WORKING 

PAPERS 

“Nobody Goes There Anymore – it’s too Crowded:” 

Level-k Thinking in the Restaurant Game

Matthew T. Jones 

WORKING PAPER NO. 315 

February 2013 

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“Nobody goes there anymore - it’s too crowded:”

Level-k Thinking in the Restaurant Game

Matthew T. Jones∗

Federal Trade Commission

February 19, 2013

Abstract

A game of herding with capacity constraints is studied experimentally. Differences between

Level-k strategies depend on the cost of choosing an alternative that has reached capacity, with a

maximum difference between Level-1 and higher levels when the cost is high. This design makes

Level-1 behavior relatively easy to identify. Though strategies consistent with higher levels are also

found, a substantial proportion of observed strategies are consistent with Level-1. Within-subject

correlations across settings suggest that Level-1 thinking can explain overweighting of private information in herding games. In addition, evidence of a correlation between cognitive ability and

level of thinking is found.

JEL codes: C92, D81, D83

Keywords: herding behavior, social learning, level-k, bounded rationality

∗Email: mjones1@ftc.gov. Telephone: 202-326-3539. Address: 600 Pennsylvania Avenue NW, Mail Drop NJ4136, Washington, D.C. 20580. This work is supported by the NSF under Grant No. SES-1121085. Any opinions,

findings, conclusions or recommendations expressed are those of the author and do not necessarily reflect the views

of the Federal Trade Commission or the NSF. The author thanks Dan Levin and James Peck for valuable advice and

guidance on this project. The author would also like to thank Caleb Cox, Charles Holt, John Kagel, Rosemarie Nagel,

Ryan Sandler, Dave Schmidt, Mike Schreck, Doug Smith, participants of the 2012 North American ESA Conference

and participants of the microeconomics brownbag seminar and theory/experimental reading group at Ohio State for

helpful feedback. The quotation in the title is attributed to Yogi Berra.

1
1 Introduction

In many decisions under uncertainty, individuals base their choices on what they learn from observing the choices of others as well as noisy private information, which can lead to herding behavior.1

However, herding may be discouraged if costs are incurred when an individual follows the action

of too many others. For example, observing people on their way to a restaurant or a rival firm

opening a new location provides information about the relative quality of their chosen alternative

but also increases the likelihood that one who follows them will be penalized because the location

has already reached capacity. Though such decisions have received little attention in the literature,2

they create a rich environment for exploring broader questions about herding behavior. In

this paper, I develop a model of herding with capacity constraints called the Restaurant Game and

study play of this game in a lab experiment. The results provide new insights on the extent to

which the common bias in herding behavior can be explained by Level-k thinking and other factors

such as strategic uncertainty and cognitive ability.

A common observation in herding experiments is that subjects rely more on their private information and less on social learning than predicted by the risk-neutral Bayesian Nash equilibrium

(see Hung and Plott (2001), Noth and Weber (2003), Celen and Kariv (2004a, 2005), Goeree et al.

(2007), Weizsacker (2010) and March et al. (2012)).3 A possible explanation for this phenomenon

is provided by Level-k thinking, a model of bounded rationality in which individuals act under

inconsistent beliefs about the rationality of others (see Stahl and Wilson (1994), Nagel (1995),

Camerer et al. (2004) and Crawford and Iriberri (2007)). According to this model, individuals

overweight private information because they fail to make inferences about unseen information from

1See Banerjee (1992), Bikhchandani et al. (1992) for seminal work on this idea.

2The most closely related theoretical work is the Veeraraghavan and Debo (2008) study of strategic location

of services with uncertain quality and waiting costs to consumers. Eyster and Rabin (2010) discuss a variant of

their model of naive herding which includes small negative payoff externalities of choosing the same as others.

Experiments by Hung and Plott (2001), Drehmann et al. (2007) and Owens (2011) have studied herding with direct

payoff externalities and found that subjects are more responsive to these than to informational externalities.

3Weizsacker’s (2010) meta-study of 13 herding experiments reveals that subjects make the empirically optimally

choice 44% of the time when it contradicts private information but 90% of the time when it is consistent with private

information. Celen and Kariv (2005) find that subjects are even more likely to overweight private information when

they observe only the choice of their immediate predecessor, as in my experiment, than when they observe the

entire sequence of preceding choices. In contrast, Dominitz and Hung (2009) elicit beliefs in a replication of prior

experiments and find that patterns in discrete choice data are better explained by heterogeneous belief updating than

systematic overweighting of private information.

2
the choices they observe others make.

The Restaurant Game is a useful setting for testing this explanation. It involves a sequence of

four players choosing between alternatives with a capacity of two. Each player receives a private

signal, which along with the observed choice of the preceding player can help her form expectations

about the relative quality of the alternatives. The third and fourth players incur a “waiting cost”

if they choose the same alternative as at least two preceding players. In the experiment, the

choices of the first two players are made by computers with fixed decision rules, allowing me to

test the extent to which the common deviation from Nash equilibrium is consistent with rational

expectations given that predecessors sometimes make errors.4 Results of the experiment confirm

that the capacity constraint and waiting cost make the third player less likely to follow preceding

players, as predicted by the Nash equilibrium. Because the problem of avoiding the waiting cost is

more complex for the fourth player, the behavior of subjects in this role is more heterogeneous. I

find that reducing strategic uncertainty by using computer players early in sequences does increase

social learning by later players, but deviations from Nash equilibrium persist.

The complexity of the fourth player’s problem and the heterogeneity in observed strategies

provide a context for exploring the role of Level-k thinking and cognitive ability in herding behavior.

When the waiting cost is high, he is predicted to choose contrary to the third player unconditional on

his private signal if he is Level-1, whereas he is predicted to follow the third player unconditional on

his private signal for Level-2 and higher (including the Nash equilibrium). I find that a substantial

proportion of strategies are consistent with each prediction. I also find a within-subject correlation

between proximity to the Level-1 prediction in this high-cost setting and proximity to Level-1 in

the standard no-cost setting. These results suggest that the commonly observed bias in herding

behavior can be explained by a substantial proportion of subjects who engage in Level-1 thinking.

4See error-rate response models such as the Quantal Response Equilibrium of McKelvey and Palfrey (1995).

Anderson and Holt (1997), Anderson (2001), Goeree et al. (2007) and Ziegelmeyer et al. (2010) find evidence that

this concept can explain deviations from equilibrium in herding environments. Kubler and Weizsacker (2004) and

Brunner and Goeree (2011) find that response to error-rates alone cannot explain biases in their herding experiments,

but this concept does fit the data when modified to incorporate limited depth-of-reasoning. Ivanov et al. (2009)

find that boundedly-rational rules of thumb explain behavior better than Quantal Response. A similar alternative

explanation is that individuals are overconfident in their private information causing them to discount information

given to them by others, but Celen et al. (2010) find disconfirming evidence that direct advice leads to strategies

closer to the equilibrium than strategies with social learning alone.

3
Moreover, I find evidence that the fourth player’s strategies are significantly closer to the Nash

equilibrium and significantly farther from the Level-1 strategy when the subject’s ACT scores are

in the top 5% of all test-takers, an indicator of high cognitive ability.

The paper proceeds as follows. Section 2 describes the Restaurant Game and its Nash equilibrium, while Section 3 explains the experimental design and the Level-k predictions. Section 4 states

the main research questions, Section 5 reports the experimental results, and Section 6 concludes.

2 The Restaurant Game

The Restaurant Game5is a model of herding with continuous signals6 and imperfect information

about predecessors’ choices7 with the addition of capacity constraints. Four players, indexed by

n ∈ {1, 2, 3, 4}, arrive on a boulevard in sequence and choose between two restaurants, Ray’s (R)

and Louie’s (L), in order of arrival. The restaurant choice of player n is denoted by xn ∈ {R, L}.

Before choosing, each player receives a noisy private signal about the relative quality of the food

at the restaurants, θn, drawn independently and uniformly from the interval [0, 1], and observes

the choice of only the immediately preceding player, xn−1. The true quality of R’s food is equal

to

P4

i=1 θi

4

, while the true quality of L’s food is equal to 1 −

P4

i=1 θi

4

. Each restaurant can serve

only two players at a time, so if Player 3 or Player 4 chooses the same restaurant as at least two

of the preceding players then that player incurs the cost of waiting for a table. Player n’s payoff

from choosing a restaurant is equal to the true quality of the chosen restaurant’s food minus that

player’s waiting cost, Cn(x1, ..., xn), which is equal to c ∈ [0, 1] if at least two of n’s predecessors

chose the same restaurant and 0 otherwise.

Suppose xn−1 = R. Player n chooses restaurant R if and only if the following holds:

E[U(

P4

i=1 θi

4 − Cn(x1, ..., xn−1, R))|θn, xn−1 = R]

≥ E[U(1 −

P4

i=1 θi

4 − Cn(x1, ..., xn−1, L))|θn, xn−1 = R].

5The restaurant-choice framing for herding games originates with Banerjee (1992) and has been used more recently

by Eyster and Rabin (2010). This game is also similar to the El Farol bar problem of Arthur (1994).

6See Smith and Sorensen (2000), Celen and Kariv (2004a, 2004b, 2005) and Owens (2011).

7See Celen and Kariv (2004b, 2005).

4
By monotonicity of U in θn, it follows that player n uses a decision rule given by:

xn(xn−1 = R) =







R if θn ≥ ˆθn(c)

L if θn < ˆθn(c)

.

The problem is symmetric for xn−1 = L, so in this case player n follows a decision rule given by:

xn(xn−1 = L) =







R if θn ≥ 1 − ˆθn(c)

L if θn < 1 − ˆθn(c)

.

Therefore, the equilibrium is fully characterized by ˆθn(c) for each n. I refer to ˆθn(c) as player n’s

equilibrium strategy at waiting cost c.

The risk-neutral Bayesian Nash equilibrium strategies, ˆθn(c), for the four players are as follows

(see Appendix A for derivations):

ˆθ1(c) = 1

2

;

ˆθ2(c) = 1

4

;

ˆθ3(c) =







3+24c

16 if c ≤

13

24

1 if c > 13

24

;

ˆθ4(c) =







1

256 (39 + 368c − 576c

2

) if c ≤

13

24

13−16c

16 if c ∈ (

13

24 ,

13

16 ]

0 if c > 13

16

.

Consider a simple example demonstrating the Nash strategies of P1 and P2. Suppose that P1

receives a private signal of .62 and P2 receives a private signal of .41. Because there are no players

preceding P1 and her private signal is greater than .5, she expects the payoff of choosing R (the

average of the four players’ private signals) to be greater than the payoff of L (1 minus the average

of the four players’ private signals). P1 chooses R, and P2 observes P1’s choice. By observing that

P1 chooses R, P2 infers that P1’s private signal is between .5 and 1. Because P2’s expectation

of P1’s signal is .75, he expects the payoff of choosing R (the average of the four players’ private

signals) to be greater than the payoff of L (1 minus the average of the four players’ private signals)

unless he receives a strong disagreeing private signal less than .25. Because P2’s private signal is

.41, he chooses R as well.

5
Figure 2.1: Nash Strategies for Players 3 and 4

The Nash strategies for Players 3 and 4, ˆθ3(c) and ˆθ4(c), are shown in Figure 2.1. For low

waiting costs, Player 3 (P3) follows Player 2 (P2) if her private signal agrees with P2’s choice8 or

if it disagrees but not too strongly. Player 4’s (P4’s) strategy at low costs is similar, except that

the informational externality of P3’s choice is slightly larger than that of P2’s, so P4 follows P3

for a slightly larger range of private signals. As the waiting cost increases, the incentive to avoid

the cost attenuates herding, so the range of private signals for which P3 follows P2 and the range

for which P4 follows P3 shrink. The range of signals for which P3 follows P2 continues to shrink

until the waiting cost is sufficiently high that the incentive to avoid it dominates the incentive to

choose the highest-quality restaurant. In this region, P3 chooses contrary to P2 unconditional on

her private signal.

Unlike P3’s, P4’s Nash strategy is non-monotonic in the waiting cost. Because increasing the

cost raises the likelihood that P3 chooses contrary to P2, and because P2 follows Player 1 (P1)

with probability 3/4, increasing the cost raises the likelihood that P4 can avoid it by following P3.

At the same time, increasing the cost makes avoiding it more important, so the range of signals for

which P4 follows P3 begins to expand with the cost when it is sufficiently high. Beyond this point,

the range of signals for which P4 follows P3 continues to expand until it reaches the level where

8Player n’s signal “agrees” with Player n − 1’s choice when θn ≥ 0.5 if xn−1 = R and when θn ≤ 0.5 if xn−1 = L.

6
the incentive to avoid the cost dominates the incentive to choose the highest-quality restaurant. In

this region, P4 follows P3 unconditional on his private signal.

3 Experimental Design and Level-k Strategies

In this section, I describe the four treatments of the experiment: BASE, ORDER, 1-2-L1 and 1-2-H.

Table 3.1 summarizes the key features of each treatment. I also consider how strategies may differ

from the Nash equilibrium in each treatment if subjects do not learn from the observed choices of

others in a way consistent with full, commonly known rationality. The Level-k model9 provides a

natural representation of such bounded rationality. In the Level-k model, players choose a bestresponse given non-equilibrium beliefs about the rationality of others. A Level-0 player chooses

an action randomly, a Level-1 player best-responds to the belief that others are Level-0, a Level-2

player best-responds to the belief that others are Level-1, and a Level-k player best-responds to

the belief that others are Level-k − 1.10 Level-k strategies are the same in BASE and ORDER but

different in the other treatments, so I describe the design and Level-k strategies of these treatments

first and return to 1-2-L1 and 1-2-H later.

3.1 Treatments BASE and ORDER

The purpose of BASE is to explore the behavior of subjects in the roles of P3 and P4 while P1

and P2 are computer players, with cost levels played in random sequence to control for order

effects. ORDER is identical to BASE except that the cost levels are played in increasing rather

than random sequence, allowing a comparison with previous literature by capturing behavior in the

standard no-cost setting before prompting responses to the capacity constraint and waiting cost.

Sessions of BASE and ORDER consist of 18 rounds. In each round, subjects are matched

9See Stahl and Wilson (1994), Nagel (1995), Costa-Gomes et al. (2001), Camerer et al. (2004), Costa-Gomes and

Crawford (2006) and Crawford and Iriberri (2007).

10The Level-1 concept closely resembles the Cursed Equilibrium of Eyster and Rabin (2005), and in fact the Level-1

and Cursed Equilibrium strategies are identical in this model. Similarly, the Level-2 prediction is identical to the

Best Response Trailing Naive Inference Equilibrium of Eyster and Rabin (2010). As discussed in Eyster and Rabin

(2009), Cursed Equilibrium and Level-1 predictions coincide in most cases (as do the Best Response Trailing Naive

Inference Equilibrium and Level-2 predictions), but Level-1 players believe that other players’ choices are uniformly

distributed while Cursed players’ beliefs can take any arbitrary distribution.

7
Table 3.1: Treatments

Treatment Order of Cost Levels Player 1 Player 2

BASE Random Nash Computer Nash Computer

ORDER No, Low, High Nash Computer Nash Computer

1-2-L1 Random Level-1 Computer Level-1 Computer

1-2-H Random Human Subject Human Subject

randomly and anonymously in pairs: one subject with the role of P3 and the other with the role

of P4. P3 and P4 make a choice in each round after choices are made by two computer players,

P1 and P2. P3 and P4 roles are assigned randomly to subjects at the beginning of the experiment,

and subjects keep the same role throughout.

Each round has exactly the same rules as the Restaurant Game presented in Section 2, except

that the parameters are multiplied by 100 and the restaurant framing is replaced by neutral language.11 Each player chooses one of two options, R and L, in sequence. The strategies followed

by computer players P1 and P2 are shown to subjects using a diagram, which is reproduced in

the left panel of Figure 3.1. In BASE and ORDER, P1 and P2 choose according to their Nash

strategies. Before choosing, P3 and P4 subjects see only the cost for that round and the choice of

the immediately preceding player on their computer screens.

The experiment uses a belief elicitiation procedure for entering choices,12 in which subjects are

asked to enter a number between 0 and 100 before learning their private signal. If the private signal

turns out to be greater than this number, the subject’s choice is R, and if the private signal turns

out to be less than this number, the subject’s choice is L. After a number is entered, the private

signal is drawn and shown on the subject’s computer screen along with the resulting choice, the

payoff associated with this choice, the cost incurred (if any) and net earnings for the round.

In each round, the cost is set at one of three values: 0, 35 or 85. In my analysis, I refer to them

as No-Cost, Low-Cost or High-Cost rounds, respectively. Six of the 18 rounds in each session are

played at each cost level. In BASE, the sequence in which these rounds are played is determined

randomly. In ORDER, the No-Cost rounds are played first, followed by the Low-Cost rounds and

11See Appendix B for the instructions and screenshots seen by subjects. The experimental software is programmed

in zTree (Fischbacher, 2007).

12This method has been used in previous continuous-signal herding experiments by Celen and Kariv (2004a, 2005),

Celen et al. (2010) and Owens (2011).

8
Figure 3.1: Computer Player Strategies

BASE/ORDER 1-2-L1

finally the High-Cost rounds.13 Payoffs are denominated in Experimental Currency Units (ECUs).

Subjects receive a starting balance of 50 ECUs plus their earnings in one randomly determined

round out of the six played at each cost level (three rounds total). They are paid cash at an

exchange rate of $0.10 per ECU, in addition to a fixed participation fee of $5.

Because P1 and P2 are computers whose choice rules are fixed and known to all human players

in the experiment, it seems implausible that P3 and P4 would have inconsistent beliefs about the

behavior of these players. However, P4’s beliefs about the rationality of human subjects in the

role of P3 may lead to behavior consistent with Level-k predictions. That is, a Level-k P4 may

best-respond to the belief that P1 and P2 follow their programmed strategies and P3 follows a

Level-k − 1 strategy. The Level-k strategies of P4 in BASE and ORDER are shown in Figure 3.2.

P4’s Level-k strategies coincide with the Nash for Level-4 and higher, and his Level-2 and Level3 strategies are very close to his Nash strategy in these treatments. On Level-2 and higher, P4

believes that P3’s choice in No-Cost rounds reveals some information about her private signal and,

on Level-3 and higher, about the private signals of P1 and P2 also. However, a Level-1 P4 believes

that P3 chooses randomly, and he best-responds to that belief by choosing based entirely on his

13Two trial rounds which do not count for payment precede these 18 rounds so that subjects can become familiar

with the software interface. In ORDER, the cost level is 0 in both trial rounds. In the other three treatments, the

cost level in trial rounds is drawn randomly and independently from {0, 35, 85}.

9
Figure 3.2: Player 4 Level-k Strategies in BASE/ORDER

own private information. Hence, the overweighting of private information observed in the herding

literature is consistent with Level-1 thinking, as distinguished from Level-2 and higher.

The advantage of this design lies in the divergence between Level-1 and higher levels, which

predict strategies at opposite endpoints of the strategy interval in High-Cost rounds. On all levels

higher than Level-1, P4 recognizes that P3 always chooses contrary to P2 in High-Cost rounds and

that P1 chooses the same as P2 with probability 3/4. Because the incentive to avoid the waiting

cost dominates in these rounds, it is optimal for P4 to follow P3 unconditional on his private signal

given these beliefs. However, the Level-1 P4 believes that P3’s choice reveals nothing about the

choice of P2 or P1 and he is most likely to avoid the waiting cost by choosing contrary to P3. Hence,

the Level-1 P4 chooses contrary to P3 unconditional on his private signal in High-Cost rounds.

3.2 Treatment 1-2-L1

Treatment 1-2-L1 is identical to BASE except that the choices of computer players P1 and P2 are

independent rather than correlated, which changes P4 predictions for Level-2 and higher but not

Level-1. Results from 1-2-L1 and BASE can then be compared to determine whether P4 behavior

responds to differences in these predictions between the two treatments.

In 1-2-L1, P1 and P2 both choose based entirely on their own private signals by using a strategy

10
Figure 3.3: Player 4 Level-k Strategies in 1-2-L1

of 50. Their strategies are represented to subjects in this treatment as shown in the right panel

of Figure 3.1. Because the choices of P1 and P2 are independent in this treatment but not in the

others, fewer iterations of best-response are involved before players reach the Nash equilibrium. The

strategies of computer players P1 and P2 thus correspond to what would be the Level-1 strategy for

human players in this treatment, which means that P3’s Nash strategy would concide with Level-2

(

ˆθ

L2

3

(c) = 1+4c

4

if c ≤

3

4

and ˆθ

L2

3

(c) = 1 if c > 3

4

) and P4’s Nash strategy with Level-3.

The Level-k strategies of P4 in 1-2-L1 are shown in Figure 3.3. His Level-1 strategy is the same

as in the other treatments. For Level-2 and higher, P4 recognizes that P3 always chooses contrary

to P2 in High-Cost rounds, but P1 chooses the same as P2 with probability 1/2 rather than 3/4

in this treatment, so P2’s choice now reveals nothing about P1’s. Therefore, P4 cannot expect to

avoid the waiting cost by following P3 in High-Cost rounds; he is equally likely to incur the cost by

following as he is by choosing contrary to P3. Hence, P4’s choice is based on his own private signal

and what he infers about P2’s private signal from P3’s choice. He follows P2 by choosing contrary

to P3 in High-Cost rounds unless his own private signal strongly disagrees with P2’s inferred choice.

11
Figure 3.4: Player 3 Level-k Strategies in 1-2-H

3.3 Treatment 1-2-H

Treatment 1-2-H differs from the other three treatments in that P1 and P2 are human subjects

instead of computer players. Results of the other treatments can be compared to those of 1-2-H to

determine the extent to which deviations from Nash equilibrium can be attributed to uncertainty

about the strategies of P1 and P2.

In this treatment, P1, P2, P3 and P4 roles are assigned randomly to subjects at the beginning

of the experiment, and subjects keep the same role throughout. In each of the 12 rounds of this

treatment, subjects are matched with one player in each other role and make choices in sequence

accordingly. Four rounds are played at each cost level, and one of the four rounds at each cost level

is chosen randomly for payment. Cost levels are played in a randomly determined order.

Because all four players in a sequence are humans in 1-2-H, both P3 and P4 may hold inconsistent beliefs about the behavior of P1 and P2 in this treatment. The Level-k strategies of P3

and P4 in this treatment are shown in Figures 3.4 and 3.5, respectively. Their Level-1 strategies

are the same because these strategies condition choices on only the private signal and action of the

immediately preceding player. P3’s Level-2 strategy is the same as her Nash (and Level-2) strategy

in 1-2-L1 and P4’s Level-2 strategy is the same as his Level-2 strategy in 1-2-L1 because computer

players P1 and P2 follow what would be their Level-1 strategies in 1-2-L1. P4’s Level-3 and Nash

12
Figure 3.5: Player 4 Level-k Strategies in 1-2-H

strategies are the same as in BASE and ORDER because they best-respond to beliefs that P1’s and

P2’s strategies are the same as those played by computer players P1 and P2 in those treatments,

with P3 following her Level-2 or Nash strategy, respectively.

4 Research Questions

Question 1: How does the capacity constraint affect the strategies of Players 3 and 4?

The Nash equilibrium predicts that capacity constraints attenuate herding behavior, as the

incentive to avoid the waiting cost works against the incentive to choose the highest-quality alternative. When the waiting cost is low, the capacity constraint is predicted to shift P3 strategies

such that following P2 is less likely but remains conditional on her private signal. When it is high,

P3 is predicted to choose contrary to P2 unconditional on her private signal. For P4, the waiting

cost is predicted to have a non-monotonic effect on strategies in BASE, ORDER and 1-2-H. When

the waiting cost is high, P4 is predicted to follow P3 unconditional on his private signal in BASE,

ORDER and 1-2-H, while he is predicted to choose contrary to P3 for all disagreeing and weak

agreeing signals in 1-2-L1.

13
Question 2: Compared to when preceding choices are made by humans, do strategies exhibit more

social learning when predecessors are computers with fixed, commonly known strategies?

Herding experiments typically study how choices are influenced by learning from preceding

choices made by human subjects. In contrast with this convention, P3 in the BASE, ORDER and

1-2-L1 treatments of this experiment makes a choice given information about the choices of computer players whose strategies are fixed and commonly known. This feature of the design allows me

to test the hypothesis that subjects in herding experiments overweight private information due to

the possibility that preceding human players make errors. To address this issue, I compare P3 and

P4 strategies in BASE to those in 1-2-H, which is identical to BASE except that P1 and P2 are human subjects. This comparison allows an assessment of the degree to which overweighting of private

information relative to the Nash is caused by rational expectations of human behavior. I also compare my results to those of Celen and Kariv’s (2005) experiment,14 in which the environment for P3

and P4 is equivalent to No-Cost rounds of 1-2-H, though the instructions and parameter scale differ.

Question 3: Does Level-k thinking explain departures from Nash equilibrium?

In contrast to standard herding models, the game explored in BASE and ORDER affords the

advantage of a particularly clear distinction between Level-1 strategies and strategies of Level-2

and higher when the waiting cost is high. Hence, these treatments are well-suited to test the predictions of the Level-k model and also allow a test of their within-subject robustness across different

settings. Comparison of results from 1-2-L1, where the choices of P1 and P2 are independent, and

BASE, where they are correlated, should also shed light on how the behavior of P4 responds to

Level-k predictions. P4’s Level-1 strategy in both BASE and 1-2-L1 is to choose contrary to P3

unconditional on his private signal. His strategy on Level-2 and higher in BASE is to follow P3

unconditional on his private signal, but in 1-2-L1 it is to choose contrary to P3 for all but strong

agreeing signals. Comparison of the data with these stark differences in predictions can shed light

14I thank the authors of this paper for sharing their data.

14
on whether Level-k thinking is responsible for the bias observed in herding games.

Question 4: Is there a relationship between subjects’ cognitive ability and the proximity of their

strategies to theoretical predictions?

I consider both the fully-rational Nash equilibrium and boundedly-rational Level-k strategies

as candidate models of behavior in this experiment. Given that some heterogeneity in behavior is

found in the data, it may be that the degree to which subjects behave as predicted by the alternative

theories is determined by their cognitive ability. I investigate the relationship between the proximity

of subjects’ strategies to the Nash, Level-1 and Level-2 predictions and their cognitive ability, as

indicated by American College Test (ACT) and Scholastic Aptitude Test (SAT) scores. ACT and

SAT scores have been shown by Frey and Detterman (2004) and Koenig et al. (2008), respectively,

to be strongly correlated with measures of general intelligence. Comparing these scores with the

experimental data may provide an explanation for observed differences in levels of thinking.

5 Experimental Results

Sessions were conducted at the Ohio State University Experimental Economics Lab in the fall of

2011 and spring of 2012. A total of 298 subjects participated in the experiment with 86 participating

in BASE over 4 sessions, 78 participating in ORDER over 3 sessions, 78 participating in 1-2-L1 over

3 sessions and 56 participating in 1-2-H over 2 sessions. All subjects participated in only one of

the treatments, so all treatment differences are between-subject, while differences between No-Cost,

Low-Cost and High-Cost rounds in each treatment are within-subject. Subjects were recruited with

email invitations sent out randomly to students in a large database of Ohio State undergraduates

of all majors. Sessions lasted between 60 and 90 minutes, with average earnings of $22.15.

The advantage of the strategy-elicitation method used in the experiment is that it allows me

to determine proximity to the different theoretical predictions and to infer the degree to which

strategies rely on private information vs. social learning. Due to the symmetry of the model and

the observed behavior, I simplify the analysis henceforth by normalizing the data to one dimension

15
Table 5.1: Predicted vs. Actual Effects of Treatment and Cost Level

BASE/ORDER 1-2-L1

Prediction No-Cost Low-Cost High-Cost Prediction No-Cost Low-Cost High-Cost

Nash P3 19 71 100 Nash P3 25 60 100

P4 15 38 0 P4 19 59 75

Level-2 P4 25 40 0 Level-2 P4 25 65 75

Level-1 P4 50 85 100 Level-1 P4 50 85 100

Mean Strategy No-Cost Low-Cost High-Cost Mean Strategy No-Cost Low-Cost High-Cost

BASE P3 44.5 <<< 69.3 71.9 1-2-L1 P3 43.2 <<< 70.7 <<< 80.2

P4 53.3 55.5 53.1 P4 52.2 << 59.3 63.6

ORDER P3 45.3 <<< 64.5 <<< 77.6 1-2-H† P3 53.4 <<< 76.0 83.2

P4 42.1*** 46.9 46.1 P4 51.6 65.3 67.5

Between-cost-level difference significant at: <<< .01 level, << .05 level, < .1 level.

Between-treatment difference (compared to BASE) significant at: *** .01 level, ** .05 level, * .1 level.

†

1-2-H: Nash same as BASE/ORDER; Level-2 same as P3 Nash/P4 Level-2 in 1-2-L1; Level-1 same as P4 Level-1s.

of strategies which combines the strategies entered when the preceding player chose R with 100

minus the strategies entered when the preceding player chose L, and I analyze all of the strategies

as if the preceding player chose R.15 Table 5.1 reports the mean strategies entered by P3 and P4

in each treatment and cost level along with Nash and Level-k predictions.16 Figures 5.1 and 5.2

display the distributions of strategies in each treatment and cost level for P3 and P4, respectively,

with the Nash prediction marked by a vertical line.

5.1 Response to Capacity Constraint

In this section, I study how the capacity constraint and waiting cost level affect the strategies

of Players 3 and 4 by comparing differences in their mean strategies between cost levels to the

differences predicted by the Nash equilibrium.17

15Figures C.1 and C.2 in Appendix C display the distributions of strategies entered by P3 and P4 subjects in each

treatment split by cost level and the choice of the immediately preceding player (R or L). These figures show that

the distribution of strategies when the preceding player chose R and the distribution when the preceding player chose

L are reasonably symmetric, with no consistent bias towards R or L. Kolmogorov-Smirnov tests find no significant

differences in the distribution of strategies when the preceding player chose R and the mirror image of the distribution

when the preceding player chose L in any treatment/cost level combination, except for P3 in No-Cost rounds of

ORDER and 1-2-L1 and for P4 in Low-Cost rounds of BASE, where the difference is accounted for by a few subjects

who always enter a strategy of 100 in these rounds.

16Table C.1 in Appendix C reports the mean strategies in subjects’ first and last of the six rounds played at each

cost level in each treatment for both P3 and P4. Mean strategies entered by both P3 and P4 in BASE and 1-2-H

show a consistent shift towards the Nash between the first and last round played at each cost level, but the data from

ORDER and 1-2-L1 do not exhibit this tendency.

17I use Wilcoxon signed-ranks tests to determine whether the capacity constraint and waiting cost have the predicted

within-subject effects on strategies. Observations for these tests are subject-level mean strategies at each cost level.

16
Figure 5.1: Distribution of Player 3 Strategies by Treatment and Cost Level

17
Figure 5.2: Distribution of Player 4 Strategies by Treatment and Cost Level

18
Result 1.1: Player 3 behavior is generally consistent with the comparative static effects of the capacity constraint and waiting cost level predicted by the Nash equilibrium.

I find that both imposing the capacity constraint with a low waiting cost and raising the waiting

cost from low to high attenuate herding behavior, as predicted by the Nash equilibrium. P3 strategies are such that following P2 is less likely with a low waiting cost than with no waiting cost and

less likely with a high waiting cost than a low waiting cost. Differences between P3’s mean No-Cost

and Low-Cost strategies are statistically significant in all treatments, but differences between her

Low-Cost and High-Cost strategies are statistically significant in ORDER and 1-2-L1 only.18

Result 1.2: When the Nash equilibrium corresponds to Level-3 rather than Level-4, Player 4 behavior is consistent with the predicted comparative static effect of the capacity constraint.

Results for P4 are also consistent with Nash comparative static predictions in 1-2-L1, where the

choices of P1 and P2 are independent. In this treatment, P4’s Low-Cost strategies are significantly

higher than his No-Cost strategies, making him less likely to follow P3 with a low waiting cost

than with no waiting cost. Because the choices of P1 and P2 are independent in this treatment,

fewer iterations of best-response are required to reach Nash than in the others. In 1-2-L1, P4’s

Nash strategy corresponds to Level-3, while in the other three treatments it corresponds to Level-4.

Hence, P4 responds to the capacity constraint as predicted when the Nash strategy involves a lower

level of thinking.

18Using Wilcoxon rank-sum tests taking subject-level means as the unit of observation, I find no significant differences between P3’s mean strategies in BASE and the other treatments. The same is true for P4, except that P4’s

mean No-Cost strategy is significantly lower in ORDER than in BASE. Because the underlying game is identical

in BASE and ORDER, I should observe no differences between these two treatments unless behavior is affected by

the order in which cost levels are played. I find evidence that this difference is due to hysteresis of strategies across

cost levels when they are played in random sequence. Though the proportion of No-Cost strategies equal to 0 or 100

in ORDER (16.6%) is similar to the rate of such strategies in Celen and Kariv (2005) (17.5%), the proportions of

such strategies in BASE (30.3%) and 1-2-L1 (28.0%) are much higher. Hence, strategies in rounds with an interior

equilibrium are distributed more in the interior of the interval when these rounds are played before rounds with a

corner equilibrium (ORDER) than when rounds are played in random sequence (BASE/1-2-L1). Because P4’s mean

No-Cost strategy is a more meaningful representation of P4 behavior in ORDER than in the other treatments, it is

not surprising that the mean is significantly closer to the Nash strategy in this treatment than in the others.

19
5.2 Social Learning with Computers vs. Human Players

In this section, I ask whether strategies exhibit more social learning when P1 and P2 are computers

with fixed, commonly known strategies than when they are human subjects. In the standard NoCost setting of all four treatments, mean strategies of P3 and P4 overweight private information

compared to the Nash prediction.19 None of the mean No-Cost strategies are significantly less than

50 at the .1 level., where 50 is the strategy which makes a choice based entirely on the private

signal.20 However, the distributions of strategies in Figures 5.1 and 5.2 reveal that behavior varies

widely in some treatments and cost levels, particularly for P4, whose distributions are largely bimodal. This finding raises an important methodological issue in analyzing herding experiments

that use strategy elicitation: the mean strategies may not tell the whole story, so the distributions

of strategies must be studied.21 To study between-treatment differences in these distributions, I

compare the proportions of observed strategies within an interval of 5 about a given benchmark.

This approach allows for some error about the benchmark while maintaining a 95% chance that

strategies in the chosen interval choose the same action as the benchmark strategy.22

Result 2: When the first two players are humans, more strategies condition only on private information and less strategies condition only on social learning than when they are computers.

In 1-2-H, strategies of human subjects in both P1 and P2 roles vary widely, with standard

deviations of 25.9 and 31.5, respectively. The mean strategy for P1 is 50.3, almost exactly matching

19In Appendix D, I explore the impact of risk aversion on the Bayesian Nash equilibrium. Risk-aversion generally

has a negligible impact on equilibrium strategies, so this does not appear to explain the observed deviations from

Nash predictions.

20This result is consistent with those of other studies with human preceding players such as Celen and Kariv (2005),

who report means equivalent to 44.6 for P3 and 43.8 for P4 in terms of my parameters.

21A substantial proportion of subjects in both roles use No-Cost strategies which are not even minimally consistent

with the Nash, choosing contrary to their immediate predecessor given an agreeing private signal. In Appendix E, I

study the strategies of those subjects whose mean No-Cost strategy satisfies basic rationality in the sense that it does

not choose contrary to the immediate predecessor given an agreeing signal, i.e., the mean No-Cost strategy is less

than or equal to 50. Among these subjects, P4 strategies are more consistent with equilibrium predictions but there

is little difference for P3. I also find that for P4, measures of cognitive ability increase the likelihood of satisfying the

basic rationality condition.

22For between-treatment differences in proportions of strategies falling within a given interval, I assess statistical

significance as follows. An indicator variable is constructed taking the value 1 if a strategy falls in the specified

interval and 0 otherwise. Observations are subject-level means of this indicator variable at each cost level. Wilcoxon

rank-sum tests on these subject-level means determine significance.

20
the Nash prediction of 50, but the mean strategy for P2 is 39.6, significantly greater than the Nash

prediction of 25 (p = .0079).23 Because P1 and P2 are likely to deviate from the Nash strategy in

this treatment, relying more on private information than the Nash equilibrium is consistent with

rational expectations for P3 and P4. However, overweighting private information for this reason is

not consistent with rational expectations in treatments where P1 and P2 are computer players.

Accordingly, the proportion of P3’s No-Cost strategies that condition only on the private signal

is higher when P1 and P2 are humans than when they are computers. The proportion of P3

strategies in the interval [47.5,52.5] is 23.2% in 1-2-H and 6.2% in BASE, though this difference is

not statistically significant (p = .2932). I also find that the proportion of P3’s No-Cost strategies

that condition only on the preceding player’s choice (overweighting social learning) is lower when

P1 and P2 are humans than when they are computers. The proportion of P3’s No-Cost strategies

falling in the interval [0,5] is 14.3% in 1-2-H and 29.8% in BASE, a statistically significant difference

(p = 0.0782). These findings indicate that reducing strategic uncertainty by using predictable

computer players increases social learning, but inconsistency with Nash equilibrium persists.24

When preceding players are humans rather than computers, the tendency observed in No-Cost

rounds to make choices based on factors other than social learning is also present in Low- and

High-Cost rounds. In these rounds, this tendency is expressed in strategies which respond to the

waiting cost by choosing contrary to the preceding player unconditionally. For both P3 and P4,

the proportions of Low-Cost strategies in [95,100] are higher in 1-2-H than in BASE (57.1% vs.

36.0% for P3 (p = 0.1466) and 48.2% vs. 27.1% for P4 (p = 0.0336)). Similarly, the proportions

of High-Cost strategies in [95,100] are higher in 1-2-H than in BASE for both players (67.9% vs.

47.7% for P3 (p = 0.1163) and 53.6% vs. 27.1% for P4 (p = 0.0313)).

5.3 Level-k Behavior

In this section, I explore the extent to which the observed behavior is conisistent with Level-k

thinking as an alternative model of behavior to Nash equilibrium. P4 behavior that is consistent

23The distributions of these strategies are shown in Figure C.3 in Appendix C.

24March et al. (2012) also find that using computer players in a herding experiment increases social learning, but

that even in this condition a substantial proportion of subjects overweight private information while others exhibit

cascade behavior.

21
Table 5.2: Expected Payoffs of Player 4 Strategies Given Actual Player 3 Behavior

Empirically Expected Payoff

Treatment Cost Optimal Strategy Optimal Nash Level-1 Level-2

No 47 55.7 52.6 55.3 52.8

BASE Low 36 39.0 37.0 37.2 37.4

High 16 14.4 7.4 7.6 7.9

No 36 58.7 56.4 57.3 56.3

ORDER Low 79 46.4 45.6 40.7 45.3

High 5 21.1 13.6 1.4 17.6

No 41 56.8 54.2 54.4 55.3

1-2-L1 Low 73 42.4 41.0 40.2 40.7

High 67 23.5 20.3 14.7 20.3

with Level-k predictions is relatively easy to identify in High-Cost rounds of BASE and ORDER,

where there is a maximum difference between the Level-1 strategy and the strategy of Level-2 and

higher. I observe substantial proportions of strategies consistent with both. In High-Cost rounds

of BASE and ORDER, respectively, 27.1% and 21.8% of P4 strategies fall in the interval [95,100],

consistent with Level-1. Similar proportions (24.8% in BASE; 28.6% in ORDER) fall in the interval

[0,5], consistent with Level-2 and higher.25

Before attributing these results to bounded rationality rather than rational expectations, it is

necessary to determine what strategies would have been optimal for P4 given the actual behavior of

preceding players. I determine the empirically optimal strategies for P4 at each cost level of BASE,

ORDER and 1-2-L1 given the actual strategies of P1, P2 and P3 and the actual signal draws of all

four players used in the experiment. Table 5.2 reports P4’s optimal strategy at each cost level of

these treatments along with his expected payoff per round from playing the empirically optimal,

Nash, Level-1 and Level-2 strategies given the histories of play in the data.

Result 3.1: The substantial proportion of strategies consistent with Level-1 when the waiting cost is

high (27.1% in BASE, 21.8% in ORDER and 29.5% in 1-2-L1) are not rationalized by the actual

behavior of preceding players.

25Penczynski (2012) surveys the experimental herding literature through the lens of the level-k model and finds

that approximately 43% of the data from 13 different studies is consistent with Level-1, while much of the remaining

data is consistent with Level-2.

22
Table 5.3: Player 4 Subjects in BASE/ORDER by Minimum Mean Absolute Deviation

High-Cost No-Cost Min. MAD

Min. MAD Nash Level-1 Total

Nash 19 (44.2%) 24 (55.8%) 43

Level-1 9 (23.1%) 30 (76.9%) 39

Total 28 (34.1%) 54 (65.9%) 82

Percentage of row total in parentheses.

Given the realized signals and choices of preceding players in the experiment, there is little

difference in expected payoff to P4 from playing each of the three predicted strategies. These

expected payoffs are close to the expected payoff of the empirically optimal strategy in most cases.

The exception is High-Cost rounds, where the expected payoff of the Level-1 strategy is substantially

smaller than those of the empirically optimal strategies. In High-Cost rounds of BASE and ORDER,

the empirically optimal strategies (16 in BASE; 5 in ORDER) are much closer to the Nash strategy

(0) than the Level-1 strategy (100). Hence, in settings where the distribution of P4 strategies is

bimodal, the Nash strategy performs better than Level-1 and is approximately optimal given the

actual behavior of preceding players. This finding indicates that P4 strategies consistent with the

Level-1 prediction are more plausibly attributed to bounded rationality than rational expectations.

Because of the heterogeneity of P4 strategies in High-Cost rounds of BASE and ORDER, it is

useful to examine the subject-level data. If a subject’s High-Cost strategies are clearly distinguished

as consistent with Level-1 or with higher levels, the experimental design allows me to analyze how

the same subject behaves at other cost levels, including the standard No-Cost setting. I study the

within-subject relationship between P4’s High-Cost and No-Cost strategies in these treatments by

measuring each P4 subject’s proximity to the Level-1 and Nash predictions at these two cost levels.

An individual subject’s proximity to a given theoretical prediction is measured using the Mean

Absolute Deviation (MAD) from that prediction of their six reported strategies at that cost level.

Table 5.3 presents a Markov transition matrix which tallies P4 subjects in BASE and ORDER

by which of their MAD from the Nash and their MAD from the Level-1 prediction is smaller in

High-Cost rounds (rows) and in No-Cost rounds (columns).

23
Result 3.2: If Player 4’s strategies are closer to Level-1 than Nash when the waiting cost is high,

his strategies are more likely to be closer to Level-1 than Nash in the standard setting.

Consistent with the bimodality of the distributions of P4 strategies, 47.6% of P4 subjects in

BASE and ORDER use strategies closer to the Level-1 prediction than the Nash prediction in

High-Cost rounds. This split of P4 subjects reveals an interesting correlation with their strategies

in the standard herding game of No-Cost rounds. Of those whose High-Cost strategies are closer

to Level-1 than Nash, 76.9% use strategies which are also closer to the Level-1 prediction than the

Nash prediction in No-Cost rounds. According to McNemar’s test, this relationship across cost

levels is statistically significant (p = .0135). Hence, separating subjects by their behavior in a

modification of a standard herding game, where Level-1 strategies are easily distinguishable from

strategies of higher levels of thinking, allows me to predict which subjects are more likely to use

strategies closer to the Level-1 than Nash in the standard setting, where the two types of behavior

are much more difficult to distinguish.26

Result 3.3: Differences in Player 4 strategies between when the the first two players’ choices are

independent and when they are correlated are consistent with Level-k thinking.

The bimodality of P4 strategies in BASE is meaningful when compared to the distribution of P4

strategies in High-Cost rounds of 1-2-L1. The Level-1 strategy is 100 in High-Cost rounds of both

1-2-L1 and BASE, and consistently, the proportions of P4 strategies in [95,100] in these treatments

(29.5% and 27.1%, respectively) are not significantly different (p = 0.9731). However, the strategy

of Level-2 and higher in 1-2-L1 is to choose contrary to P3 for all but strong agreeing signals, while

in BASE it is to follow P3 unconditionally. Consistently, only 9.4% of P4 strategies fall in [0,5]

in High-Cost rounds of 1-2-L1, significantly less than the 24.8% observed in this interval in BASE

(p = 0.0078). This difference between treatments indicates that while many P4 subjects behave

26Stahl and Wilson (1995) find some evidence that subjects’ level of thinking is consistent across different games.

In contrast, Georganas et al. (2012) find that level of thinking may or may not be consistent across similar games,

and that subjects’ level in one family of games may not be consistent with their level in another family of games.

24
consistently with Level-1, a substantial proportion use strategies which differ between treatments

as predicted by higher levels of thinking.27

5.4 Analysis of ACT/SAT Scores

In this section, I test for a relationship between subjects’ strategies and their cognitive ability, as

measured by ACT and SAT scores. I obtained consent from 59.9% of subjects from BASE, ORDER

and 1-2-L1 to access their ACT scores, SAT scores and major field of study through the Ohio State

University registrar.28 ACT scores were obtained for 45.9% of subjects from these treatments,

while SAT scores were obtained and SAT-ACT concordance scores29 used for another 14.0% of

these subjects.30

As in Section 5.3, I use the Mean Absolute Deviation (MAD) of a subject’s strategies from a

given theoretical prediction to measure proximity to that prediction. Because MAD is essentially a

fractional measure, I use the log-odds ratio of the MAD, (log(

MAD

100−MAD )) as the dependent variable

in these regressions to avoid predicted values outside the interval of possible strategies.31 Using

OLS, I regress this measure of proximity to a given theoretical prediction on indicators for whether

a subject has an ACT or SAT-ACT concordance score in the top 5% of all test-takers or below

the top 20% of all test-takers32 and an indicator for having a quantitiative major, including math,

science, engineering and economics. For each theoretical prediction, I conduct the regression at

three levels of observation: using the MAD of a P4 subject’s strategies from that prediction over

(1) all cost levels for subjects in BASE, ORDER and 1-2-L1, (2) No-Cost rounds only for subjects

in BASE and ORDER, and (3) High-Cost rounds only for subjects in BASE and ORDER. Table

27This result is consistent with findings of a p-beauty contest experiment by Coricelli and Nagel (2009). Subjects

in this experiment whose strategies correspond to higher levels of thinking against human opponents tend to play

a Level-1 strategy when matched with a randomizing computer, while subjects exhibiting lower levels of thinking

behave similarly in both treatments.

28This is one of only a few studies in the experimental economics literature to use verified ACT or SAT scores (as

opposed to self-reported scores) as a measure of cognitive ability. See Benjamin and Shapiro (2005), Casari et al.

(2007), Ivanov et al. (2009, 2010) and Jones (2012) for other examples.

29See http://professionals.collegeboard.com/profdownload/act-sat-concordance-tables.pdf for SATACT concordance tables.

30Summary statistics on these test scores are reported in Table C.2 in Appendix C.

31See Papke and Wooldridge (1996).

32ACT percentile is the appropriate measure because ACT scores are based on a rank-order scale and not an

additive scale.

25
Table 5.4: OLS Regressions: Log-Odds Ratio of Player 4 Mean Deviation on Test Scores/Major

Nash Level-1 Level-2

Variable Estimate (S.E.) Estimate (S.E.) Estimate (S.E.)

Mean deviation over all cost levels, BASE/ORDER/1-2-L1

ACT in Top 5% -0.290* (0.173) 0.438** (0.214) -0.237 (0.172)

ACT below Top 20% -0.222 (0.182) 0.144 (0.225) -0.223 (0.182)

Quantitative Major -0.180 (0.215) 0.183 (0.266) -0.235 (0.214)

Constant -0.347*** (0.111) -0.711*** (0.137) -0.426*** (0.110)

Observations 72 72 72

Mean deviation over No-Cost strategies only, BASE/ORDER

ACT in Top 5% -0.574** (0.280) -0.274 (0.275) -0.463 (0.289)

ACT below Top 20% -0.387 (0.347) -0.251 (0.339) -0.519 (0.363)

Quantitative Major 0.006 (0.357) -0.137 (0.350) 0.134 (0.381)

Constant -0.263 (0.177) -0.758*** (0.173) -0.503*** (0.183)

Observations 51 51 50

Mean deviation over High-Cost strategies only, BASE/ORDER

ACT in Top 5% -1.019** (0.489) 1.019** (0.489) -1.019** (0.489)

ACT below Top 20% -0.366 (0.579) 0.366 (0.579) -0.366 (0.579)

Quantitative Major -0.622 (0.598) 0.622 (0.598) -0.622 (0.598)

Constant 0.525* (0.296) -0.525* (0.296) 0.525* (0.296)

Observations 49 49 49

5.4 reports the results of these regressions.

Result 4: Player 4 subjects with an ACT score in the top 5% of all test-takers use strategies closer

to the Nash prediction and farther from the Level-1 prediction.

The regressions using the MAD over all cost levels for P4 subjects in BASE, ORDER and

1-2-L1 reveal evidence of a positive correlation between cognitive ability and proximity to the

Nash prediction and a negative correlation between cognitive ability and proximity to the Level-1

prediction. Specifically, having an ACT or SAT-ACT concordance score in the top 5% of all testtakers makes the MAD from the Nash strategy significantly smaller (at the .1 level) and the MAD

from the Level-1 strategy significantly larger (at the .05 level). This result suggests that a subject

with high cognitive ability is more likely to learn through inference from P3’s observed action and

26
less likely to behave as if P3 chose randomly.33

The same regressions are also conducted for P4 strategies from BASE and ORDER only, separately for No-Cost and High-Cost rounds. Unlike the first set of regressions, the theoretical

predictions used to calculate MAD do not vary across observations in these regressions. Consistent

with results from the pooled data, I find that having an ACT or SAT-ACT concordance score in

the top 5% of all test-takers makes the MAD from the Nash strategy significantly smaller (at the

.05 level) in No-Cost rounds of BASE and ORDER. The effect on proximity to the Level-1 strategy

is not significant in these rounds, but a weaker relationship with proximity to Level-1 is expected

because the Level-1 strategy is at the midpoint of the strategy interval, which limits the magnitude

of variation about this prediction.

In High-Cost rounds of BASE and ORDER, the maximum difference between the Nash and

Level-1 strategies allows me to more easily differentiate between levels of thinking that motivate

behavior. The regressions on P4 High-Cost strategies in these rounds reveal a clear relationship

between test scores and proximity to the Nash and Level-1 predictions, suggesting that subjects

with high cognitive ability use strategies significantly closer to the Nash prediction and farther from

the Level-1 prediction (at the .05 level) where these two benchmarks are quite distinct. This result

provides an explanation for the dichotomy of P4 behavior present in the data from these rounds.

Regressions using MAD from the Level-2 strategy (where it is different from Nash) as the

dependent variable do not yield any significant results. Furthermore, having a score below the

top 20% of all test-takers or a quantitative major does not significantly affect the proximity of P4

strategies to any of the three theoretical predictions. All of the above regression results are robust

to the exclusion of test score or major from the equation, which does not change the significance

of remaining coefficients.

By a strict interpretation of the theory, the Level-k model does not apply to P3’s problem in

33This result is consistent with the findings of Ivanov et al. (2009, 2010) that subjects in their endogenous-timing

investment experiment with high SAT scores are more likely to respond as predicted to informational externalities.

It is also consistent with the results of Burnham et al. (2009) and Gill and Prowse (2012), who find that subjects

who guess lower in a p-beauty contest also perform better on tests of intelligence. In a p-beauty contest experiment

with fMRI imaging by Coricelli and Nagel (2009), subjects with greater brain activity related to mental calculation

use higher-level strategies. Camerer et al. (2004) also observe differences in level of thinking in p-beauty contests

between subject pools of different abilities. In contrast, Georganas et al. (2012) find little evidence of a relationship

between levels of thinking in other types of games and scores in several tests of cognitive ability.

27
BASE, ORDER and 1-2-L1 because she observes the choices of computer players with commonly

known strategies, so there is no room for inconsistent beliefs about their rationality. However, I also

study whether P3 subjects behave as if they hold incorrect beliefs about these computer players’

strategies.34 By this more liberal intepretation, the Level-k benchmarks for P3 would be the same

as those in 1-2-H (see Figure 3.4). I conduct the same regression on the pooled P3 data from all

cost levels of BASE, ORDER and 1-2-L1 as is conducted for P4.35 These regressions indicate that

having an ACT score in the top 5% of all test-takers significantly reduces P3’s MAD from the Nash

and Level-1 strategies (at the .1 level). These effects are in the same direction because the Level-1

strategy is relatively close to the Nash strategy for P3. Having a score below the top 20% has

no significant effect, but having a quantitative major significantly reduces a subject’s MAD from

Nash, Level-1 and Level-2 (at the .1, .01 and .1 levels, respectively). Because P3’s payoffs and

information are determined entirely by hers and the computer’s decisions, her task is essentially

a one-person decision problem. Hence, it makes sense that having a quantitative major, a proxy

for the subject’s numeracy and ability to read graphs, is as important if not more important than

cognitive ability in determining her proximity to the Nash prediction.

6 Conclusion

This research provides insight into why people tend to deviate from Bayesian rationality in situations where it requires making inferences from the observed choices of others. Many people simply

make no such inferences, behaving instead as if others choose randomly. On the other hand, a substantial proportion of people do gain information from observing others when they have sufficient

confidence about the strategies others are following. When deviations occur, these findings suggest

that those with high cognitive ability are less likely to be responsible.

There are many potential applications and directions for future research extending from these

results, including financial markets and other contexts where inferences based on observed choices of

others are critical to decision-making. The insights of this experiment can be applied more directly

34See Charness and Levin (2009) for an experiment in which Cursed play persists in one-person decision problems

where beliefs about the rationality of others are not relevant.

35Results are reported in Table C.3 in Appendix C.

28
to network design and other problems where congestion is an important concern. In such contexts,

results suggest that achieving the desired outcome should not hinge upon the assumption that

people will sort themselves with common knowledge of rationality. Instead, many people should be

expected to respond without higher-level reflection on the motives of others.

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32
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145.

A Derivation of Risk-Neutral Bayesian Nash Equilibrium

Suppose xn−1 = R. Risk-neutral player n chooses alternative R if and only if the following holds:

E[

P4

i=1 θi

4 − Cn(x1, ..., xn−1, R)|θn, xn−1 = R]

≥ E[1 −

P4

i=1 θi

4 − Cn(x1, ..., xn−1, L)|θn, xn−1 = R].

Because θn+1, ..., θ4 are independent with mean 1

2

, this inequality can be re-written as,

E[

Pn−1

i=1 θi+θn+ 1

2

(4−n)

4 − Cn(x1, ..., xn−1, R)|θn, xn−1 = R]

≥ E[1 −

Pn−1

i=1 θi+θn+ 1

2

(4−n)

4 − Cn(x1, ..., xn−1, L)|θn, xn−1 = R],

which simplifies to:

θn ≥

n

2

− E[

nX−1

i=1

θi − 2(Cn(x1, ..., xn−1, R) − Cn(x1, ..., xn−1, L))|xn−1 = R].

33
Hence, player n uses a cutoff strategy given by:

xn(xn−1 = R) =







R if θn ≥ ˆθn

L if θn < ˆθn

,

where ˆθn =

n

2 − E[

Pn−1

i=1 θi − 2(Cn(x1, ..., xn−1, R) − Cn(x1, ..., xn−1, L))|xn−1 = R]. The problem

is symmetric for xn−1 = L, so in this case the player follows a strategy given by:

xn(xn−1 = L) =







R if θn ≥ 1 − ˆθn

L if θn < 1 − ˆθn

.

I now derive the Risk-Neutral Bayesian Nash Equilibrium strategies for Players 1 through 4.

Player 1: Because θ2, θ3 and θ4 are drawn independently and uniformly from [0,1], it follows

trivially that ˆθ1 =

1

2

holds.

Player 2: Because neither option’s capacity can be reached after only one player’s choice,

E[θ1−2(C2(x1, R)−C2(x1, L))|x1 = R] = E[θ1|x1 = R] = 3

4

holds, which imples that ˆθ2 = 1−

3

4 =

1

4

holds.

Player 3: By Bayes’ Rule it follows from ˆθ1 and ˆθ2 that P r(x1 = R|x2 = R) = 3

4

holds. Hence,

E[θ1+θ2|x2 = R] = 3

4E[θ1+θ2|x1 = x2 = R]+ 14E[θ1+θ2|x1 = L, x2 = R] = 34

(

5

8+

6

8

)+ 1

4

(

7

8+

2

8

) = 21

16

holds. Also, E[2(C3(x1, R) − C3(x1, L))|x2 = R] = 2 3

4

c =

6c

4

holds. Therefore, ˆθ3 is equal to the

minimum of 3

2 −

21

16 +

6c

4 =

3+24c

16 and 1 because 3+24c16 > 1 implies that it is never optimal for Player

3 to follow Player 2. 3+24c

16 > 1 holds if and only if c > 1324 is satisfied.

Player 4: By Bayes’ Rule it follows from ˆθ1,

ˆθ2 and ˆθ3 that if c ≤

13

24 is satisfied then P r(x2 =

R|x3 = R) = 13−24c

16 holds. Hence, we have,

E[θ1 + θ2 + θ3|x3 = R]

=

13−24c

16 (

3

4E[θ1 + θ2 + θ3|x1 = x2 = x3 = R] + 14E[θ1 + θ2 + θ3|x1 = L, x2 = x3 = R])

+

3+24c

16 (

1

4E[θ1 + θ2 + θ3|x1 = x3 = R, x2 = L] + 34E[θ1 + θ2 + θ3|x1 = x2 = L, x3 = R])

,

where the following hold if c ≤

13

24 is satisfied:

34
E[θ1 + θ2 + θ3|x1 = x2 = x3 = R] = 5

8 +

6

8 +

19+24c

32 ;

E[θ1 + θ2 + θ3|x1 = L, x2 = x3 = R] = 7

8 +

2

8 +

19+24c

32 ;

E[θ1 + θ2 + θ3|x1 = x3 = R, x2 = L] = 1

8 +

6

8 +

29−24c

32 ;

E[θ1 + θ2 + θ3|x1 = x2 = L, x3 = R] = 3

8 +

2

8 +

29−24c

32 .

Some algebra yields E[θ1+θ2+θ3|x3 = R] = 473−576c

2

256 . In addition, the following holds: E[2(C4(x1, x2, R)−

C4(x1, x2, L))|x3 = R] = 2( 13−24c

16 c +

1

4

3+24c

16 c −

3

4

3+24c

16 c) = 368c−1152c

2

256 . Therefore, if c ≤

13

24 is satisfied then ˆθ4 = 2 −

473−576c

2

256 +

368c−1152c

2

256 =

39+368c−576c

2

256 holds. However, if c > 1324 is satisfied,

then the following holds:

P r(x2 = R|x3 = R) = 0;

E[θ1 + θ2 + θ3|x1 = x3 = R, x2 = L] = 1

8 +

6

8 +

1

2

;

E[θ1 + θ2 + θ3|x1 = x2 = L, x3 = R] = 3

8 +

2

8 +

1

2

.

In this case, E[θ1 + θ2 + θ3|x3 = R] = 19

16 and E[2(C4(x1, x2, R) − C4(x1, x2, L))|x3 = R] =

2( 1

4

c −

3

4

c) = −c hold. Therefore, if c > 13

24 is satisfied then ˆθ4 is equal to the maximum of

2 −

19

16 − c =

13−16c

16 and 0 because 13−16c16 < 0 implies that Player 4 should always follow Player 3.

13−16c

16 < 0 holds if and only if c > 1316 is satisfied.

Individual rationality is satisfied trivially for Players 1 and 2 because they never incur the

waiting cost and for Player 3 because she can always avoid the cost by choosing contrary to Player

2. For Player 4, the individual rationality condition for choosing alternative L given x3 = R,

E[

P4

i=1 θi

4 −C4(x1, x2, x3, R)|θ4, x3 = R] ≥ 0, can be solved for the condition, 473−880c+576c

2 ≥ 0,

which holds for all c ∈ [0, 1].

B Instructions and Screenshots

Instructions for the BASE treatment are reprinted below. The instructions for 1-2-L1 are identical

except for the graph depicting the strategies of computer Players 1 and 2. The ORDER and 1-2-H

instructions use the same language as below with the necessary re-arranging and modifications.

35
This is an experiment in the economics of decision making. If you follow these instructions

carefully and make good decisions, you may earn a considerable amount of money which will be

paid to you in cash at the end of the experiment.

The experiment is divided into 18 rounds. At the beginning of the experiment, you will be

randomly assigned a role of either Player 3 or Player 4, and you will keep the same role in every

round of the experiment. At the beginning of each round, you will be matched randomly and

anonymously with a player of the other role, creating a match between Player 3 and Player 4.

The match in each round is determined independently of matches in previous rounds. You and the

person with whom you are matched will each make a choice after choices are made by two computer

players, Player 1 and Player 2.

Each player is asked to choose one of two alternatives, LEFT and RIGHT. Choices are made

in sequence: computer Player 1 chooses first, then computer Player 2, followed by human Player 3

and finally human Player 4.

Each player receives a private signal, which is a number drawn randomly and uniformly from

the interval [0,100], independent of the private signals drawn for the other players. That is, for

each player, each number in the interval [0,100] is equally likely to be drawn as that players private

signal, regardless of which numbers are drawn for the other players. All players see only their own

signal and do not see the signals of any other players.

Players 2, 3 and 4 see the choice of the player who chooses immediately before they do, but

not the choices of the other players. That is, Player 2 sees the choice of Player 1, Player 3 sees the

choice of Player 2, and Player 4 sees the choice of Player 3. Players see the choice of the preceding

player (LEFT or RIGHT), but not the private signal of the preceding player.

When it is your turn to make a choice, you will see the choice of the preceding player (LEFT or

RIGHT) on your computer screen, and you will be asked to enter a critical number between 0 and

100 before your private signal is shown to you. If your private signal turns out to be LESS than

this number, your choice will be LEFT, and if your private signal turns out to be GREATER than

this number, your choice will be RIGHT. In other words, when you enter this critical number, it

means that for each possible private signal greater than this number, you would choose RIGHT,

36
and for each possible private signal less than this number, you would choose LEFT. After you enter

this number, your private signal will be drawn and your choice will be made for you according to

the number you enter. When the round ends, your private signal will be shown to you along with

your chosen alternative.

Payoffs for this experiment are denominated in Experimental Currency Units (ECUs). Your

net payoff in ECUs in a given round is equal to the gross value of your chosen alternative minus

any cost you incur.

The gross value of RIGHT in a given round is equal to the average of the private signals drawn

for all four players in that round. The gross value of LEFT is equal to 100 minus the average of

the private signals drawn for all four players in that round. For example, if the four private signals

drawn are 11, 42, 83 and 20 then the average of the signals is (11 + 42 + 83 + 20)/4, which is

equal to 39. Hence, the gross value of RIGHT is 39 ECUs and the gross value of LEFT is 61 ECUs

(100 39 = 61) in that round.

Players 3 and 4 incur a cost if they choose the same alternative as at least two of the preceding

players. The cost in each round will be equal to 0, 35 or 85, and the cost is the same for both

Players 3 and 4 in any given round. For example, suppose the cost is 35. If both Players 1 and 2

chose the same alternative as Player 3 in that round then 35 ECUs are subtracted from the gross

value of Player 3’s chosen alternative to determine her net payoff for the round. Otherwise, Player

3 does not pay the cost. If at least two of Players 1, 2 and 3 chose the same alternative as Player

4 in that round, 35 ECUs are subtracted from the gross value of Player 4s chosen alternative to

determine her net payoff for the round. Otherwise, Player 4 does not pay the cost. Players 1 and

2 never incur a cost.

The computer players, Player 1 and Player 2, are programmed to choose according to the rules

shown in the graph below, which includes Player 1s private signal on the horizontal axis and Player

2s private signal on the vertical axis. The solid line inside the graph represents the rule followed by

computer Player 1. If it receives a private signal to the right of this line, it chooses RIGHT, and if

it receives a private signal to the left of this line, it chooses LEFT. The dotted line inside the graph

represents the rule followed by computer Player 2. If it receives a signal above this line, it chooses

37
RIGHT, and if it receives a signal below this line, it chooses LEFT. The regions of the graph are

labeled by the choices Players 1 and 2 make for each pair of Player 1 and Player 2 signals in that

region.

The cost will be 0, 35 and 85 for six rounds each and will be known (and the same) for both

players, but the order in which these 18 rounds will be played is determined randomly. For each of

the three cost levels, one of the six rounds played at that cost will be drawn randomly. You will

be paid your earnings for only these three rounds. Because you do not know which rounds will be

chosen for payment, you should play each round as if you will be paid for it. At the end of the

experiment, you will be paid $0.10 per ECU earned in the three rounds selected for payment plus

the starting balance of 50 ECUs. You will also receive the participation fee of $5.

Before we begin, we will play two trial rounds that do not count for payment so that you can

get familiar with the software. Your role in the trial rounds (Player 3 or Player 4) will be the

same as in the rest of the experiment. If you have any questions about the instructions, please

ask them now. If you have questions during the experiment, please raise your hand and one of

the experimenters will assist you. Please turn off your cell phones at this point. You should not

communicate with any of the other participants for the duration of the experiment.

38
Figure B.1: Choice Screen

39
Figure B.2: Feedback Screen

40
C Figures and Tables

Figure C.1: Player 3 Strategies by Player 2 Choice

41
Figure C.2: Player 4 Strategies by Player 3 Choice

42
Figure C.3: Player 1 and Player 2 Strategies in 1-2-H

Table C.1: Mean Strategies, First and Last Round at Each Cost Level

Player 3 Player 4

Treatment Setting Nash First Last Nash First Last

BASE No-Cost 19 49.0 37.5 15 58.7 51.0

Low-Cost 71 67.6 73.5 39 61.2 50.6

High-Cost 100 60.9 78.6 0 53.5 50.1

ORDER No-Cost 19 54.6 45.6 15 42.3 46.3

Low-Cost 71 61.1 56.5 39 48.5 49.1

High-Cost 100 67.1 82.1 0 54.9 46.6

1-2-L1 No-Cost 25 52.5 46.8 19 50.6 53.7

Low-Cost 60 61.5 77.1 59 56.7 63.3

High-Cost 100 76.2 80.0 75 59.3 66.6

1-2-H No-Cost 19 63.9 47.6 15 61.8 48.7

Low-Cost 71 66.0 91.0 39 59.2 58.2

High-Cost 100 67.4 87.7 0 81.9 54.9

Table C.2: ACT and SAT-ACT Concordance Score Summary Statistics

Median 27

Mean 27.58

Std. Err. 0.289

% of BASE/ORDER/1-2-L1 Subjects’ Scores Obtained* 59.9%

% with ACT 45.9%

% with SAT Only** 14.0%

% with Score in Top 5% 15.3%

% with Score below Top 20% 14.0%

*64.0% of BASE, 62.8% of ORDER and 52.6% of 1-2-L1 subjects.

**SAT-ACT concordance scores used.

43
Table C.3: OLS Regressions: Log-Odds Ratio of Player 3 Mean Deviation on Test Scores/Major

Nash Level-1 Level-2

Variable Estimate (S.E.) Estimate (S.E.) Estimate (S.E.)

Mean deviation over all cost levels, BASE/ORDER/1-2-L1

ACT Score in Top 5% -0.246* (0.139) -0.309* (0.162) -0.213 (0.135)

ACT Score below Top 20% 0.072 (0.144) 0.106 (0.168) 0.035 (0.139)

Quantitative Major -0.315* (0.158) -0.605*** (0.185) -0.296* (0.153)

Constant -0.772*** (0.094) -0.825*** (0.109) -0.788*** (0.091)

Observations 73 73 73

D Risk Aversion

In this Appendix, I explore the impact of risk-aversion on the Bayesian Nash equilibrium by solving

numerically for the equilibrium strategies of Players 3 and 4 under the assumption that the utility

of choice xn is given by U(xn) = p

π(xn), where π(xn) is the payoff of choice xn and all players

1, .., n − 1 are assumed to behave according to the risk-neutral Nash. These strategies are denoted

by ˆθ

RA

3

and ˆθ

RA

4

and shown below along with their risk-neutral alternatives (ˆθ

RN

3

and ˆθ

RN

4

) below:

ˆθ

RA

3 ≈







.1873 if c = 0

.7316 if c = .35

1 if c = .85

;

ˆθ

RA

4 ≈







.1520 if c = 0

.3865 if c = .35

.1780 if c = .85

;

ˆθ

RN

3 =







.1875 if c = 0

.7125 if c = .35

1 if c = .85

;

ˆθ

RN

4 ≈







.1523 if c = 0

.3798 if c = .35

0 if c = .85

.

These risk-averse strategies bear negligible differences from the risk-neutral strategies with one

exception: the equilibrium strategy for a risk-averse P4 with c = .85 chooses contrary to P3 for

a substantial range of strong disagreeing signals, whereas the risk-neutral strategy is to follow

P3 unconditional on his private signal. Hence, if risk aversion plays an important role in the

experiment, it should express itself in deviations from the Nash only for P4 at a high waiting cost.

44
E Subjects Satisfying Basic Rationality

Table E.1: Effects of Treatment and Cost Level for Subjects Satisfying Basic Rationality

BASE/ORDER 1-2-L1

Prediction No-Cost Low-Cost High-Cost Prediction No-Cost Low-Cost High-Cost

Nash P3 19 71 100 Nash P3 25 60 100

P4 15 38 0 P4 19 59 75

Level-2 P4 25 40 0 Level-2 P4 25 65 75

Level-1 P4 50 85 100 Level-1 P4 50 85 100

Mean Strategy No-Cost Low-Cost High-Cost Mean Strategy No-Cost Low-Cost High-Cost

BASE P3 26.8 <<< 68.7 74.1 1-2-L1 P3 28.3 <<< 74.8 <<< 86.9

P4 35.3 33.2 37.4 P4 40.1 <<< 54.8** << 65.4***

ORDER P3 33.5 <<< 64.4 << 81.2 1-2-H† P3 36.8 61.0 79.2

P4 33.4 < 44.7 44.0 P4 38.8 << 69.7*** 63.8*

Between-cost-level difference significant at: <<< .01 level, << .05 level, < .1 level.

Between-treatment difference (compared to BASE) significant at: *** .01 level, ** .05 level, * .1 level.

†

1-2-H: Nash same as BASE/ORDER; Level-2 same as P3 Nash/P4 Level-2 in 1-2-L1; Level-1 same as P4 Level-1s.

In this Appendix, I study the behavior of subjects whose mean No-Cost strategy satisfies basic

rationality in the sense that it does not choose contrary to the immediate predecessor given an

agreeing signal (i.e., the mean No-Cost strategy is less than or equal to 50). The percentages of

subjects satisfying basic rationality in BASE, ORDER, 1-2-L1 and 1-2-H, respectively, are 58.1%,

59.0%, 59.0% and 50.0% for P3 and 39.5%, 74.4%, 41.0% and 57.1% for P4.36 Table E.1 reports

these subjects’ mean strategies by treatment and cost level.

Differences in mean P3 strategies between cost levels are qualitatively similar to the results obtained from the full data, but larger in magnitude. For P4, Low-Cost strategies differ significantly

from No-Cost strategies as predicted in ORDER, 1-2-L1 and 1-2-H, although the effect remains

insignificant in BASE. P4’s mean High-Cost and Low-Cost strategies in 1-2-L1 also differ significantly as predicted by the Nash equilibrium. I find that P4’s mean High- and Low-Cost strategies

differ significantly between the 1-2-L1 and BASE treatments such that following P3 is less likely in

1-2-L1, as predicted by the Nash. These findings suggest that subjects who satisfy a basic rationality condition in rounds with no waiting cost generally respond to the capacity constraint and

waiting cost as predicted by Nash equilibrium.

In an effort to explain what determines whether subjects satisfy basic rationality, I investigate

36For comparison, Celen and Kariv (2005) report that 60.8% of strategies in their experiment satisfy the same

basic rationality condition.

45
Table E.2: Probits Reporting Marginal Effects of Test Scores/Major on Basic Rationality

Player 3 Player 4

Variable Estimate (S.E.) Estimate (S.E.)

Score in Top 5% 0.247 (0.143) 0.270* (0.134)

Score below Top 20% 0.080 (0.138) -0.031 (0.144)

Quantitative Major -0.029 (0.163) 0.118 (0.169)

Observations 73 72

the relationships between subjects’ academic records and the proximity of their strategies to the

theoretical predictions. Table E.2 reports the results of probit regressions with a dependent variable

taking 1 as its value if a subject satisfies basic rationality and 0 otherwise. Explanatory variables

include indicators for whether a subject has an ACT or SAT-ACT concordance score in the top 5%

of all test-takers or below the top 20% of all test-takers and an indicator for having a quantitiative

major.

For P4, having a test score in the top 5% of all test-takers is estimated to raise the probability

of basic rationality by 27 percentage points (significant at the .1 level).37 The marginal significance

of this estimate combined with its large magnitude suggest that the likelihood of satisfying basic

rationality in rounds with no waiting cost is correlated with cognitive ability for P4. For P3, I find

no significant relationship between cognitive ability and basic rationality, though the magnitude of

the estimate (24.7 percentage points) is similarly large.

37Probit regressions using only test scores or only major as explanatory variables do not yield important differences

from the results of the regressions including all of the explanatory variables presented in Table E.2.

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