r0 ≥ 12 . Indeed, with ρ = 0, the optimal persuasion mechanism
is fully determined by the sign of λ, which is in turn determined by whether mean r0
is greater or smaller than 21 .
5.2. Comparative statics. Theorem 2 allows for sharp comparative statics analysis
on the amount of information that is optimally disclosed by the sender. Specifically,
if g is logconcave and if a change in primitives increases optimal cutoff ω ∗ , thenthe optimal mechanism discloses more information. We now show that the sender
optimally discloses more information when she is less biased relative to the receiver,or when receivers are more reluctant to act.
Let ρ(r) = ρ ∈ R. Recall that ρ is the alignment parameter between the sender’sand the receiver’s preferences: a greater ρ means a smaller sender’s bias.
Consider a family of c.d.f.s Gt of the receiver’s typeGt (r) ≡ G (r − t) ,where t ∈ R is a parameter and G admits a strictly positive, differential, and logconcave density g on R with g 0 + ρg being almost everywhere nonzero. Parameter t is aparallel shift of the distribution of types. A greater t means a more “conservative”(reluctant to act) distribution, since every type of the receiver has a greater cost ofaction.
Since gt is logconcave for every t, upper censorship is optimal by Theorem 2. Letω ∗ (ρ, t) ∈ Ω be the optimal upper-censorship cutoff.
Proposition 1. For all ρ and t such that ω ∗ (ρ, t) ∈ (0, 1):(a) ω ∗ (ρ, t) is strictly increasing in ρ,
11That is, G is the maximum entropy distribution on the class of probability distributions withsupport on [0, 1] and a given mean.

PERSUASION OF A PRIVATELY INFORMED RECEIVER

15(b) ω ∗ (ρ, t) is strictly increasing in t.
The intuition for part (a) is that for a higher ρ, the sender puts more weight on
the receiver’s utility, so she optimally endows the receiver with a higher utility byproviding more information. To get the intuition for part (b), consider the case ofρ = 0. When t increases, the peak of the density function g shifts to the right.
For a large enough t density g is increasing on [0, 1], so full disclosure is optimal.
Symmetrically, when t is sufficiently small, density g is decreasing on [0, 1], so no
disclosure is optimal.
6. Applications
6.1. Public education campaign. The government would like to persuade the public to reduce the amount of smoking by commissioning a public education campaign
about the health risks of cigarettes.
There is a competitive market for cigarettes. The supply is perfectly elastic: themarket clearing price is equal to the cost parameter, p = c. The demand is induced
by a continuum of consumers. Each consumer values cigarettes at ω − ε, where ω isan uncertain common value of cigarettes and ε is an idiosyncratic preference shock;ω ∈ [0, 1] and ε ∈ R are distributed according to F and G, respectively, where density
g is log-concave. Thus, a consumer would like to purchase cigarettes if and only if
her value ω − ε exceeds price p.
Denote r = ε + p. Then a buyer’s net payoff is u(ω, r) = ω − r and the c.d.f.
of r is Gp (r) ≡ G (r − p). The total consumed quantity will depend on consumers’
beliefs about the expected value of ω conditional on the information they have. If thisexpected value is m, then a consumer would like to purchase cigarettes if and only
if m ≥ r. The demandR m curve is thus equal to Gp (m). Thus, the consumer surplus isequal to CS(m) = 0 (m − r)dGp (r).
Smoking imposes a negative externality on the entire population proportional to theamount of cigarette consumption, αGp (m), where α > 0. The social planner would
like to maximize the social welfare by designing a public education campaign (persuasion mechanism) that reveals information about ω and thereby alters its expectedvalue, m. For each ω and r social planner’s preferences are captured by payoffv(ω, r) = u(ω, r) − α
if consumer with type r buys cigarettes, and zero if he does not buy. The social
welfare is equal to V (m) = CS(m) − αGp (m).
As in Section 2, let π be a persuasion mechanism. The social planer maximizes
Z Z
Z
Vπ (r) =v(ω, r)π(ω, r)dF (ω)dGp (r) = (Uπ (r) − αqπ (r)) dGp (r)Z= C + Iˆp (r)Uπ (r)drwhere Iˆp (r) = gp (r) − αgp0 (r) and C is a constant. Our results from Section 5 apply,
allowing us to characterize the optimal educational campaign. In particular, full

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Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

information disclosure is optimal if g is decreasing (so a large mass of consumers has
high value for cigarettes), irrespective of the magnitude of the negative externality,
α. A lower-censorship that fully informs consumers only if quality ω is above a cutoff
is optimal if g is log-concave (Theorem 2).
Naturally, absent negative externality (α = 0), full disclosure is always optimal:
the social planner only increases the consumer surplus by letting the consumers makeinformed choices. But in presence of the externality, the social planner may wish tohide information. A larger negative externality and a higher price of cigarettes lead to(weakly) less disclosure (Proposition 1). The intuition is that if the price is sufficiently
large, then g is increasing (so a large mass of consumers has low value for cigarettes)
and a substantial fraction of consumers would not buy cigarettes if uninformed. So, abetter information is more likely to increase cigarette consumption than to decrease
it. Even though there is a positive effect of information due to consumers making
better informed choices, this is offset by the externality effect if α is large.
Finally, our analysis can easily handle environements with positive externalities(negative α), such as educational campaigns about benefits of vaccination. In suchenvironments, upper-censorship that fully informs consumers only if quality ω is below
a cutoff is optimal. Moreover, a larger positive externality and a lower price of vaccines
lead to less disclosure.
6.2. Lobbying. We consider a modification of the lobbying example from Kamenica
and Gentzkow (2011) adapted for the case of two actions of the receiver and uncertainty about the receiver’s preferences. A lobbying group (sender) commissions a
study with the goal of influencing a politician’s decision about adoption of a policy.
The politician (receiver) chooses whether to adopt the policy (â = 1) or not (â = −1).
The social value of the policy is determined by state ω̂ ∈ [−1, 1] and politician’s typer̂ ∈ [−1, 1]. The politician’s payoff is equal to ũ(â, ω̂, r̂) = −(â − ap (ω̂, r̂))2 , where
the politician’s bliss point is given by ap (ω̂, r̂) = ω̂ − r̂. The lobbyist’s payoff is equal
to ṽ(â, ω̂) = −(â − al (ω̂, r̂))2 , where the lobbyist’s bliss point is biased towards c:al (ω̂, r̂) = αap (ω̂, r̂) + (1 − α)c with α ∈ [0, 1) and c > 0.
Note that11(ũ(1, ω̂, r̂) − ũ(−1, ω̂, r̂)) =(1 + (ω̂ − r̂))2 − (1 − (ω̂ − r̂))2 = ap (ω̂, r̂)
4
4
and
1
(ṽ(1, ω̂) − ṽ(−1, ω̂)) = al (ω̂, r̂) .
4αMaking the substitution ω = ω̂+1
, r = r̂+1
, and ρ = 2(1−α)c, and changing the notation22
to a = 0 for â = −1 and a = 1 for â = 1, we obtain1(ũ(1, ω̂, r̂) − ũ(−1, ω̂, r̂)) = ω − r,2
(1 − α) cv(ω, r) =(ṽ(1, ω̂) − ṽ(−1, ω̂)) = 1 + ρ (ω − r)2u(ω, r) =PERSUASION OF A PRIVATELY INFORMED RECEIVER

17

with ω, r ∈ [0, 1] and ρ ≥ 0. Thus, our analysis applies. In particular, if density g ofr̂ is logconcave, then upper-censorship is optimal with more information disclosed if
the lobbyist is less biased (α closer to 1 and c closer to 0).
There are two noteworthy differences with the results in Kamenica and Gentzkow(2011) for this environment. First, the optimal persuasion mechanism is very muchdriven by the shape of the distribution of the private information of the receiver andmuch less by the degree of the conflict of preferences between the parties expressedin terms of the effective bias. In Kamenica and Gentzkow (2011), the value of αdetermines the structure of the optimal mechanism, whereas c played no role. In ourenvironment, both of them interact. Second, in Kamenica and Gentzkow (2011) thelobbyist either commissions a fully revealing study or no study at all. As Kamenica
and Gentzkow (2011) remark: “This contrasts with the observation that industryfunded studies often seem to produce results more favorable to the industry thanindependent studies.” In our environment, more complex mechanisms can be optimaland the expected probability of approval of the policy can be higher than the ex-ante
probabilities of approval under full or no information.
6.3. Media censorship. The following is an adaptation of the model of media endorsement of competing political candidates (parties) in Chiang and Knight (2011)
augumented by the possibility of censorship by a partial government.12
There is a government, two political parties c ∈ {G, O} (pro-Government andOpposition) competing in a parliamentary election, a continuum of voters and acontinuum of newspapers, each of unit measure. Parties are characterized by a pairof platform and quality (ic , ωc ), where we assume that ic , ωc ∈ [0, 1], and without lossof generality set iG < iO . If party c wins, the voter’s payoff is equal toβ(ic − r)2 ,2where r ∈ [0, 1] is the voter’s ideal platform (ideology) and β > 0 represents therelative utility weight placed on the party’s platform. The value of r is distributed
across the population of voters according to c.d.f. G. The voters know the platformsof the parties but are uncertain about their relative quality differential ω̂ = ωG −ωO of
the candidates on the party ballots. We assume that ω̂ is distributed on R according
to c.d.f. F̂ . Voters support the party that maximizes their expected utility.
The newspapers receive information about the quality of the candidates and choose
to endorse one of the parties. Each newspaper n observes signal ωn = ω̂ + n , wheren are idependent identically distributed uniform error terms with zero mean. We
denote the distribution of ωn by Fn . A newspaper endorses the pro-Government
party iff ωn > γn , where γn describes the newspaper’s editorial policy. Voters know
the editorial policies and choose a single newspaper to read.
In Chiang and Knight (2011), the newspaper editorial policies are exogenous. We
extend the model by allowing the government to determine the set of editorial policies available in the market by shutting down newspapers with undersirable editorialu(c, r) = ωc −12Another model with similar structure is Chan and Suen (2008).

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Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

policies and subsidizing creation of additional newspapers if some desired editorial
policies are lacking. Thus, we allow the government to determine the set of the editorial policies Γ = {γn |n ∈ N }. The objective of the government is to maximize theshare of the votes for the pro-Government party.
The timing of the game is as follows. The government chooses the set of permitted
editorial policies.13 The voters choose their preferred newspapers. The newspapers
observe the information about the party quality and make their endorsements. The
votes are cast. The solution concept is perfect Bayesian equilibrium.
Proposition 2. Assume that the density of the distribution of the voter ideology
is log-concave. Then, Γ = {γ|γ ≤ ω ∗ } for some ω ∗ ∈ [0, 1], that is, the government
censors all newspapers with the editorial policies above ω ∗ and ensures that all editorial
policies with γ ≤ ω ∗ are represented in the market.
7. Discussion
In this section we discuss side issues and extensions that connect this paper to theliterature beyond the topic of Bayesian persuasion.
7.1. Rotation order. The set of upper-censorship (lower-censorship) mechanisms is
totally ordered by cutoff points ω ∗ in Ω. The extremes, ω ∗ = 0 and ω ∗ = 1, correspondto the two extreme mechanisms, full disclosure and no disclosure.
To compare to Johnson and Myatt (2006), we consider the case of log-concave
density g of the receiver’s type and assume that ρ = 0 (so the sender cares aboutmaximizing the probability that the receiver acts). Interestingly, the set of c.d.f.sof the posterior expected payoff θ induced by upper-censorship (lower-censorship)mechanisms is also totally ordered by the rotation order R (Johnson and Myatt2006): for every two c.d.f.s H and Ĥ one can find a rotation point θ∗ such that H R
Ĥ iff H(θ) ≥ Ĥ(θ) for all θ ≤ θ∗ and H(θ) ≤ Ĥ(θ) for all θ ≥ θ∗ . Roughly speaking,a higher c.d.f. in the rotation order has a higher dispersion. For upper-censorshipmechanisms, the rotation point θ∗ is ω ∗ −ωB∗ , where ωB∗ is the optimal upper-censorshipcutoff. This rotation point is increasing w.r.t. R . For lower-censorship mechanisms,∗∗
the rotation point θ∗ is ω ∗ − ωW
where ωW
is the worst lower-censorship cutoff. Thisrotation point is decreasing w.r.t. R .
Let us first restrict attention to the set of lower-censorship mechanisms (which
are generally suboptimal, since under log-concave g an optimal mechanism is upper
censorship by Theorem 2). The c.d.f.s H induced by the lower-censorship mechanisms
are ordered by rotation order R and satisfy the condition of Lemma 1 of Johnson
and Myatt (2006) that the rotation point is decreasing w.r.t. R , which entails that
one of the extremes (full disclosure or no disclosure) maximizes the sender’s payoffon this set.14
13The newspapers are not strategic, they only follow their editorial policies (unless censored by

the government).
14Of course, neither may be optimal on a more general domain of mechanisms.

PERSUASION OF A PRIVATELY INFORMED RECEIVER

19

If, instead, we restrict attention to the set of upper-censorship mechanisms (thatcontain an optimal mechanism by Theorem 2), then the c.d.f.s H induced by themechanisms in this set are also ordered by rotation order R , but the condition ofLemma 1 of Johnson and Myatt (2006) is not satisfied. In this case, the sender’s payoff
is not quasiconvex w.r.t. R , so it can be maximized at an interior upper-censorshipmechanism.
7.2. Cutoff mechanisms and optimal delegation. A mechanism π is a cutoff
mechanism if it recommends the receiver to act if and only if ω is above some cutoffϕ(r̂) in [0, 1]:(
1, if ω ≥ ϕ(r̂),π (ω, r̂) =0, otherwise.
A cutoff mechanism can be equivalently described as a menu T ⊂ [0, 1] of cutoff tests
for the receiver to choose, such that T = {ϕ(r)}r∈R . A cutoff test t ∈ T is a binary
signal that recommends the receiver to act if and only if ω ≥ t.
Upper-censorship and lower-censorship mechanisms are cutoff mechanisms. For example, an upper-censorship mechanism with censorship cutoff w∗ can be represented
as the menu of cutoff tests T = [0, ω ∗ ] ∪ {1}.
Observe that test t = 0 (t = 1) yields the uninformative recommendation to always
act (never act), irrespective of the state. Since type r = 0 always acts and type r = 1
never acts, for every incentive-compatible cutoff mechanism the set of cutoff tests T
must contain 0 and 1. There are no further restrictions: every T ⊂ [0, 1] such that{0, 1} ⊂ T corresponds to an incentive-compatible cutoff mechanism.
The problem of optimal persuasion on the class of cutoff mechanisms is equivalent
to the problem of optimal delegation of choice from set T ⊂ [0, 1], with the constraintthat T must contain the endpoints, 0 and 1. In this delegation problem, the receivermust pick a cutoff test and then follow the recommendation of the test. The sender
restricts the receiver’s choice to a subset T ⊂ [0, 1] such that {0, 1} ⊂ T and lets thereceiver choose t from T .
Note that restriction to the class of cutoff mechanisms is not without loss of generality. Consider the following example.
Example 1. Let ρ(r) = 0 (so the sender wishes to maximize the probability that thereceiver acts). Let distribution F be uniform and let distribution G be bimodal as
shown on Fig. 4.
Under this assumptions, G is the value function for the sender: conditional onposterior expected value of the state m, the receiver acts iff r ≤ m, thus the sender’spayoff is G(m), the probability that the receiver acts. The sender’s unconditional
payoff is a convex combination of these values.
The upper bound on the sender’s expected payoff, V ∗ , is given by the concave
closure of G evaluated at E[ω] (cf. Kamenica and Gentzkow, 2011). This upperbound can be achieved by the binary public signal that sends message m = 1/3 ifω ∈ (1/12, 7/12) and m = 2/3 otherwise. However, this upper bound cannot be
achieved by any cutoff mechanism.

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Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

V∗

G

r
0

1
3

E[ω] = 2123

1

Figure 4. Utility under upper-censorship mechanism.

Appendix A. Structure of Optimal Mechanisms: General Case
In Section 5 we studied optimal persuasion mechanisms under the assumption thatfunction I changes sign at most once. Here we describe the structure of the optimalmechanism for a more general case.
Assume that I is almost everywhere nonzero and changes sign n times.15 Let{r1 , r2 , . . . , rn } be the set of types at which I changes its sign.
Theorem 3. Every optimal utility profile U is the convex envelope of the graph of U
and a set of at most n+1
points. Each point lies between U and U and corresponds to2
a distinct interval (ri , ri+1 ) on which I is negative.
Theorem 3 implies that optimal U is piecewise linear unless U (r) = U (r), withkinks on intervals where I is negative. Fig. 5 illustrates the structure of the optimal
utility profile for an example where I(r) (the red curve) changes its sign three times.
The optimal utility profile U (r) (the black kinked curve) is the lower contour of theconvex hull of points A and B and the graph of U (the solid blue curve). Points A
and B are kinks of U (r), and located over the intervals where I(r) is negative. On
the first interval of positive I(r), U (r) is the straight line. On the second interval of
positive I(r), U (r) is the straight line that smoothly merges into U (r). The segmentof U (r) to the right of the tangency point coincides with U .
15On the intervals where I(r) = 0 the sender is indifferent about the choice of U , hence multiple

solutions emerge in this case. The characterization of the solutions is a straightforward but tedious,and thus omitted.

PERSUASION OF A PRIVATELY INFORMED RECEIVER

21

U (r)
A
U (r)

Tangency of U and U

B
r
0
1

I(r)
−

+

−

+

Figure 5. Optimal utility profile for the case where I(r) changes sign threetimes.

Moreover, an optimal U is fully determined by a nonincreasing profile (u1 , . . . , un ),where each ui defines the value of U at point ri , so ui ∈ [U (ri ), U (ri )] for all i =1, . . . , n. This follows from the two properties below.
Clearly, as follows from (11), on any interval (ri , ri+1 ) where I(r) is positive, theoptimality requires that U (r) is pointwise maximized subject to feasibility (U ≤ U )and convexity of U . That is, for any given values of U at boundary points ri and ri+1 ,the utility profile U on the interior of (ri , ri+1 ) is a straight line unless U (r) = U (r),as illustrated by Fig. 6 and Fig. 7. Formally:(P1 ) On every interval (ri , ri+1 ) on which I is positive, U is the greatest convex
function that passes through the endpoints U (ri ) and U (ri+1 ) and does notexceed Ū .
Similarly, on any interval (ri , ri+1 ) where I(r) is negative, the optimality requiresthat U (r) is pointwise minimized subject to U ≥ U and convexity of U . That is, on theinterior of (ri , ri+1 ) the utility profile U is the minimum convex function that passes
through endpoints U (ri ) and U (ri+1 ). It is an upper envelope of two straight lines
whose slopes are the derivatives at the endpoints, U 0 (ri ) and U 00 (ri+1 ), as illustrated
by Fig. 8. Formally:

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Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

U (r)

U (r)
U (r)
I(r)
r
0
−

+

1

−

Figure 6. Optimal utility profile on the interval of where I(r) is positive.

Tangency pointsof U and U
U (r)

U (r)
U (r)
I(r)

r

0
−

+

−

1

Figure 7. Optimal utility profile on the interval of where I(r) is positive.

(P2 ) On every interval (ri , ri+1 ) on which I is negative, U is piecewise linear withat most one kink, and thus must satisfy
U (r) = max U (ri ) + U 0 (ri )(r − ri ), U (ri+1 ) + U 0 (ri+1 )(r − ri+1 ) , r ∈ (ri , ri+1 ).

PERSUASION OF A PRIVATELY INFORMED RECEIVER

23

U (r)

U (r)
U (r)
I(r)
r
0

+

−

+

1

Figure 8. Optimal utility profile on the interval of where I(r) is negative.

Consider a nonincreasing sequence (u1 , . . . , un ) that satisfies ui ∈ [U (ri ), U (ri )] for
all i = 1, . . . , n. Let ui determine the value of U at point ri , U (ri ) = ui . Observe thatthe interior of every interval (ri , ri+1 ) is uniquely determined by properties (P1 ) and(P2 ). Thus, optimal U can be fully described by the profile (u1 , . . . , un ). Finding an
optimal U is thus an n-variable optimization problem.
Proof of Theorem 3. The proof is immediate by necessary conditions (P1 ) and (P2 )that every optimal U must satisfy.
Appendix B. Nonlinear Utility of the ReceiverIn this section we discuss the complications of handling the case of nonlinear utilityof the receiver. We relax the linearity assumption and only assume that the receiver’s
payoff from acting, u (ω, r), is continuous and strictly monotonic (w.l.o.g., increasingin ω and decreasing in r), and satisfies normalization u (r, r) = 0 for all r.
Note that if types ω and r are correlated, the analysis below carries over if we
impose strict monotonicity on function ũ (ω, r) = u (ω, r) g (r|ω) /g (r) rather than onu, because Rthe receiver’s interim expected payoff under mechanism π can be written
as U (r) = Ω ũ (ω, r) π (ω, r) dF (ω).
B.1. Monotonicity. Monotonicity of u implies the same ordinal preferences overthe states by all types of the receiver. Without this assumption, optimal persuasion
mechanisms need not be public, as the following example demonstrates.

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Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

Example 2. Let ω = (ω1 , ω2 ) ∈ {0, 1} × {0, 1} and r ∈ {r1 , r2 , r3 }. Let all pairs(ω, r) be equally likely. Define u (ω, r1 ) = ω1 − 1, u (ω, r2 ) = ω2 − 1, and u (ω, r3 ) =3/4 − ω1 ω2 . Suppose that the sender wishes to maximize the probability that thereceiver acts.
Consider the following mechanism:
1 if r = r1 and ω1 = 1, or if r = r2 and ω2 = 1, or if r = r3 ,π (ω, r) =0 otherwise.
Essentially, this mechanism allows the receiver to learn at most one component of ω.
Clearly, under this mechanism, it is incentive compatible for the receiver to truthfully
report r and follow the recommendation. Thus, the probability that the receiver acts
(a = 1) under π is:2
Pr π (a = 1) = Pr (r = r1 ) Pr (ω1 = 1) + Pr (r = r2 ) Pr (ω2 = 1) + Pr (r = r3 ) = .
3
However, the sender cannot induce the receiver to choose a = 1 with probability
2/3 using a public signal. To see this, note first that the receiver r1 chooses a = 1
only if he is certain that ω1 = 1, and the receiver r2 chooses a = 1 only if he iscertain that ω2 = 1. Thus, under any public signal, the probability that the receiveracts cannot exceed 2/3. The only possibility of how the sender could achieve thisprobability would be to reveal both ω1 and ω2 , but in that case the receiver r3 would
not act when (ω1 , ω2 ) = (1, 1).
B.2. Additive-multiplicative separability. Recall that Theorem 1 only proves
equivalence of implementation of the receiver’s utility profile under private and public
persuasion. To establish equivalence of the sender’s payoff, we used two assumptions,that the sender’s utility is a function of the receiver’s utility and the receiver’s action,and that the receiver’s action profile is pinned down by her utility profile (Remark
1). The latter property is a consequence of linear u. More generally, this property
obtains if and only if u is additively-multiplicatively separable in the sense of Remark
2 (see Proposition 3 below). In other words, when u is not additively-multiplicatively
separable, it may be the case that two persuasion mechanisms implement the same
utility profile for the receiver, and yet the sender’s expected payoff is different.
Proposition 3. Let π1 and π2 be two mechanisms that are distinct for every r but
implement the same receiver’s utility profile U . The probability of a = 1 is the samefor π1 and π2 if and only if there exist functions b, c, and d such that u (ω, r) =b (r) + c (r) d (ω) for every (ω, r).
Proof. Consider two mechanisms π1 and π2 that implement the same utility profile U
for the receiver. We have for each i = 1, 2 and for all rRu (ω, r) π (ω, r) dF (ω) = U (r) ,R ∂u(ω,r) i
πi (ω, r) dF (ω) = U 0 (r),∂rwhere the first line holds by definition of U and the second line is the local incentive
compatibility condition.

PERSUASION OF A PRIVATELY INFORMED RECEIVER

25

R

The expected action, qπi (r) = πi (ω, r) dF (ω) is the same across i = 1, 2 for all r, and 1 are linearly dependent for all r:
if and only if the vectors u (ω, r), ∂u(ω,r)∂r∂u (ω, r)+ µ (r) u (ω, r) = 1 for some µ and γ.
(12)∂rNote that γ(r) 6= 0, because otherwise u (ω, r) would not depend on ω. For every ω,
the solution of differential equation (12) is given byZ
R µ(r)
1 R µ(s)− γ(r) drdsγ(s)u (ω, r) = e
Const (ω) +
e
dr ,
γ (r)
γ (r)

which completes the proof with b (r) , c (r) , and d (ω) defined by RZ
R µ(r)µ(r)1 R µ(s)− γ(r) drds− γ(r) drγ(s)(b (r) , c (r) , d (ω)) = eedr, e, Const (ω) .
γ (r)
B.3. Ex-post equivalence. Theorem 1 establishes equivalence of private and public
persuasion mechanisms in terms of the interim expected payoff of the receiver. Thisdefinition is in line with recent papers on standard mechanism design with transfers(Manelli and Vincent, 2010 and Gerskov et al., 2013).
A stronger notion of equivalence requires private and public persuasion mechanisms
implement the same ex-post outcomes (actions by the receiver). Notice that equivalence in terms of ex-post outcomes implies equivalence in terms of interim expected
payoffs, without any assumption (such as linearity) on the sender’s and receiver’s
payoff functions. Under monotonic u, it is easy to verify whether a given private
mechanism π is implementable by public signal:
Proposition 4. An incentive-compatible persuasion mechanism π is ex-post equivalent to a public signal if and only if π(ω, r) is non-increasing in r for every ω.
Proof. Consider an arbitrary public signal σ. By monotonicity of u, for every message
m if type r acts, then every type r0 < r will act as well. Consequently, we can focus
on direct signals where every message m is equal to the type who is indifferent to act
or not after receiving this message, Eω [u(ω, m)|m] = 0. Thus, the mechanism16X
π(ω, r) =Pr m ≥ r σm (ω).
m

is non-increasing in r for every ω.
R 1Consider a mechanism π that is non-increasing in r. Thus, the probability qπ (r) =π(ω, r)dF (ω) that the receiver of type r will act is non-increasing in r. The c.d.f.
0
H(r) = 1 − qπ (r) defines a public signal as described in the proof of Theorem 1. 
Observe that when u is nonlinear, a incentive-compatible mechanism π need nothave a monotonic qπ , and hence it may be impossible to construct an equivalent public
signal.
16We write σm (ω) for the probability of message m conditional on state ω.

26

Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

B.4. Binary state. If the state is binary, then there is ex-post equivalence between
private and public mechanisms under the assumption of monotonic u.17
Proposition 5. Let the support of F consist of {0, 1}. Then every incentive-compatible
mechanism π is ex-post equivalent to a public signal.
Proof. It is sufficient to show that π is non-increasing in r for r ∈ (0, 1). The receiverr prefers to report n = r rather than r0 only if:Xu (ω, r) (π (ω, r) − π (ω, rb)) Pr (ω) ≥ 0.
(13)
ω=0,1

Writing (13) for (r, rb) = (r2 , r1 ) and (r, rb) = (r1 , r2 ) yields:−u (0, r2 )u (0, r1 )δ (r2 , r1 , 0) ≤ δ (r2 , r1 , 1) ≤ −δ (r2 , r1 , 0)u (1, r2 )u (1, r1 )(14)

where δ (r2 , r1 , ω) ≡ (π (ω, r2 ) − π (ω, r1 )) Pr (ω). Because u (0, r) < 0 and u (1, r) > 0
for r = r1 , r2 , the monotonicity of u in r implies that0<−u (0, r2 )u (0, r1 )≤−for r2 ≤ r1 .
u (1, r2 )u (1, r1 )Combining (14) and (15) gives π (ω, r2 ) ≥ π (ω, r1 ) for ω = 0, 1, and r2 ≤ r1 .

(15)
Appendix C. Omitted Proofs
C.1. Proof of Lemma 1. Incentive compatibility (IC) implies truthtelling,
Uπ (r) ≥ Uπ (r, r̂) for all r, r̂ ∈ R.

(16)

Also, observe that type r = 0 can secure the maximal attainable payoff E[ω] by always
acting (irrespective of recommendation). On the other hand, the maximal attainablepayoff of type r = 1 is zero, which can be secured by never acting. Together with
(IC), this implies
Uπ (0) = E[ω] and Uπ (1) = 0.
(17)
By the standard envelope argument, (16)–(17) is equivalent to (2)–(4).
It remains to show that (16)–(17) imply (IC). By contradiction, suppose that(16)–(17) hold and there exist r and r̂ such thatUπ (r) <max

a0 ,a1 ∈{0,1}{Uπ (r, r̂, a0 , a1 )} .

By (16) we have Uπ (r, r̂, 0, 1) ≤ Uπ (r). Also, it can be easily verified that Uπ (r, r̂, 0, 1) ≤Uπ (r, r̂, 1, 0) implies
Uπ (r, r̂, 1, 0) ≤ max {Uπ (r, r̂, 0, 0), Uπ (r, r̂, 1, 1)} .
Thus we are left withUπ (r) < max {Uπ (r, r̂, 0, 0), Uπ (r, r̂, 1, 1)} .
17In fact, this result is tight:

if the state has at least three values, then there is no ex-postequivalence between private and public mechanisms, even if the receiver’s utility is linear.

PERSUASION OF A PRIVATELY INFORMED RECEIVER

27

Note that a0 = a1 = 1 (a0 = a1 = 0) means that the receiver chooses to act (notto act) regardless of the recommendation, in which case his expected payoff is E[ω](zero). By (17) this is also the payoff of the obedient receiver who reports r̂ = 0(r̂ = 1, respectively). HenceUπ (r, r̂, 0, 0) = Uπ (r, 0) = E[u(ω)] and Uπ (r, r̂, 1, 1) = Uπ (r, 1) = 0.
But (16) implies Uπ (r) ≥ max {Uπ (r, 0), Uπ (r, 1)}, a contradiction.
C.2. Proof of Lemma 2. We haveZ Vπ (r) =1 + ρ(r)u(ω, r) π(ω, r)dF (ω) = qπ (r) + ρ(r)Uπ (r).
Ω

Since

R1
r

qπ (s)ds = Uπ (r) by Lemma 1, integrating by parts yields
ZZ
g 0 (r)Uπ (r)dr.
qπ (r)dG(r) = g(0)Uπ (0) +
R

R

Hence

Z

Z
Vπ (r)dG(r) = Uπ (0)g(0) +RUπ (r) (g 0 (r) + ρ(r)g(r) dr.

R

Substituting C = Uπ (0)g(0) = E[ω]g(0) and I(r) = g 0 (r) + ρ(r)g(r) yields (10).
C.3. Proof of Proposition 1. Types r < 0 always act and types r > 1 never act;
so we omit these types from the analysis. The sender’s expected payoff under publicmechanism σ can be written as:
Z 1
Vσ = C +Jt (r) dHσ (r) ,(18)0where C is a constant that does not depend on σ, Hσ is c.d.f. of posterior values
Eσ [ω|m], andZr(gt (s) + ρGt (s)) ds.

Jt (r) =0

Consider ρ and t such that ω ∗ (ρ, t) ∈ (0, 1). Denote ω ∗∗ = E [ω|ω ≥ ω ∗ ]. The
derivative of the sender’s expected payoff (18) under upper censorship with respect
to cutoff ω ∗ is:
Z ω∗∗
dV
∗
= f (ω )(Jt0 (ω ∗∗ ) − Jt0 (s)) ds∗dωω∗Z ω∗∗

Z ω∗∗
∗
∗∗
∗∗
= f (ω )(gt (ω ) − gt (s)) ds + ρ(Gt (ω ) − Gt (s)) ds .
ω∗ω∗This derivative is strictly increasing in ρ; so ω ∗ is strictly increasing in ρ by Theorem
1 of Edlin and Shannon (1998).
Consider t at which ω ∗ (ρ, t) ∈ (0, 1). Notice that
Jt0 (r) = g (r − t) + ρG (r − t) = J 0 (r − t) ;

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Anton Kolotilin, Ming Li, Tymofiy Mylovanov, Andriy Zapechelnyuk

so
Z ω∗∗
d2 V
∗
(Jt00 (ω ∗∗ ) − Jt00 (s)) ds= −f (ω )dtdω ∗∗
ω= −f (ω ∗ ) Jt00 (ω ∗∗ ) (ω ∗∗ − ω ∗ ) + f (ω ∗ ) (Jt0 (ω ∗∗ ) − Jt0 (ω ∗ )) .
dV
At optimal interior cutoff, we have dω
∗ = 0; so
Z ω∗∗
0
∗∗
∗∗
∗
Jt0 (s) ds.
Jt (ω ) (ω − ω ) =

(19)

ω∗

Since Jt00 (r) is almost everywhere nonzero, Jt0 is not constant on (ω ∗ , ω ∗∗ ). Moreover,by Theorem 2, It (r) = Jt00 (r) crosses the horizontal axis at most once and from above;so Jt0 (r) is quasiconcave. Therefore, (19) implies that Jt00 (ω ∗∗ ) < 0 and Jt0 (ω ∗∗ ) >d2 V∗
Jt0 (ω ∗ ); so dtdωis strictly increasing in t by Theorem 1 of Edlin and∗ > 0 and ω
Shannon (1998).
C.4. Proof of Proposition 2. Let ω̂n = E [ω̂|ωn ] and notice that E [ω̂|ωn ] is increasing in ωn , because εn is uniformly distributed. Thus, without loss we can assume thatnewspaper n observes ω̂n and endorses the pro-Government party iff ω̂n > γ̂n . Givena newspaper’s signal about quality differential ω̂n , the voter’s expected net payofffrom choosing the pro-Government party versus the Opposition party is equal toββũ(ω̂n , r) = ω̂n − (iG − r)2 + (iO − r)2
2
2

β 2
= ω̂n − β (iO − iG ) r +iO − i2G2
β 22ω̂n + (iO −iG )and letDefine ω = β(i2O −iG)u(ω, r) =1û(ωn , r) = ω − r.
β (iO − iG )Let F be the distribution of ω. Let γ̂(r) be the choice of the editorial policy by voter
r. Now, consider the auxiliary problem in which the government maximizes V overthe set of persuasion mechanisms. Then, by Propositon 2, the optimal persuasion
policy is upper-censorship. We now show that Γ̂ = [0, ω ∗ ] implements the outcome ofthe optimal persuasion policy, which establishes the claim of the proposition.
Obseve that the voter’s payoff from choosing the newspaper with editorial policyγ̂ net of his full information payoff U (r) is equal toZ rv(γ̂, r) =(ω − r)dF (ω)γ̂

If Γ̂ = [0, ω ∗ ], this payoff is maximized by choosing the newspaper with the editorial
policy γ̂ = r if r ≤ ω ∗ and γ = ω ∗ otherwise. It follows that the voter will choose tovote for the pro-Government party iff ω ≥ r for r ≤ ω ∗ , iff ω > ω ∗ if E[ω|ω > ω ∗ ] ≥ r
and r > ω ∗ , and never otherwise. Observe that the same behavior is induced by the
optimal persuasion policy of upper-censorship at ω ∗ .

PERSUASION OF A PRIVATELY INFORMED RECEIVER

29

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