Eduardo Monteiro

Humberto Moreira

English

Research Papers in Economics

T￿tulo: E¢ ciency in Two Player Repeated Games of Imperfect Monitoring
Autores: Eduardo Monteiro (EPGE / FGV -RJ) e Humberto Moreira (EPGE
/ FGV -RJ)
Resumo:
A literatura em jogos repetidos com monitoramento pœblico imperfeito tem
se concentrado em e￿ciŒncia aproximada, atingida conforme os jogadores se tornam
i￿nitamente pacientes.
Esse trabalho, por outro lado, estuda os equil￿brios e￿cientes com jogadores
impacientes.
￿ mostrado que, no caso de dois jogadores, uma caracteriza￿ªo completa
do conjunto de equil￿brios e￿cientes em termos simples Ø poss￿vel. E que e￿ciŒncia
depende da intensidade de puni￿ıes necessÆrias para implementar certas a￿ıes.
Como exemplo, uma demonstra￿ªo de ine￿ciŒncia no dilema dos prisioneiros
Ø dada, sob hip￿teses mais fracas do que o ususal.
Abstract:
Most of the literature on repeated games with imperfect public monitoring
has focused on approximate e¢ ciency, achieved as players become in￿nitely patient.
By contrast, this note studies the set of fully e¢ cient public equilibria with
impatient players.
It is shown that, for two player games, a full characterization of this set can
be given in simple terms. And that e¢ ciency depends on the severity of punishments
needed to enforce certain actions.
As an example, a proof of ine¢ ciency in the prisoner￿ s dilemma is given,
under scarce assumptions.
Palavras Chave: Jogos repetidos, monitoramento pœblico imperfeito, equi-
l￿brio perfeito pœblico, e￿ciŒncia.
Key Words: Repeated games, imperfect public monitoring, public perfect
equilibrium, e¢ ciency.
`rea ANPEC: `rea 7 - Microeconomia, MØtodos Quantitativos e Finan￿as.
Classi￿ca￿ªo JEL: C73 - Stochastic and Dynamic Games; Evolutionary Games;
Repeated Games.
1E¢ ciency in Two Player Repeated Games of Imperfect
Monitoring
Abstract. Most of the literature on repeated games with imperfect public
monitoring has focused on approximate e¢ ciency, achieved as players become
in￿nitely patient.
By contrast, this note studies the set of fully e¢ cient public equilibria
with impatient players.
It is shown that, for two player games, a full characterization of this set
can be given in simple terms. And that e¢ ciency depends on the severity of
punishments needed to enforce certain actions.
As an example, a proof of ine¢ ciency in the prisoner￿ s dilemma is given,
under scarce assumptions.
Classic results in game theory guarantee that, essentially, any outcome is pos-
sible in in￿nitely repeated games. Since players may be harshly (and eternally)
punished for any deviation from equilibria, e¢ cient outcomes can always be en-
forced. Or so the folk theorem with complete information says. But if monitoring
is imperfect ￿as it often is ￿such conclusions may go adrift.
Here we study games in which players can not perfectly observe each other￿ s
actions. Only an imperfect public signal is available. This class of games is in-
teresting because (i)It has diverse applications, new and old, including relational
contracts (Levin 2003), monetary policy (Athey et. al. 2005), collusion (Green
and Porter 1984) and bidding rings (Aoyagi 2003). (ii)Little is known about their
e¢ cient equilibria. In fact, the ￿rst example of such e¢ cient equilibria was given
in Athey and Bagwell (2001).
This note shows this result may be extended to generic two player games of
imperfect public monitoring. A complete characterization of the e¢ cient equilibria
of such games is given.
A related result is Fudenberg et. al. (2006) which present an algorithm for
￿nding the limit set of e¢ cient equilibria, as players become in￿nitely patient. Yet,
their work focuses on the n-player case, which admits considerably less structure.
By contrast, lemma 2 describes this set for any given discount factor. It is
also shown that, if players are su¢ ciently patient this entire limit set is achieved.
Lemma 1 relates the shape (and all important non-emptiness) of this set and the
severity of punishments needed to enforce certain actions. A proof of ine¢ ciency
in the prisoner￿ s dilemma, under scarce assumptions, follows easily.
2EFFICIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING 3
1. The Model
1.1. The setup and basic results. Two players play a stage game at t =
0;1;2;￿￿￿. They take actions ai in a ￿nite set Ai. Actions are not directly ob-
servable, but they induce probabilities ￿(￿ja) on a ￿nite set of public outcomes Y .
Moreover we assume that these probabilities have a constant support, not depend-
ing on a1. The actual payo⁄ ri(ai;y) to a player depends on his own action and on
the public outcome. But not directly on the other player￿ s action (although does
a⁄ect the distribution of y).
Example 1 (Partners￿dillema). The two players are owners of a ￿rm, and
operate it. Pro￿ts y are random, but their distribution will depend on e⁄ort levels
ai 2 Ai = fwork;shirkg. Their payo⁄s are ri =pro￿t=2￿e⁄ort.
Let gi(a) = E(ri(ai;y)ja) be the average gain from playing a pro￿le a.
A typical stage game has an strategic form such as2
c n
c
n
f
0
@
A B
C D
E F
1
A
Throughout this note we will use letters A;B;C;￿￿￿ to denote both action
pro￿les, as cc and their payo⁄ vectors g(cc).
We admit correlated equilibria, as in Aumann (1987). That is, players can
condition their actions on a public randomization device. This makes V , the
set of feasible payo⁄s of the stage game a polygon - the convex closure of points
A;B;C ￿￿￿(￿gure 1). Assume, also, that each side contains only two pure action
pro￿les3.
In the repeated game players maximize
vi = (1 ￿ ￿)
X
￿
tEgt
i
for 0 ￿ ￿ < 1. The factor (1 ￿ ￿) normalizes supergame payo⁄s as average payo⁄s.
So the set of feasible payo⁄s of repeated play is also V .
Our solution concept is perfect public equilibrium. That is, subgame perfect
equilibria in which players condition their actions only on the public history. This
is a widely used solution concept, and precise de￿nitions can be found in Fudenberg
and Tirole (1991) or Mailath (forthcoming). Abreu et. al. (1990) show that, as
long as other players play public strategies, there is no gain in conditioning on
1The assumptions of ￿nite A and Y are included to avoid pathologies.The results carry over
to most cases where they are not satis￿ed. In fact, Athey and Bagwell (2001), who provided
the original example of e¢ cient equilibria, studied a model with a continuum of actions. The
constant support hypothesis means that a player can never be certain about the other￿ s actions.
It is assumed, for instance, by Abreu et. al. (1990). Without it, e¢ cient equilibria may prescribe
o⁄ the equilibrium path ine¢ cient punishments. So a full characterization of e¢ cient values would
depend on knowledge of the whole set of equilibria, which is largely unavailable. Still, as players
become in￿nitely patient, Fudenberg et. al. (2006) show much can be said.
The constant support assumtion is the reason we said our result hold only generically.
2We will use A;B;C;￿￿￿ to denote both action pro￿les, as cc and their payo⁄ vectors g(cc).
3More precisely, only payo⁄ vectors of two pure action pro￿les.4 EFFICIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING
Figure 1. A feasible set.
private history4. Let PPE(￿) denote the set of perfect public equilibria for a given
discount factor ￿.
We are now turn to the e¢ cient equilibrium values - those that are not Pareto
dominated by any feasible payo⁄. Such a value must be located on a downward
sloping side of the polygon. From now on we consider such a side, AB with slope
m5.
Proposition 1. For any given ￿ < 1, the set of e¢ cient equilibria in AB is
a (possibly empty) compact interval I(￿). As ￿ nears 1, these intervals stabilize -
there exists ￿ such that I(￿) = I(￿), for all ￿ ￿ ￿.
Proof. PPE(￿) is compact and convex6, so I(￿) = PPE(￿) \ AB must be a
closed interval. The fact that I(￿) is constant for large ￿ is a direct consequence of
lemma 1. ￿
This remark draws a qualitative picture of how the e¢ cient equilibria I(￿)
evolve. Still, it does not speak on their shape, or even on the possibility of e¢ ciency.
The following lemma does so, in terms of the severity of punishment needed to
enforce action pro￿les A and B.
Lemma 1. Let a and b be points in AB with a1 = A1 + P1
A and b2 = B2 +
P2
B(￿gure 2). For large ￿ the set of e¢ cient equilibria in AB is
￿ [a;b] if a1 < b2.
￿ The static equilibria, otherwise.
Naturally, without de￿ning the numbers P, the lemma does not say much.
Informally, the number P1
A is the minimum level of punishment that has to be
in￿ icted on player 1 to play his ill-favored action A. A precise de￿nition will be
given shortly.
4Yet, Kandori and Obara (2006) show that e¢ ciency may improve if all players use private
strategies.
5For simplicity assume A1 < B1 and m 6= 0 or 1.
6Abreu et. al. show it to be compact, and it is convex for we are assuming correlated
equilibria.EFFICIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING 5
Figure 2. The e¢ cient equilibria of lemma 1.
This lemma has an interesting corollary. Suppose player 2 plays the same action
in pro￿les A and B. So player 1 can deviate from A to B, and the punishment
P1
A ￿ B1 ￿A1. But then the lemma implies there are no e¢ cient equilibria in AB:
Corollary 1. If a player uses the same action in pro￿les A and B, and
neither is a static equilibrium, then there are no e¢ cient equilibria in AB.
The following examples illustrate the techniques.
1.2. Partners￿dilemma. Let w be work and s be shirk. We will use ww; ws;
sw and ss to denote both action pro￿les and their payo⁄ vectors. For the partners￿
dilemma to be interesting, payo⁄s are usually distributed as in the classic prisoner￿ s
dilemma. That is,
ws1 < ss1 < ww1 < sw1 (1.1)
sw2 < ss2 < ww2 < ws2
Mailath and Samuelson (forthcoming) and Radner et. al. (1986) give examples
of such games which are bounded away from e¢ ciency. But Fudenberg et. al. (1994)
show that as ￿ nears 1, these games may have approximately e¢ cient equilibria In
fact, their folk theorem holds generically if #Y ￿ 4. And, to our knowledge, the
literature is silent about ine¢ ciency in this case.
Yet, e¢ cient equilibria could only be achieved on the sides (ws)(ww) or (sw)(ww)
(￿gure 3). But then corollary 1 shows they can never achieve exactly e¢ cient equi-
libria, irrespective of the signal space Y .
Proposition 2. Consider a discounted in￿nitely repeated game G with imper-
fect public monitoring and
￿ Two stage actions for each player, w and s.
￿ Stage payo⁄s respecting 1.1.
￿ Constant support.
then G has no e¢ cient perfect public equilibrium.6 EFFICIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING
Figure 3. The prisoner￿ s dilemma.
Still, there is a similar example in which e¢ ciency is possible. Suppose each
partner can choose an e⁄ort level in E = f0;1=8;1=4g. The ￿rm￿ s average pro￿ts
are given by
p
e⁄ort, so the e¢ cient level of e⁄ort is 1=4. In this case, partners can
take turns working 1=4. So they can arrange an incentive scheme in which getting
good results today means a lower probability of working tomorrow, and e¢ ciency
may be achieved. A numerical example is shown in ￿gure 1.2. The points are pure
strategy payo⁄s. E¢ cient equilibria are the points on the line outside the circles.
Figure 1.2 shows how the interval of e¢ cient equilibria evolves as ￿ grows.
-0.1 0 0.1 0.2 0.3 0.4
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
v
1
v
2
The extended partership game.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
0.05
0.1
0.15
0.2
0.25
d
A
t
t
a
i
n
a
b
l
e
 
v
1
Attainable e¢ cient values, for given ￿.
1.3. E¢ ciency and punishments. We now de￿ne the quantities mentioned
in lemma 1.
Let u = (u1;u2) : Y ￿! R2 be reward functions. If the static game were
to be played once, and payments u to be made conditional on Y , ￿ would be anEFFICIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING 7
equilibrium i⁄
(1.2)
gi(￿) + E(ui(y)j￿)
￿ gi(ai;￿￿i) + E(ui(y)jai;￿￿i) for i = 1;2 and every ai in Ai
We will say that the reward function u implements ￿ if 1.2 holds.
P1
A will now be de￿ned in terms of an incentive problem, of implementing A
with two conditions. First, there is a weighted budget balance: player 2 always
receives m￿what player 1 receives. And secondly, the expected payment to each
player is 0. Then P1
A is the minimum, over all transfer functions, of the largest
punishment that has to be in￿ icted on player 1. More precisely:
Definition 1. Let P1
A be
Min
u
(Max
y
￿u1(y)) (1.3)
s.t.
8
<
:
u implements A
u2 = mu1 (budget balance)
E(u(y)jA) = 0 (0 expected payment)
Likewise, P2
B is de￿ned. There is an alternative de￿nition of P1
A through a
simpler, albeit less meaningful, program:
Min
u (Eu1(y)jA)=2 (1.4)
s.t.
8
<
:
u implements A
u2 = mu1 (budget balance)
E(u1(y)jA)=2 ￿ u1(y0) for every y0 in Y .
It should be noted that 1.3 readily implies that the classical Green and Porter
(1984) model of collusion with hidden action does not achieve e¢ ciency. For, if
￿rms are producing monopoly quantity, it is pro￿table for both of them to increase
production. So no budget balanced transfer scheme can enforce monopoly output.
Formulas for ￿nding the sets I(￿) are given in the appendix, along with proofs
of previous assertions.
2. Proofs
2.1. Recursive methods. The proofs use the recursive methods of Abreu et.
al. (1990). They show that every equilibria v can be factored in a strategy ￿ in
the current period and a continuation reward function u : Y ￿! R2, such that u
￿-implements ￿:
Definition 2. A reward function u ￿-implements ￿ if
vi = (1 ￿ ￿)gi(￿) + ￿E(ui(y)j￿) (2.1)
￿ (1 ￿ ￿)gi(ai;￿￿i) + ￿E(ui(y)jai;￿￿i)
that is,
E(ui(y)j￿) ￿ E(ui(y)jai;￿￿i)
￿
1 ￿ ￿
￿
(gi(ai;￿￿i) ￿ gi(￿))8 EFFICIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING
So our previous de￿nition of implementation is 1=2-implementation, for when
￿ = 1=2 the player weights present and future equally.
A key element of the recursive approach is the Bellman map T.
Definition 3. The Bellman map T￿ is de￿ned for compact subsets of R2 as
T￿(W) = cofg(￿) + Eu : u takes on values in W and ￿-implements ￿g
Definition 4. A set W in R2 is self-generating if W ￿ T(W).
The key facts we will use are summarized as the next remark:
Remark 1. The set PPE(￿) is compact, and is the largest ￿xed point of T.
All self-generating sets are contained in PPE(￿). T takes compact sets on compact
sets.
This facts are well known, and may be found on either Fudenberg and Tirole
(1991) or Mailath and Samuelson (forthcoming). Equation 2.1 has an interesting
geometric interpretation: v is an average of present gains and continuation values
u(y). So, for values on a side AB, only promises in AB are made. This simple fact
has important consequences for the Bellman map.
We will omit, for a moment, indexes ￿. The above fact translates as T(W) \
AB ￿ T(W \ AB). But since T(PPE) = PPE, we have
PPE \ AB = T(PPE) \ AB (2.2)
￿ T(PPE \ AB)
Which results in the following remark:
Remark 2. The closed interval I(￿) is the largest self-generating closed interval
in AB.
Proof. I(￿) is a closed interval, for it is the intersection of AB with PPE,
which is know to be closed and convex. By self-generation every self generating
interval in AB is contained in I(￿). And, by 2.2, I(￿) is self-generating. ￿
2.2. The equilibria for general ￿ < 1. By remark 2, I(￿) is the largest self-
generating interval in AB. But the restrictions for an interval to be self-generating
are linear. So, the largest one, I(￿) may be de￿ned by a linear program. We note
this observation as the following lemma:
Lemma 2. Let a(￿) be a point in AB with a1(￿) equal to
Min
u;w a1 (2.3)
s.t.
8
> > > > > > > > > > <
> > > > > > > > > > :
a = (1 ￿ ￿)A + ￿u
b = (1 ￿ ￿)A + ￿w
u ￿-implements A
w ￿-implements B
u 2 AB
w 2 AB
a1 ￿ u1(y) ￿ b1 for every y in Y .
a1 ￿ w1(y) ￿ b1 for every y in Y .
and b(￿) de￿ned by b1(￿) = Maxu;w b1.with the same restrictions. If a1(￿) ￿ b1(￿)
then I(￿) = [a(￿);b(￿)]. Otherwise I(￿) are the static equilibria in AB.EFFICIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING 9
2.3. Proof of lemma 1. Consider, for a moment, the left half of program
2.3:
Min
u
a1 (2.4)
s.t.
8
> > <
> > :
a = (1 ￿ ￿)A + ￿u
u ￿-implements A
u belongs to the line AB
a1 ￿ u1(y) for every y in Y .
the key observation is that its solution does not depend on ￿.
Proof. Say a and u0 are feasible for a discount factor ￿
0. Then it is trivial to
verify that a and
u =
￿ ￿ ￿
0
￿(1 ￿ ￿
0)
a +
1 ￿ ￿
￿
￿
0
1 ￿ ￿
0u0
are feasible for ￿. ￿
Moreover, as ￿ approaches 1, u approaches a. So the ignored constraints are,
indeed satis￿ed if a1 < b1.
Now, consider program 2.4 with ￿ = 1=2, and the variable e u = u￿A substituting
u. Then program 1.4 obtains. The equivalence between programs 1.3 and 1.4 can
be checked directly.
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games with imperfect monitoring,￿Econometrica 58, 1041-1063.
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Theory, 112, 79-105.
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EFFCIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING

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EFFCIENCY IN TWO PLAYER REPEATED GAMES OF IMPERFECT MONITORING

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