GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 177

An Experimental Test of a Committee Search Model

Yoichi Hizen, Keisuke Kawata, and Masaru Sasaki

February 2011; revised September 2012GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

An Experimental Test of a Committee Search Model
Yoichi Hizen

Keisuke Kawatay

Masaru Sasaki

Hokkaido University

Hiroshima University

Osaka University and IZA

September 11, 2012

Abstract
The objective of this paper is to design a laboratory experiment for an in…nite-horizon
sequential committee search model in order to test some of the implications obtained by themodel in Albrecht, Anderson, and Vroman (2010) (AAV). We …nd that, compared with singleagent search, the search duration is longer for committee search under the unanimity rule, but
is shorter for committee search in which at least one vote is required to stop searching. In
addition, according to estimates from round-based search decisions, subjects are more likely
to vote to stop searching in committee search than in single-agent search. This con…rms thatagents are less picky in committee search. Overall, the experimental outcomes are consistent
with the implications suggested by the AAV model. However, despite the prediction from theAAV model, we could not obtain a signi…cant outcome in relation to the size order of the
The authors would like to thank the editor, the associate editor, and two anonymous referees of this journal,
Jim Albrecht, Ryoichi Imai, Toshiji Kawagoe, Noritaka Kudoh, Fumio Ohtake, Katsuya Takii, and Susan Vroman,and participants in the 14th Experimental Social Science Conference, the Fall 2010 Japanese Economic Association
Meeting, the Osaka Workshop on the Economics of Institutions and Organizations, the 2010 International Workshop
on Experimental Economics and Finance, and the 2011 Asia-Paci…c Economic Science Association Meeting forhelpful comments and suggestions on an earlier version of this paper. The usual disclaimer applies. The authors also
gratefully acknowledge the …nancial support of the Ministry of Education, Culture, Sports, Science, and Technology(Grant-in-Aid for Scienti…c Research No. 20530196) and the Global Center of Excellence program at Osaka University.
yCorrespondence:

Graduate School of Social Science, Hiroshima University, 1-2-1 Kagamiyama, Higashi-

Hiroshima, Hiroshima, 739-8525, Japan, E-mail: keisuke@hiroshima-u.ac.jp

1

probabilities of voting to stop searching in committee search for the various plurality voting
rules.
JEL classi…cation: C91, D83
Keywords: experiments, committee search, plurality voting rules.

2

1

Introduction

The decision mechanism of agents regarding whether to stop or continue searching has been considered in many …elds of economics, including labor economics, monetary economics, macroeconomics,
and industrial organization. Very recently, there has been an emerging interest in committee search,
in which a decision is made by a group of multiple agents rather than by a single agent. An evolving theoretical literature duly analyzes the properties of decision-making in the case of committee
search (Albrecht, Anderson, and Vroman, 2010; Compte and Jehiel, 2010). However, to our best
knowledge, no corresponding empirical studies have been conducted, mainly because it is generallydi¢ cult to collect data on committee search processes. This paper is thus the …rst attempt to provide experimental evidence on committee search and to test the theoretical implications obtainedby the committee search model in Albrecht et al. (2010) (hereafter AAV). Overall, we …nd that
our experimental outcomes are consistent with those obtained by the AAV model.
In the AAV model, a group engages in search activity to …ll a vacant employment position or
searches for a new house as a family. The members of the group or family then decide by votewhether to hire a newly encountered worker or to purchase a house. The AAV model assumes thatmembers are homogeneous with respect to preferences and that each member draws a value froman identical and independent distribution across members. The model then compares the member’s
reservation value in single-agent search and committee search in an environment where the drawn
value di¤ers among the members under various plurality voting rules. The main predictions of
the AAV model are that members are less picky in committee search than in standard single-agent
search in the sense that each member’s threshold is lower and that the members’thresholds vary by
voting rule. Another implication obtained in the AAV model is that the search duration increases
with the number of votes required to stop the committee search process.
The search environment characterized in the model is usually far removed from the environment
observed from the micro data. Therefore, the search environment cannot be perfectly duplicatedusing micro data. However, we can recreate this search environment in the laboratory using
controlled treatments. In recent years, many studies have been devoted to experimental analysis

3

of the single-agent search model (Cox and Oaxaca, 1989 ; Harrison and Morgan, 1990). This
experimental task is empirically tractable and attractive for testing the implications of sequential
search models. Experimental studies on the sequential search model have proliferated, covering a
range of topics such as the e¤ect of unemployment bene…t sanctions on individual search behavior(Boone, Sadrieh, and van Ours, 2009) and the di¤erences in individual search behavior by attitudes
toward loss and risk (Schunk, 2009). In addition, Schunk and Winter (2009) explored the reasons
why in many of these studies agents stop searching earlier than what is theoretically optimal.
We expand upon this body of work by examining the decision-making processes of multiple
agents engaged in a committee search activity. The main feature of our experimental design is that
we conduct three types of game to identify exactly the predictions of the AAV model where agentsare assumed to be homogeneous with respect to preferences. Game A provides the benchmark
as a standard single-agent search task, Game B is a committee search task where three groupmembers have a common value drawn from a distribution, and Game C is a committee search task
where three group members each draw di¤erent values from the same distribution.1 The di¤erence
between Games A and B is attributable to heterogeneity among members with respect to their risk
and loss attitudes, time preferences, and any unobserved characteristics.2 The di¤erence between
Games A and C arises from the heterogeneity among members already mentioned, plus additional
heterogeneity in the sense of the di¤erent values the other members independently draw from thesame distribution. Therefore, the di¤erence between the above two di¤erences is caused only by thesecond form of heterogeneity among members, in that the values drawn by the other members of
the group are di¤erent. This is similar to the AAV model. In addition, we design three subgames
for each of Games B and C: Subgame 1 adopts a plurality voting rule in which the committee
search activity is stopped if at least one member votes to stop searching (the one-vote rule); in
Subgame 2, the committee search activity is stopped only if at least two-thirds of members voteto stop searching (the majority rule); and in Subgame 3, the committee search activity is stopped
only if all members vote to stop searching (the unanimity rule). The results of these subgames
1
2

This implies that each member draws a value from an independent and identical distribution.
The heterogeneity of preferences among members in a group is ruled out in the AAV model.

4

provide evidence concerning the e¤ect of voting rules.
We conducted experimental tests of an in…nite-horizon sequential search model with a 5%
probability that the experiment coercively ends. With this experiment, the focus is on exploring(i) the search duration and (ii) the probability of voting to stop searching in committee searchwith various plurality voting rules compared with single-agent search. Our …nding regarding search
duration is that, compared with single-agent search, the search duration is longer for committee
search with the unanimity rule but shorter for committee search with the one-vote rule, after
controlling for the heterogeneity of preferences among group members regarding risk and loss
attitudes, time preferences and any unobserved factors. However, in our experiments, the di¤erence
in search duration between single-agent search and committee search with the majority rule isstatistically unclear. These outcomes imply that two e¤ects operate to determine this relationship.
The …rst e¤ect is that it takes more time to reach an agreement in committee search with the
majority rule than it does in single-agent search. Thus, on the one hand, the committee search
structure with the majority rule lengthens the search duration. However, on the other hand, thesecond e¤ect is that committee search with the majority rule lowers each subject’s reservation
value because she or he is less picky, thereby shortening the search duration. In our experiment,
under the majority rule, these opposing e¤ects cancel each other out, leading to the conclusionthat there is no di¤erence in search duration between single-agent search and committee searchwith the majority rule. These results imply that search duration is increasing in the number ofvotes required to stop the committee search, which supports the …rst part of Proposition 5 in the
AAV model. In addition, a comparison of the search duration between committee search with theunanimity rule and single-agent search shows that the search duration is increasing in group size,
holding the unanimity rule …xed. The single-agent search structure is regarded as a special case of
the unanimity rule. This supports the implication obtained from the second part of Proposition 4
in the AAV model.
Our second focus is on identifying di¤erences in a subject’s willingness to accept a drawn
value between single-agent search and committee search. To do this, we estimate the average5marginal e¤ects from a probit model to examine the determinants of the probability of voting tostop searching using data from every round-based decision about whether to vote to stop searching.
Our …ndings are that subjects are more likely to vote to stop searching in committee search thanin single-agent search, and that this outcome is strongly observed in committee search with theone-vote and majority rules. These estimated results con…rm the threshold e¤ect referred to in
the AAV model (Proposition 2 therein), in the sense that subjects lower their reservation values
in committee search and thus become less picky about the standard of acceptance.
However, our experimental outcome cannot statistically support the AAV model’s predictionin terms of the size order of the reservation values among the types of committee search with thevarious plurality voting rules. The AAV model predicts that we cannot express the reservation value
as an inverse hump-shaped function of the number of votes required to stop the search (the second
part of Proposition 5 in the AAV model). Unfortunately, given our limited sample size, we cannot
signi…cantly support this prediction. However, we can say that the probability of voting to stop
the search is at …rst gently, and then rapidly, decreasing in the number of votes required to stop thesearch. That is, the reservation value is quantitatively increasing in the number of votes required
to stop the search, which is consistent with the AAV model’s prediction. We …nd that subjects
stop searching earliest in committee search with the one-vote rule, followed by the majority rule,and then the unanimity rule. This result arises from the subjects’ preferences regarding the two
negative externalities (one that the committee search activity stops despite the subject’s preference
to continue searching, and the other that the committee search activity continues despite a subject’s
preference to stop searching). For the most part, the one-vote and unanimity voting rules are,respectively, most strongly in‡uenced by the …rst and second externalities, whereas the majority
rule is in‡uenced by both externalities, but only moderately. Our results imply that subjects who
participated in this experiment incurred a larger disutility from the negative externality whereby
the committee search activity stops despite the subject’s preference to continue searching thanfrom the other negative externality whereby the committee search activity continues despite thesubject’s preference to stop searching.

6In summary, our experimental outcomes are consistent with the implications in the AAV modelin terms of the relationships between committee and single-agent search for search duration andthe probability of voting to stop. However, we do not obtain a signi…cant experimental outcome
in terms of the relationship of the probability of voting to stop committee search activities among
given plurality voting rules. We also obtain other interesting …ndings in our comparison of thee¤ects of Games B and C on the probability of voting to stop searching. The probit estimatesshow that many of the estimated coe¢ cients on Games B are signi…cantly positive relative to thereference group for Game A. This implies that subjects are not homogeneous with respect to theirrisk and loss attitudes, time preferences, and any unobserved factors, and that the heterogeneityof preferences among group members lowers each member’s reservation value. Moreover, because
the estimated coe¢ cients on each Game C are signi…cantly positive and larger in magnitude thanthe corresponding ones on Game B, additional heterogeneity exists in that the values the othermembers draw are di¤erent, which reinforces the incentive to vote to stop searching in an earlier
round.
The paper is organized as follows. Section 2 discusses the implications of the AAV modelin detail. Section 3 explains the strategy for identi…cation, and Section 4 elaborates upon theexperimental design. Section 5 includes descriptive statistics and results of the regression analysis.
Section 6 provides some concluding remarks.

2

Model

Albrecht et al. (2010) construct a committee search model in which group members decide whether
to stop or continue searching by vote. They assume that group members are risk neutral andhomogeneous with respect to preferences, and that each member randomly draws a value from an
independent and identical distribution across members. We should note that values are therefore
uncorrelated across group members. This model setup is a proxy describing the more realistic
environment in which members draw the same value, but do not know how other members evaluate7the value or their attitudes toward loss and risk.3 This setting may di¤er from a realistic situation,
but it is analytically tractable and qualitatively identical. Given the value in hand, each groupmember votes for or against stopping the search.
Albrecht et al. (2010) found that agents are less picky in committee search than in single-agent
search because each member faces two negative externalities: (i) committee search continues undera given voting rule despite an individual’s preference for the search to stop; and conversely (ii)committee search stops under a given voting rule despite the individual’s preference to continue
searching. These negative externalities are attributable to the assumption that each member draws
a value from the independent and identical distribution. Thus, the reservation value is lower incommittee search than in single-agent search, thereby leading to a shorter search duration (a higher
probability of stopping the search). The AAV model refers to this as the threshold e¤ ect. However,there is another e¤ect determining search duration; that is, committee search with plurality voting
rules either raises or lowers the probability of stopping the search, given any reservation value.
This is referred to as the vote aggregation e¤ ect. Whether the probability of stopping the search
for any given reservation value is higher or lower in committee search than in single-agent searchdepends on the given reservation value in the single-agent search structure, the discount factor andthe plurality voting rule applied. 4
Figure 1 illustrates an example of the AAV model and decomposes the probability of stopping
the search in the case of single-agent search versus committee search into the two e¤ects, i.e., thethreshold and the vote aggregation e¤ects. We assume here that a group consists of three members(we design the group as comprising three members in our experiment). In addition, we assume thatan individual member conducts a committee search activity with the other two members and facesa uniform distribution F (x) of drawn value x with a lower bound of x and an upper bound of x: Theprobability of continuing to search P (x; 3; i) is calculated by the sum of the binomial probabilities
3

Compte and Jehiel (2010) consider the case where members hold the same value but do not know how othermembers evaluate this value.
4
The reservation value and the discount factor are closely related in the standard sequential search model. If an
individual discounts the future more, then the individual’s reservation value is lower, implying that he or she wants
to exit the search earlier.

8that exactly i

1 or fewer among the three members vote to stop, given that the reservation value

is x. For example, P (x; 3; 2) indicates the probability that none or only one of the three members
votes to stop, in which case this group continues to search under the majority rule. Be aware that
P (x; 1; 1)

F (x). (1P (x; 3; i)) instead represents the probability of stopping the search. In thisillustration, the reservation value is lower in committee search with the majority rule (xc ) than insingle-agent search (xs ), and the probability of stopping the search is higher in committee searchwith the majority rule (1P (xc ; 3; 2)) than in single-agent search (1P (xs ; 1; 1)). The di¤erencein the probability of stopping the search between committee search with the majority rule andsingle-agent search is thus decomposed into the threshold e¤ect (P (xs ; 3; 2)vote aggregation e¤ect (P (xs ; 1; 1)

P (xc ; 3; 2)) and theP (xs ; 3; 2)).

The vote aggregation e¤ect results in a higher probability of stopping the search in committee
search with the one-vote rule than in single-agent search for any given reservation value (see Figure2). Because the committee search activity stops if any of the three members votes to stop, theprobability of stopping the search is higher. This reinforces the shorter search duration that
occurs for this type of search. On the other hand, in the comparison of single-agent search versus
committee search with the unanimity rule shown in Figure 3, the vote aggregation e¤ect induces
a lower probability of stopping the search in the case of the committee search activity for any
given reservation value because it takes longer for the three members to reach an agreement. Thus,
this type of search leads to a longer search duration. Under the unanimity rule in this speci…cexample, the vote aggregation e¤ect is large enough to dominate the threshold e¤ect, thereby
resulting in the longer search duration. In a comparison of single-agent search versus committee
search with the majority rule, the vote aggregation e¤ect reinforces the threshold e¤ect within therange of lower reservation values (or lower discount factors), implying a shorter search duration.
The vote aggregation e¤ect has the opposite e¤ect to the threshold e¤ect within the range ofhigher reservation values (or higher discount factors). For su¢ ciently high reservation values,
the threshold e¤ect dominates the vote aggregation e¤ect, leading to a shorter search duration.
However, for only moderately high reservation values, the magnitude relation between these two

9e¤ects reverses, resulting in a longer search duration (see Figure 4).

3Strategy for identi…cation

This section considers a methodology to identify the implications of the AAV model. In our
experiment, we restrict ourselves to the case where the group consists of three members and thencompare the experimental outcomes under the various plurality voting rules. We have two main
reasons for choosing three-member groups. The …rst is that this group size is su¢ ciently large toanalyze the search behavior of individual agents in a committee search activity under the various
plurality voting rules described below. The second reason is that this small group size allows us toobtain data from a large number of groups in our laboratory with limited capacity.

3.1

Comparison of the single-agent search model and the committee search
modelThis subsection describes a way of testing and comparing the single-agent search model with acommittee search model with a variety of plurality voting rules. Recall that in the AAV model,
agents are homogeneous with respect to their risk and loss attitudes, time preferences, and anyother unobserved characteristics, and that the di¤erence between single-agent and committee searchbehavior therefore arises only from heterogeneity in the sense that the values drawn by groupmembers are di¤erent and unknown to each other.5 In our experiment, however, there exists yet
another source of heterogeneity in the sense that grouped subjects di¤er in their risk and loss
attitudes, time preferences, and any unobserved characteristics. To eliminate the bias arising fromthe latter form of heterogeneity, we conduct the following three games.
Game A: A single agent independently decides to stop or continue searching.
5

The properties of the AAV model remain the same, regardless of whether the di¤erent values drawn by the othermembers are known or unknown. In our experiments, subjects drew the value privately, and therefore no member
knew the values that the other members drew.

10Game B: A group draws a common value from a distribution (group members know thatthey hold the same value), and then the group members collectively decide by vote to stopor continue searching.
Game C: Each member of a group draws a value from the independent and identical distribution, and then the group members collectively decide by vote to stop or continue searching.
Using these games, we can observe the search durations of each subject. The di¤erence in the
search duration between Games A and B is attributable to the heterogeneity among group members regarding their risk and loss attitudes, time preferences, and any unobserved characteristics.
The di¤erence in the search duration between Games A and C is attributable to two types of heterogeneity: heterogeneity in terms of what other members’preferences are, as detailed above, andin the di¤erent values that group members draw from the independent and identical distribution.
Therefore, the di¤erence-in-di¤erences of the search duration stems only from the heterogeneity
in terms of the di¤erent values that the group members draw, implying that the heterogeneity interms of the di¤erences among members regarding their risk and loss attitudes, time preferences,
and any unobserved characteristics is eliminated. This method therefore picks up the exact di¤erence in the search duration between single-agent search versus committee search as characterized
by the AAV model.

3.2

Comparison of the di¤erent plurality voting rules

The AAV model shows that the probability of stopping the committee search activity varies according to the plurality voting rule applied. To identify the e¤ects of the di¤erent plurality voting
rules, we conduct the following three subgames.
Subgame 1: The committee search activity is stopped if at least one of the group members
votes to stop (the one-vote rule).
Subgame 2: The committee search activity is stopped only if two-thirds or more of thegroup members vote to stop (the majority rule).
11Subgame 3: The committee search activity is stopped only if all the group members voteto stop (the unanimity rule).
In Subgame 1, members are faced with the risk that committee search will stop despite anindividual member’s preference to continue searching, whereas in Subgame 3, committee searchcontinues even if an individual member wants to stop searching. Subgame 2 is the in-between caseof Subgames 1 and 3. Using these subgames, we can identify the e¤ects of the risks presented by
the di¤erent plurality voting rules.

4

Experimental design

We conducted our experiment on February 2, 2010, in the experimental laboratory of the Center for
Experimental Research in Social Sciences at Hokkaido University, Japan. The experiment consistedof four separate sessions because of the constraints in laboratory capacity. Each session involved
the same eight games in a di¤erent order (two games of Game A and three subgames each of Game
B and Game C). We designed various experiments of an in…nite-horizon sequential search modelwith a 5% probability that the experiment coercively ends.6 When the committee search activity is
coercively terminated, the subject unconditionally obtains the value drawn in the previous round.
Recall was not allowed in this search model, meaning that the value drawn in the previous roundis not available for consideration in the current round, except for the case of coercive termination.
Although subjects are not encouraged to search longer in a search environment where recall is notallowed, this design is simple and exactly duplicates the structure of the AAV model.
The games di¤er in terms of the treatments in each experimental session. The experimental
processes with the di¤erent treatments for single-agent and committee search are set out below.
6

We also ran …nite-horizon versions of the same experiments at Osaka University, Japan. Speci…cally, participants

were told that the experiments would end after 20 rounds. The results from the …nite-horizon experiments are
qualitatively similar to those from the in…nite-horizon experiments.

12

4.1

Game A: Single-agent search

Game A is the benchmark for a normal single-agent search task. A subject makes a draw from a
uniform distribution with a lower bound of zero and an upper bound of 3,000. After making thedraw, the subject decides whether to stop or continue searching. If the subject chooses to continue,
he or she moves to the next round and makes another draw from the same distribution.
In an in…nite-horizon sequential search model, the value of searching for a single agent VA is
given by:

VA =(1|)F (RA )V A +{z}continuing search

where

|

Z 3000xdF (x) ;xdF (x) +
RA
{z
} |
{z
}

Z RA
0

terminating search(1)

accepting the o¤er

represents the exogenous probability that the experiment coercively ends, RA is the reser-

vation value, and F (x) is the uniform distribution. For simplicity, there is no explicit discountover rounds, which encourages subjects to search longer. Our experiments are designed in such a
way that a subject’s search activity has to be terminated coercively with a probability of 5% foreach round. This probability partially ful…lls the role of a search cost that the subject incurs bycontinuing to search in the next round. The subject then has an incentive to stop searching even in
the in…nite-horizon sequential search model with no discount over rounds. The sum of the …rst two
terms on the right-hand side in eq. (1) represents the value of rejecting a drawn value. The …rstof these terms is the value of continuing the search after the subject survives to the next round,and the second of these terms indicates the value of the search activity being terminated after theo¤er is rejected. When a search activity is coercively terminated, the subject has no choice but
to accept the value that she or he rejected in the previous round.7 The …nal term represents thevalue of accepting a drawn value. Because the reservation value of gaining RA is equivalent to thevalue of searching in the next round (RA = RA + (17

)VA ), we obtain RA = VA :The experiment is designed to reduce the loss that each subject would have incurred if the search was terminatedafter she or he had decided to continue to search. This encourages the subject to search longer. In other words, in adesign where each subject receives no payment when the search activity is terminated coercively, it is expected that
she or he will not engage in the search activity for many rounds.

13

4.2

Game B: Committee search with a common value

Game B involves a form of committee search in which all members of a group draw a common
value from the uniform distribution with a lower bound of zero and an upper bound of 3,000, andthey know that they have the same value. Whether the committee search stops or continues isthen decided by vote among group members under various plurality voting rules. Because groupmembers are randomly reshu- ed in every game, no member knows who the other two groupmembers are, which rules out the presence of a learning e¤ect regarding other group members’
voting behavior.
If group members are homogeneous with respect to preferences, as in the AAV model, thecommittee search model with a common value is reduced to the single-agent search model shownin Game A. Therefore, the value for a group member of searching by committee VB is:

VB =(1|)F (RB )V B +{z}continuing search

Because RB = RB +(1

|

Z RB
0

Z 3000xdF (x) +xdF (x):{z} | RB {z}terminating search(2)

accepting)VB , we obtain RB = VB : According to eq. (2), if all group members arehomogeneous with respect to preferences, they all either accept or reject a drawn value together,regardless of which voting rule is employed.
If there is a di¤erence in the reservation values between Games A and B in our experiment, it islargely attributable to the heterogeneity of preferences in search activity among members. In our
experiment, Game B consists of three subgames, each of which di¤ers according to the plurality
voting rules explained in Section 3.2.

4.3

Game C: Committee search with di¤erent values

In Game C, similarly to Game B, group members decide by vote whether to stop or continue
searching. However, unlike Game B, each group member separately makes a draw from a uniform
distribution with a lower bound of zero and an upper bound of 3,000, which means that the drawn
values are identically independent across the group members. Game C, like Game B, consists of14

three subgames, C-1, C-2, and C-3, as explained in Section 3.2.
Suppose that committee search is stopped if at least one group member votes to stop searching(Subgame C-1: one-vote rule). The value for a subject of searching by committee VC1 is given by:

VC1 =

(1
|

)F (RC1 )3 VC1
{z
}

|

0

continuing committee search after the subject votes to continue+

2

F (RC1 )Z RC1{zxdF (x)}terminating committee search after the subject votes to continue

Z 3000xdF (x)+
+
RC1
|
{z
}
accepted by self

[1
|

2

Z RC1

F (RC1 ) ]{z 0

xdF (x)}:(3)

accepted by one or both of the others, but not by self

The sum of the …rst two terms on the right-hand side in eq. (3) represents the value of committee
search when all group members vote against stopping the search. The …rst of these two terms
denotes that the committee group survives to the next round with a probability of (1), butthe second term shows that the group has to stop searching, and thus the group obtains the value
drawn in the previous round. The sum of the third and fourth terms in eq. (3) indicates the value
for the subject of accepting the drawn value. The third term represents that at least one member,including the subject, votes to stop searching, and the fourth term shows that one or both of theother two members vote for stopping the search, although the subject votes against it. Because
RC1 = RC1 + (1

)VC1 according to the reservation value rule, we obtain RC1 = VC1 :Next, we consider a committee search model in which committee search is stopped only if at
least two out of three members vote to stop searching (Subgame C-2: majority rule). The value
for a subject of searching by committee VC2 is obtained as follows:

15

VC2 = (1|)[F (RC2 )3 +3(1F (RC2 ))F (RC2 )2 ]V C2{z}continuing committee search
2+ [F (RC2 ) +2(1|F (RC2 ))F (RC2 )]
{z

Z RC2
0

xdF (x)}terminating committee search after the subject votes to continue+

2

F (RC2 )|Z 3000RC2{z

xdF (x)}terminating committee search after the subject votes to stop
Z 3000:+[1 F (RC2 )2 ]xdF (x)
RC2

|

{z

+[1
|

2

Z RC2

F (RC2 )]{z 0

xdF (x)}accepted by others, but not by self

(4)}

accepted by self and one or both other members

The …rst term on the right-hand side in eq. (4) shows the value of continuing the committee search
activity after the committee survives to the next round. [F (RC2 )3 +3(1 F (RC2 ))F (RC2 )2 ] implies
the probability that at least two members vote against stopping the search. The second and thirdterms represent the value of the committee search activity being coercively terminated. The second
term shows the case in which at least two members, including the subject, vote against stopping
the search before termination of the committee search activity, whereas the third term deals withthe case in which the subject votes for stopping the search, but the other two members vote against
it before termination. The fourth and …fth terms indicate the value for the subject of stopping
the committee search activity; the fourth term shows the case in which the subject votes against
stopping the search, but the other two members vote for it, whereas the …fth term indicates thatthe subject and one or both of the other two members vote to stop searching. As discussed above,
we have RC2 = VC2 :Finally, we move to the committee search model in which committee search is stopped only if
all members of the group vote to stop (Subgame C-3: unanimity rule). The value for a subject ofsearching by committee VC3 is given by:

16

VC3 = (1
|

)[1

(1

F (RC3 ))3 ]V C3{z}continuing committee search
2+ [F (RC3 ) +2(1|F (RC3 ))F (RC3 ) + (1{z2

F (RC3 )) ]

Z RC3
0

xdF (x)}terminating committee search after the subject votes to continue
Z 3000Z 30002
2+ [F (RC3 ) +2(1 F (RC3 ))F (RC3 )]xdF (x)+[1 F (RC3 )]xdF (x): (5)
RC3
RC3

|

{z

terminating committee search after the subject votes to stop

} |

{z

accepted by all members}

The …rst term on the right-hand side in eq. (5) represents the value for the subject of continuing
the committee search after her or his committee survives to the next round, whereas the last term
denotes the value for the subject of accepting a drawn value when all of the members vote to stop
the search unanimously. The second and third terms represent the value of the committee search
activity being coercively terminated. The second term indicates the value of the committee search
activity being terminated after at least the subject votes against stopping the search. In the thirdterm, committee search is terminated after at least the subject votes for stopping the search, but
one or both of the other members vote against it.

4.4

Hypotheses

This subsection sets out our experimental hypotheses developed to test the theoretical implicationsof the AAV model. Because Albrecht et al. (2010) found many novel and interesting implications
from their committee search model, we cannot test all possible hypotheses arising from it. Accordingly, we focus the laboratory experiment on testing two sets of hypotheses relating to: (i) thecomparison of search duration between single-agent and committee search under various plurality
voting rules; and (ii) the comparison of the willingness to stop searching between single-agent andcommittee search under various plurality voting rules. For the …rst set of hypotheses (regarding
search duration), we test the theoretical implications of the …rst part of Proposition 5 and thesecond part of Proposition 4 in Albrecht et al. (2010). For the second set of hypotheses (regarding17

the willingness to stop searching), we test the implications of Proposition 2 and the second part of
Proposition 5.
We begin with the …rst set of hypotheses.
H1 (the …rst part of Proposition 5): The average search duration is increasing in thenumber of votes required to stop committee search. In addition, the average search durationis shorter (longer) in the case of committee search with the one-vote rule (unanimity rule)than in the case of single-agent search.
If this hypothesis is statistically supported, as indicated in the AAV model, then, compared withsingle-agent search, the probability of stopping the search is higher in committee search with theone-vote rule but lower in committee search with the unanimity rule. The probability of stopping
the search in committee search with the majority rule lies between the other plurality voting rules.
H2 (the second part of Proposition 4): The average search duration is increasing in thenumber of group members, given that the number of votes required to stop searching equals
the number of group members.
To test this hypothesis, we compare the average search duration between committee searchwith the unanimity rule where the search is stopped if all three members of the group vote to stopand single-agent search where the individual search stops if “one out of the one member of thegroup”decides to stop the search. Our expectation is that the search duration is longer in the caseof committee search with the unanimity rule than in the case of single-agent search.
(H1) and (H2) address the combined e¤ects (the threshold and vote aggregation e¤ects) on the
average duration of committee search. The next step is to employ the probit model to estimate
determinants of individual voting behavior regarding stopping searching, using data from eachround-based decision from the eight games. This identi…es the threshold e¤ect whereby subjects
are less picky in committee search than in single-agent search, as described in Proposition 2 inAlbrecht et al. (2010). We then move to the second set of hypotheses as follows.

18

H3 (Proposition 2): A subject votes for stopping the search in an earlier round of committee search, regardless of the plurality voting rules, compared with single-agent search.
This test allows us to capture the threshold e¤ect and compares the reservation values between
single-agent and committee search using the various plurality voting rules. The …nal hypothesis
we test in our experiment is the implication from the second part of Proposition 5.
H4 (the second part of Proposition 5): The reservation value is not permitted to be aninverse hump-shaped function of the number of votes required to stop the committee search
activity. In other words, in committee search, the reservation value cannot be lower under the
majority rule than under the one-vote rule. At the same time, the reservation value cannot
be lower under the majority rule than under the unanimity rule.
We thus test the null hypothesis that RC1

RC2 and RC2

RC3 , where Ri represents thereservation value for game i. In other words, the probability of voting to stop the committee search
activity cannot be expressed as a hump-shaped function of the number of votes required to stop
the search.
In addition, we test whether group members are on average homogeneous with respect to theirrisk and loss attitudes, time preferences, and any unobserved characteristics. If the estimatedcoe¢ cients on the dummy variables from Game B are not jointly di¤erent from zero in the probit
estimation of individual voting behavior, we can support the hypothesis that group members arehomogeneous with respect to their preferences. This draws our attention to eliminating the bias
arising from the heterogeneity of preferences across group members when the four hypotheses aretested. Section 5 provides the estimated results.

4.5

Administration and payo¤s

We conducted four sessions. The order of games in each session was as follows: …rst session: Games
A, B-3, B-2, B-1, C-3, C-2, C-1, and A; second session: Games A, C-1, C-2, C-3, B-3, B-2, B-1,
and A; third session: Games A, C-3, C-2, C-1, B-1, B-2, B-3, and A; and fourth session: Games A,
19

B-1, B-2, B-3, C-1, C-2, C-3, and A. We instructed the subjects to play a training game that was
the same as Game A once before the experiment began. Although we might not be able to rule out
the possibility that this training a¤ected the subjects’behavior in the …rst Game A, this step wasnecessary to ensure that the subjects correctly understood the nature of the experiment. We ran
Game A twice as the …rst and last games in each session. This enables us to determine whether
an anchoring e¤ect arises in the sense that there is any di¤erence in the search behavior between
the …rst and last Game A. The anchoring e¤ect implies that the subjects’behavior is a¤ected byresults that they obtained in previous games. The subjects were 60 undergraduate students fromvarious academic disciplines. We ran the experiments entirely on computers using the software
package Z-Tree (Fischbacher, 2007). 8
The instruction sheet presented full information about the search task.9 Following the experiment, the participants answered a questionnaire and the payo¤ procedures took place. With
regard to payo¤s, we emphasized that: (i) the subjects’payo¤ was truncated at JPY0 (EUR0) (i.e.,they could not incur losses from the search task) and (ii) they would earn an appearance fee of
JPY1,000 (EUR7.9).10 The performance pay was determined based on the result from one of theeight games randomly chosen by each subject. The expected total payo¤ was JPY2,500 (EUR19.75)
to 3,000 (EUR23.7). Therefore, because the on-duty time for the experiment was approximately
90 minutes, the hourly wage was calculated at JPY1,600 (EUR12.64) to 2,000 (EUR15.8). This isapproximately twice as much as the average hourly wage for college students in Japan, implyingthat we set the appropriate way of payo¤ to encourage subjects to work hard.
8

The programs were produced by Takanori Kudou, a graduate student of the Engineering Division of Electrical,

Electronic and Information Engineering, Osaka University.
9
The instruction sheet is reproduced in the appendix.
10
We use the exchange rate of JPY100 to EUR0.79 for February 2, 2010, the date when the experiment was
conducted.

20

5Results

5.1

Search duration

We begin with some descriptive statistics before undertaking statistical hypothesis tests of the …rst
set of hypotheses (H1) and (H2). Table 1 provides selected descriptive statistics, including the
averages and standard deviations of the durations for the in…nite-horizon sequential search model.
We …nd that the average search duration for single-agent search in the …rst Game A di¤ers fromthat in the last Game A at the 5% level of signi…cance, implying that there may be an anchoringe¤ect in the sense that a subject’s search behavior is in‡uenced by results she or he obtained in
the previous experimental games. This may result in an identi…cation bias in the statistical tests.
The average search duration is longer for committee search under the majority and unanimity
rules when group members have di¤erent values (Game C) compared with when they have thesame value (Game B), but the reverse is observed when the one-vote rule applies. As the required
number of votes to stop searching increases, the average search duration becomes longer, regardlessof whether group members draw the same or di¤erent values.
Table 2 provides the results of t-tests of the di¤erence in search duration between committee
search under the di¤erent plurality voting rules versus single-agent search. The top three rowsof this table compare the average duration for single-agent search and committee search in which
all group members draw the same value (Game B). We …nd that the null hypothesis that the
average duration of committee search with the one-vote rule is equal to or larger than that ofsingle-agent search is rejected at the 10% level of signi…cance. Conversely, compared with singleagent search, the average duration is shorter in committee search with the majority rule and longer
in committee search with the unanimity rule, but in both these cases, the di¤erence in the average
duration between the two models is not signi…cantly di¤erent from zero.
According to the AAV model, group members are homogeneous with respect to their risk andloss attitudes, time preferences, and any unobserved characteristics, and therefore share the same
threshold. Therefore, when the group members make the same draw and evaluate it in common,
neither the threshold nor the vote aggregation e¤ect arises, resulting in no di¤erence in the average
21

duration between the single-agent and committee search models, regardless of which plurality
voting rule applies.
Although the di¤erences in the average search duration between single-agent and committee
search with the majority and unanimity rules in Game B are statistically insigni…cant, this does
not necessarily mean that group members are homogeneous with respect to their preferences. Ifgroup members are heterogeneous in terms of their preferences, they evaluate the same drawn valuedi¤erently, in a case of which both threshold and vote aggregation e¤ects arise. On the one hand,it takes more time to reach an agreement in committee search with the majority and unanimity
rules than in single-agent search, which lengthens the search duration. On the other hand, eachsubject’s reservation value decreases because she or he is less picky, thereby shortening the search
duration. It is possible that the two opposing e¤ects cancel each other out, which suggests thatgroup members might have been homogeneous with respect to their preferences. To correctly test
the implications of the AAV model where members are homogeneous, it is necessary to test thedi¤erence in the threshold level among group members, which is analyzed in Section 5.2.
The bottom three rows in Table 2 compare the average duration between single-agent andcommittee search in which group members draw di¤erent values (Game C). Recall that the di¤erence in the average duration between the two models arises from the heterogeneity of preferencesamong group members, as discussed above, and from the other heterogeneity in terms of the different draws that other members make. The search duration is shorter in committee search withthe one-vote rule than in single-agent search at the 1% level of signi…cance. Similarly, the search
duration is longer in committee search with the unanimity rule than in single-agent search at the
1% level of signi…cance. The average search duration is insigni…cantly shorter in committee searchwith the majority rule than in single-agent search. We have not yet identi…ed whether the difference in average duration arises from the heterogeneity among group members with respect totheir preferences or from the other heterogeneity in the sense that the draws other members makeare di¤erent. We can correctly con…rm the implications of the AAV model by correspondingly
deducting the di¤erences displayed in the top three rows in Table 2 from the di¤erences displayed22

in the bottom three rows. We test the null hypothesis that the di¤ erence-in-di¤ erences of theaverage search duration, (Game C-1 – Game A) – (Game B-1 – Game A) = Game C-1 – Game
B-1, is zero. The same procedure applies to the other two voting rules.
Table 3 gives the results of t-tests of the di¤erence in the average duration between single-agent
and committee search after controlling for heterogeneity among group members. As shown in the…rst row of the table, the average search duration is shorter in committee search with the one-vote
rule than in single-agent search at the 1% level of signi…cance. Similarly, the null hypothesis thatthe average search duration in committee search with the unanimity rule is equal to or shorter
than the average duration in single-agent search is rejected at the 1% level of signi…cance. Anotherinterpretation of this result is that as the number of group members increases from one to three,holding the unanimity rule …xed, the average search duration becomes longer. The single-agent
search model is then considered a special case of the committee search model with the unanimity
rule. This result is consistent with the second part of Proposition 4 in the AAV model. Looking
at the di¤erences between single-agent and committee search with the majority rule in Table 3,
we cannot signi…cantly reject the null hypothesis that the two average durations are equal. The
AAV model predicts that if the reservation value of single-agent search (or the discount factor)
is extremely low or extremely high, the average search duration is shorter in committee searchwith the majority rule, but otherwise it is longer, as illustrated in Figure 4. Therefore, it is not
surprising that these outcomes are obtained from our experiment. An examination of the di¤erencesin committee search with the various plurality voting rules shows that the average search duration
is increasing in the number of votes required to stop the search. This outcome supports the …rst
part of Proposition 5 in the AAV model.
Table 4 provides the ordinary least squares estimates of the determinants of search durationin the in…nite-horizon sequential search version. The dependent variable is each subject’s search
duration in each game, while the vector of independent variables consists of dummy variables fortreatment, game order, each subject’s attitude toward risk, a dummy variable for female gender,
and/or dummy variables for individual e¤ects. The variable regarding the game order indicates23the order in which the subject played the games in each session. The variable for risk attitude isincluded to partially control for individual heterogeneity. To collect this variable, we administereda questionnaire to all participants after eight games and asked three questions about their attitude
toward risk. Of these, we selected one question relating to the price subjects were willing to pay
for a lottery with a 25% chance of winning JPY2,000 (EUR15.8) and a 75% chance of receiving
JPY0 (EUR0).1112 We then calculated the index measuring the extent of absolute risk aversion
using the method in Cramer et al. (2002).13 If this index is positive, a subject is considered risk
averse; if negative, the subject is considered risk seeking. If the index is exactly zero, the subject
is risk neutral.
We control for individual e¤ects in column [1] of Table 4, and replace the individual e¤ects withother individual characteristics represented by absolute risk aversion and gender in columns [2] and[3], respectively. The columns in Table 4 indicate almost the same results: the coe¢ cient on GameC-1 is negative, whereas that on Game C-3 is positive, both at the 1% level of signi…cance when
the reference group is de…ned as the …rst Game A.14 The coe¢ cient on Game B-1 is negative at amarginal level of signi…cance (10%) in columns [2] and [3]. From the estimates of search duration,
we …nd that in committee search with the one-vote rule, group members are heterogeneous with11

We again use the exchange rate of JPY100 to EUR0.79 on February 2, 2010, corresponding to when the experi-

ment was conducted.
12
The other two questions were: “With at least what chance of rain do you take an umbrella?” and “What price
are you willing to pay for a lottery with a 25% chance of winning JPY200 (EUR1.6) but a 75% chance of receiving
JPY0 (EUR0)?” In the …rst question, subjects that responded with a lower value were considered to be more risk
averse. We also estimated the determinants of search duration using indices of absolute risk aversion obtained fromthese questions and obtained similar results.
13
According to Cramer et al. (2002), the extent of absolute risk aversion is calculated as follows:

0:5(0:25

0:25
20002

2000 price;
2 0:25 2000 + price2 )where price implies the price that a subject is willing to pay for the lottery with a 25% chance of winning JPY2,000(EUR15.8) but a 75% chance of receiving JPY0 (EUR0).
14
Note that the coe¢ cients on games are the same in all columns. This is because, in the estimates of search
duration where there are eight observations for search duration for each subject, individual characteristics are perfectly
uncorrelated with the treatment variables (games) that are given exogenously.

24respect to their risk and loss attitudes, time preferences, and any other unobserved characteristics.
However, we should note that, as mentioned before, both threshold and vote aggregation e¤ects
arise if group members evaluate the common drawn value di¤erently. It is then possible thatthe threshold and vote aggregation e¤ects are opposite and cancel each other out in Games B-2
and B-3. In this case, the estimated coe¢ cients on these games become insigni…cant despite theheterogeneity of preferences among group members. We cannot exactly identify whether or notgroup members are homogeneous in terms of search behavior based on the estimates of search
duration in Table 4.
Assuming that group members are heterogeneous in terms of their preferences, we test the …rst
set of hypotheses regarding search duration, (H1) and (H2), by deducting the coe¢ cients on GameB from the corresponding values for Game C, using the estimated results from column [1] of Table
4. We obtain the same results as Table 3, as shown in Table 5. The null hypothesis that the
search duration in committee search with the one-vote rule is equal to or longer than the search
duration in single-agent search is rejected at the 1% level of signi…cance. Similarly, we reject thenull hypothesis that the average search duration in committee search with the unanimity rule isequal to or shorter than the average duration in single-agent search at the 1% level of signi…cance.
This implies that the search duration is increasing in group size, holding the unanimity rule …xed.
This result again supports the second part of Proposition 4 in the AAV model (H2). We cannot
reject the null hypothesis of no signi…cant di¤erence in the search duration between committee
search with the majority rule and single-agent search. As discussed, this result is not surprisingbecause the AAV model shows that the search duration is either shorter or longer in committee
search with the majority rule, depending mainly on the reservation value of the single-agent searchmodel and the agent’s discount factor. We con…rm that the search duration is increasing in thenumber of votes required to stop committee search. This result is again consistent with the …rst
part of Proposition 5 in the AAV model (H1).
Consider now the estimated coe¢ cients on the other independent variables. The variable regarding the game order is statistically insigni…cant in all columns of Table 4. In the estimates of25

the search duration, we can say that the anchoring e¤ect whereby a subject’s behavior is a¤ectedby how she or he behaved in the previous games is minor. We estimate the e¤ects on the search
duration of individual characteristics represented by absolute risk aversion and gender. In columns[2] and [3] of Table 4, we …nd that the coe¢ cients on these variables are insigni…cant.

5.2

The probability of voting to stop

This subsection considers each round-based decision by subjects on whether to vote for stopping
the search and then tests the second set of hypotheses regarding the probability of voting to stop
the search, (H3) and (H4). Table 6 displays the average marginal e¤ects of the probit model toestimate determinants of the vote to stop searching, using the round-based data on search decisions.
The dependent variable is dichotomous, taking a value of one if a subject accepts a drawn value
in the case of single-agent search or votes to stop searching in the case of committee search, andzero otherwise. The independent variables are dummy variables for treatment, the drawn value,
the round, game order, attitude toward risk, a dummy variable for female gender, and/or dummy
variables for individual e¤ects. The purpose of these estimations is to capture di¤erences in theprobability of voting to stop the search, or in the reservation value between committee search andsingle-agent search, and then to extract the threshold e¤ect quantitatively.
We control for individual e¤ects in columns [1] and [2] of Table 6. Column [1] excludes theindependent variable for round whereas column [2] includes this variable. We replace the individuale¤ects with other individual characteristics represented by absolute risk aversion and gender incolumns [3] and [4], respectively. In all columns, the coe¢ cients on Game B-2, Game B-3, andall Games C are positive at the 1% level of signi…cance, compared with the reference group of
the …rst Game A, whereas the coe¢ cients on Game B-1 are signi…cantly positive at the 5–10%
level. Because the coe¢ cients on Games B are signi…cantly positive and di¤erent from zero, we
can say that heterogeneity exists in terms of preferences among group members, which encourages
subjects to vote in favor of stopping the search in an earlier round. Because the coe¢ cientson committee search are larger when the group members draw di¤erent values (Games C) than26

when the group members draw the same value (Games B), regardless of the plurality voting rule,
the remaining heterogeneity regarding the di¤erent values that other members draw from the
distribution reinforces the incentive to vote to stop searching in an earlier round. These results
con…rm the threshold e¤ect, which suggests that the reservation value is lower in committee searchthan in single-agent search in the AAV model.
Table 7 provides the results of the tests of hypothesis (H3) that subjects are less picky incommittee search, regardless of which plurality voting rule is employed, than in single-agent search.
Each coe¢ cient on Games C represents the average marginal probability of voting to stop in GamesC, compared with that in the …rst Game A, using the estimated results in column [2] of Table 6.
We should note that, as before, we test (H3) by deducting the coe¢ cients on Games B from thosecorresponding to Games C, which allows us to control for the heterogeneity of preferences acrossgroup members. As shown in Table 7, we signi…cantly reject the null hypotheses that the averagemarginal probability of voting to stop in Game C is equal to or lower than that in Game B under
the one-vote and majority rules. This implies that subjects vote to stop the search in an earlier
round under the one-vote and majority rules. We thus support Proposition 2 in the AAV model(H3), stating that subjects are less picky in terms of their acceptance standard in committee searchthan in single-agent search. However, contrary to our expectations, we cannot reject (H3) in thecase of the unanimity rule.
Next, we compare the coe¢ cients on Games C in terms of the magnitude to test the second
part of Proposition 5 (H4). The probability of voting to stop searching varies according to theplurality voting rules. Albrecht et al. (2010) showed that the reservation value cannot be inverse
hump-shaped in the number of votes required to stop searching; that is, the probability of voting tostop the committee search cannot be expressed as a hump-shaped function of the number of votes
required to stop the search. Table 8 displays the results from testing (H4) using the estimatedresults from column [2] of Table 6. The …rst row in Table 8 tests the null hypothesis that the
average marginal probability of voting to stop the search in Game C-1 is equal to or lower thanthat in Game C-2, while in the second row, we test the null hypothesis that the average marginal27

probability of voting to stop the search in Game C-3 is equal to or lower than that in Game C-2.
As before, we control for the heterogeneity of preferences among group members by deducting thecoe¢ cients for Games B from the corresponding values for Games C. As shown in Table 8, wecannot signi…cantly reject the two null hypotheses, contrary to our expectations. Nevertheless,we note that the statistical hypothesis testing does not necessarily imply that we accept that theprobability of voting for stop is hump-shaped in the number of votes required to stop the committee
search.
In addition, Table 8 indicates that the probability of voting to stop is at …rst gently and thenrapidly decreasing in the number of votes required to stop the search. This provides weak evidence
to quantitatively support the prediction of the AAV model that a subject votes in favor of stopping
the search in an earlier round under the one-vote rule (Game C-1), followed by the majority rule(Game C-2) and the unanimity rule (Game C-3). In other words, the reservation value is lowest in
Game C-1, followed by Game C-2, and then Game C-3. One of the reasons for this result may bethat the subject’s preferences in relation to the two negative externalities (i.e., the …rst externality
relating to the committee search stopping despite the subject’s preference to continue, and thesecond externality where the committee search continues despite the subject’s preference to stop)
are di¤erent. Recall that the one-vote and unanimity rules are strongly in‡uenced by the …rst andsecond externalities, respectively, whereas the majority rule is in‡uenced by both externalities,
but only moderately. If the subject incurs a larger disutility from the …rst externality than thesecond, she or he tends to vote to stop in an earlier round under the one-vote rule, followed by
the majority rule, and then the unanimity rule, because she or he does not want other members
to stop the committee search, despite her or his preference to continue searching. On the other
hand, if the subject incurs a larger disutility from the second externality than from the …rst, sheor he tends to vote to stop in an earlier round under the unanimity rule, followed by the majority
rule, and then the one-vote rule, because she or he does not want other members to continue thecommittee search despite her or his preference to stop searching. Our results then provide weakevidence that subjects incur a larger disutility from the negative externality involving the stopping28

of the committee search despite the subject’s preference to continue than from the second negative
externality relating to the committee search continuing despite the subject’s preference to stop.
Other interesting variables also a¤ect the probability of voting to stop searching. The coe¢ cienton drawn value is positive at the 1% level of signi…cance in all columns of Table 6. When a subject
draws a higher value, she or he is more likely to vote to accept it. According to column [2] ofTable 6, the coe¢ cient on round remains positive at the 1% level of signi…cance. This refutes
our expectation that the round is not a determinant of voting to stop searching in an in…nitehorizon sequential search. The variable regarding the game order is negative at the 1–5% levels ofsigni…cance in all columns. This implies that subjects vote more aggressively in favor of continuing
to search in later games.
A …nal question is whether there are any systematic di¤erences in the probability of voting tostop searching among subjects. To explore this, we employ the individual characteristics, including
the extent of absolute risk aversion and gender. The variable indicating the extent of absoluterisk aversion is as expected. Its coe¢ cient on absolute risk aversion is positive at the 1% level ofsigni…cance in column [3] of Table 6, implying that more risk-averse subjects are more likely tovote to stop searching in an earlier round.

6

Concluding remarks

This paper described a laboratory experiment to study an in…nite-horizon sequential search-bycommittee model and tested some of the implications obtained in Albrecht et al. (2010). To
date, there have been no empirical studies on committee search, mainly because of the di¢ cultyin collecting suitable data. Using our laboratory experiment, we collected original data fromsubjects. This paper’s main contributions are to provide experimental evidence about committee
search and then to test the properties of search duration and voting behavior for various plurality
voting rules. Our experimental design involved decomposing the source of the di¤erence in searchbehavior between single-agent search and committee search into e¤ects caused by heterogeneitywith respect to preferences among group members and other heterogeneity in terms of the di¤erent
29values other members draw from the identically independent distribution.
Our …ndings are summarized as follows. After controlling for the heterogeneity of preferencesamong group members, the average search duration is longer in committee search with the unanimity rule than in single-agent search, whereas the average search duration is shorter in committee
search in which at least one vote is required to stop the search than in single-agent search. In a
comparison of single-agent search versus committee search with the majority rule, the hypothesisof no di¤erence in search duration is not signi…cantly rejected. This result is not surprising given
the properties of the AAV model stating that the duration of committee search is either longer
or shorter than that of single-agent search, depending on the value of the discount factor. These
results imply that search duration is increasing in the number of votes required to stop committee
search. This supports the …rst part of Proposition 5 in the AAV model (H1). In addition, we
found that the search duration is increasing in group size, holding the unanimity rule …xed, when
we compare the search duration in single-agent search with that in committee search with theunanimity rule. This result is consistent with the second part of Proposition 4 in the AAV model(H2).
To identify the threshold e¤ect whereby negative externalities caused by committee search
involving voting operate to lower a member’s reservation value, we estimated the determinantsof voting to stop searching. We found that subjects are more likely to vote to stop searching incommittee search than in single-agent search. These estimated results con…rm the threshold e¤ect,in the sense that agents are less picky in committee search than in single-agent search, as the AAV
model indicates in Proposition 2.
The AAV model predicts that the reservation value cannot be inverse hump-shaped in thenumber of votes required to stop searching. That is, the probability of voting to stop searching
cannot be hump-shaped in the number of votes required to stop searching. Unfortunately, ourexperimental outcome cannot signi…cantly support this prediction in terms of the size order of thereservation values. We also cannot rule out the possibility that the reservation value is expressedas an inverse hump-shaped function of the number of votes required to stop searching. Comparing30

the size of the coe¢ cients on games, however, we found that the probability of voting to stop
searching is at …rst gently and then rapidly decreasing in the number of votes required to stop
searching, meaning that the reservation value is lowest under the one-vote rule, followed by the
majority rule, and then the unanimity rule. This is weakly and quantitatively consistent with thepredictions of the AAV model. This result implies that subjects who participated in the experiment
incurred larger disutility from the negative externality involving committee search stopping despitea preference to continue, than from the second negative externality relating to committee searchcontinuing despite a preference to stop.
Overall, our experimental outcomes are consistent with the implications in the AAV model interms of comparisons of the search duration and the probability of voting to stop searching forcommittee versus single-agent search. However, the outcomes cannot statistically support the AAV
model’s prediction according to the di¤erent plurality voting rules in terms of comparison of theprobability of voting to stop searching in the committee search model.
Of greatest interest to us now in this research topic is whether we would obtain the same
experimental results with larger groups. As in the AAV model, we con…rmed from the test of (H2)in our experiment that the search duration was longer as the group size increased from one to three
under the unanimity rule. Our expectation is that the search duration will become even longer as
the group size increases to, say, …ve under the unanimity rule because the vote aggregation e¤ect
operates more strongly. We therefore need to check for consistency in our results for larger groups.
This provides one of many directions for our future research.

7

Appendix: Instructions

Note: Following are the instructions for Session 4 at Hokkaido University. Other sessions di¤eredfrom this session in terms of the order of the games.
Welcome to our experiment! In this experiment, you will be asked to play eight games. In eachgame, you will be asked to choose either to accept a value that is randomly selected from a uniform
distribution with a lower bound of zero and an upper bound of 3,000, or to refuse this value and31

move on to the next round to wait for a higher value. If you are willing to accept an o¤ered value,
you click on the “Y” displayed on the PC screen; if not, you click on the “N”. You can continue
to search as long as you want, but please remember that your search activity will be terminatedcoercively with a probability of 5%, in which case you will automatically receive the value drawn in
the round immediately before termination. Your score will be determined according to the values
that you accept.
We would like you to play eight di¤erent games. The …rst game is as follows.
Game A: In each round, the computer randomly selects a value from a uniform distribution
with a lower bound of zero and an upper bound of 3,000. You decide whether to accept thevalue drawn from this distribution. If you accept the value, then you …nish your search andthe value is your score. If you do not accept the value, you move on to the next round andobserve another value newly drawn by the computer.
The next three games are as follows.
Game B-1: You are grouped with two other participants. Grouping is done randomly by thecomputer, and no member knows who the other members are. In this treatment, you play
a committee search activity with the other two members. In each round, the computer randomly selects a value for all three group members, including you, from a uniform distribution
with a lower bound of zero and an upper bound of 3,000. All three group members, including
you, receive the same value. You independently decide whether to accept the drawn value.
If you prefer to accept the value, you vote for stopping the search, but if you do not accept
the value, you vote against stopping. This committee search activity is stopped if at least
one member of the group votes for stopping. Otherwise, your group moves on to the next
round and observes another value newly drawn by the computer.
Game B-2: The process of Game B-2 is similar to that of Game B-1 except for the plurality
voting rule; that is, this committee search activity is stopped only if at least two-thirds ofthe members of the group vote for stopping.
32

Game B-3: The process of Game B-3 is similar to that of Game B-1 except for the plurality
voting rule; that is, this committee search activity is stopped only if all three members of thegroup vote for stopping.
The next three games are as follows:
Game C-1: You are grouped with two other participants. Grouping is done randomly by
the computer, and no member knows who the other members are. In this treatment, youplay a committee search activity with the other two members. In each round, the computer
randomly selects for each group member a value from a uniform distribution with a lower
bound of zero and an upper bound of 3,000. Each group member therefore has a di¤erent
value, and you do not know what value the other two members draw, and vice versa. You
decide whether to accept the drawn value. If you accept your value, you vote for stopping thesearch, but if you do not accept your value, you vote against stopping. This committee search
activity is stopped if at least one member of the group votes for stopping. Otherwise, yourgroup moves on to the next round, and each member receives another value newly drawn by
the computer.
Game C-2: The process of Game C-2 is similar to that of Game C-1 except for the plurality
voting rule; that is, this committee search activity is stopped only if at least two-thirds ofthe members of the group vote for stopping.
Game C-3: The process of Game C-3 is similar to that of Game C-1 except for the plurality
voting rule; that is, this committee search activity is stopped only if all three members of thegroup vote for stopping.
The …nal game is as follows.
Game A: In each round, the computer randomly selects a value from a uniform distribution
with a lower bound of zero and an upper bound of 3,000. You decide whether to accept thevalue drawn from this distribution. If you accept the value, then you …nish your search and33

the value is your score. If you do not accept the value, you move on to the next round andobserve another value newly drawn by the computer.
Before starting the experiment, we would like you to practice Game A once. Please let us
know if you have any questions. We will explain the rule of each game again before it starts.
After the experiment, please respond to a questionnaire. You will be paid an appearance fee of
JPY1,000. The performance pay will be determined based on one of the scores from the eightgames you randomly choose, and your payment will be calculated as JPY1 for each scoring point.
Payment processes will take place after the experiment is concluded. Please be quiet and do notcommunicate with other participants during the experiment. Thank you for your participation.

References[1] Albrecht, J., Anderson, A., and Vroman, S. (2010) “Search by Committee,” Journal of Economic Theory, 145(4), pp.1386–1407.
[2] Boone, J., Sadrieh, A., and van Ours, J. C. (2009) “Experiments on Unemployment Bene…t
Sanctions and Job Search Behavior,” European Economic Review, 53(8), pp.937–51.
[3] Compte, O. and Jehiel, P. (2010) “Bargaining and Majority Rules: A Committee Search Perspective,” Journal of Political Economy, 118(2), pp.189–221[4] Cox, J. C., and Oaxaca, R. L. (1989) “Laboratory Experiments with a Finite-Horizon JobSearch Model,” Journal of Risk and Uncertainty, 2, pp.301–30.
[5] Cramer, J. S., Hartog, J., Jonker, N., and Van Praag, C. M. (2002) “Low Risk Aversion
Encourages the Choice for Entrepreneurship: An Empirical Test of a Truism,” Journal of
Economic Behavior and Organization, 48, pp.29–36.
[6] Fischbacher, U. (2007) “Z-Tree Zurich Toolbox for Ready-Made Economic Experiments,” Experimental Economics, 10, pp.171–78.
34

[7] Harrison, G. W. and Morgan, P. (1990) “Search Intensity in Experiments,”Economic Journal,
100, pp.478–86.
[8] Schunk, D. (2009) “Behavioral Heterogeneity in Dynamic Search Situations: Theory and Experimental Evidence,” Journal of Economic Dynamics and Control, 33, pp.1719–38.
[9] Schunk, D. and Winter, J. (2009) “The Relationship between Risk Attitudes and Heuristics in
Search Tasks: A Laboratory Experiment,” Journal of Economic Behavior and Organization,
71, pp.347–60.

35

Table 1: Average Search Durations
Voting rule #Searchers Sample Mean
S.D.
Value
Game A(1)
1
60
3.233
3.306
Game B-1
commonone vote
3
60
2.650
1.571
Game B-2
commonmajority
3
60
2.750
1.684
Game B-3
commonunanimity
3
60
3.600
2.981
Game C-1
different
one vote
3
60
1.300
0.561
Game C-2
different
majority
3
60
2.850
1.921
Game C-3
different
unanimity
360
11.7008.947
Game A(8)
1
60
4.633
4.422
Average
60
4.090
4.992
Game A(1) and Game A(8) represent the first and last trial of Game A, respectively.

Max
18
6
6
13
3
7
32
21
32

Min
1
1
1
1
1
1
1
1
1

Table 2: Comparisons with Single-Agent Search (First Game A)
Game B-1
commonone vote The search duration is shorter at the 10% level of significance.
Game B-2
commonmajority The search duration is insignificantly shorter.
Game B-3
commonunanimity The search duration is insignificantly longer.
Game C-1
different
one vote The search duration is shorter at the 1% level of significance.
Game C-2
different
majority The search duration is insignificantly shorter.
Game C-3
different
unanimity The search duration is longer at the 1% level of significance.
Note: We compared the means of the search durations using the one-tailed t-test.

Table 3: Comparisons of Search Durations between Single-Agent Search and Committee SearchFirst Game A Game C-1
The search duration is shorter in Game C-1 at the 1% level of significance.
First Game A Game C-2
The search duration is insignificantly longer in Game C-2.
First Game A Game C-3
The search duration is longer in Game C-3 at the 1% level of significance.
Note: To control for heterogeneity of preferences among group members, we tested the implications of the
AAV model by deducting a difference of the search duration between first Game A and Game C from adifference of the search duration between first Game A and Game B. In fact, we compared the means of thesearch durations between Games B and C using the one-tailed t-test.

36

Table 4: Estimates of Search Durations (OLS)
Search duration[1][2]
Game B-1-1.1383-1.1383 *(0.6916)(0.6564)
Game B-2-1.0383-1.0383(0.6682)(0.6491)
Game B-3-0.1883-0.1883(0.7324)(0.7043)
Game C-1-2.4883 ***-2.4883 ***(0.6502)(0.6118)
Game C-2-0.9383-0.9383(0.6790)(0.6500)
Game C-3
7.9117 ***7.9117 ***(1.1441)(1.2278)
Game A(8)0.29000.2900(1.0881)(1.1284)
Game order
0.15860.1586(0.1242)(0.1254)
Risk aversion-353.158(503.46)
Female[3]-1.1383 *(0.6607)-1.0383(0.6521)-0.1883(0.7051)-2.4883 ***(0.6162)-0.9383(0.6543)7.9117 ***(1.2293)0.2900(1.1336)0.1586(0.1258)

Constant

1.6102(1.1131)

3.169 ***
(0.4777)

0.0282(0.3641)3.0311 ***(0.6668)

Individual effect

YES

NO

NO

N
480
480
480
F-test
4.234226.611126.5625R2
0.47810.36680.3660Robust standard errors are in parentheses. *** 1% significance, ** 5%significance, * 10% significance. The dependent variable is eachsubject's search durations. Game B-1 (common value + one-vote rule),Game B-2 (common value + majority rule), Game B-3 (common value+ unanimity rule), Game C-1 (different values + one-vote rule), Game
C-2 (different values + majority rule), Game C-3 (different values +unanimity rule). Game A(8) is the single-agent search game that
subjects played in the last trial, which captures the anchoring effect,
compared with the first trial of Game A (A(1)). We calculate theabsolute risk aversion index using the willing -to-pay price for a lottery
with a 25% chance of winning JPY2,000 (EUR15.8) but a 75% chanceof receiving JPY0 (EUR0).

37

Table 5: Tests for Search Durations
Difference in the
Null hypotheses
Std. Err.
t valuep-valueTestcoefficients[1] Coef(C-1)-Coef(B-1)≥0-1.3500
0.3691-3.6600
0.0000
Rejected[2] Coef(C-2)-Coef(B-2)=0
0.10000.41440.24000.8090 Not Rejected[3] Coef(C-3)-Coef(B-3)≤08.10001.13127.16000.0000
Rejected
Note: Each row represents differences in the search duration between Games B and Games C by each plurality voting rule, using the results fromcolumn [1] of Table 4, when the reference group is defined as the first Game A. The magnitude of the coefficients on Game B is attributable to theheterogeneity among group members in terms of their preferences. The magnitude of the coefficients on Game C is attributable to the heterogeneityof preferences among group members plus the heterogeneity in terms of what value the other group members draw. Therefore, the differences
between the coefficients between Game B and C indicate the marginal effects derived only by the second heterogeneity in terms of what value theother members draw, after controlling for the first heterogeneity of preferences among group members.

38

Table 6: Average Marginal Effects on the Probabilities of Voting to Stop the Search(Probit Estimations)
Willing to accept=1
[1]
[2]
[3]
[4]
Coef.
Coef.
Coef.
Coef.
Drawn value
0.0003 ***0.0003 ***0.0003 ***0.0003 ***(0.0000)(0.0000)(0.0000)(0.0000)Round
0.0039 ***-0.0016-0.0017(0.0010)(0.0012)(0.0012)Game B-1
0.0460 *0.0566 **0.0664 **0.0798 **(0.0265)(0.0271)(0.0320)(0.0326)
Game B-2
0.0859 ***0.0949 ***0.0981 ***0.1117 ***(0.0239)(0.0243)(0.0288)(0.0293)
Game B-3
0.1098 ***0.1170 ***0.1174 ***0.1335 ***(0.0343)(0.0348)(0.0338)(0.0332)
Game C-1
0.1153 ***0.1311 ***0.1360 ***0.1500 ***(0.0319)(0.0318)(0.0434)(0.0451)
Game C-2
0.1496 ***0.1577 ***0.1497 ***0.1575 ***(0.0254)(0.0254)(0.0323)(0.0330)
Game C-3
0.1360 ***0.1182 ***0.1299 ***0.1412 ***(0.0218)(0.0231)(0.0254)(0.0253)
Game A(8)0.02850.0317-0.0037
0.0096(0.0321)(0.0325)(0.0372)(0.0373)
Game order-0.0111 ***-0.0116 ***-0.0087 **-0.0094 **(0.0035)(0.0035)(0.0042)(0.0042)58.507 ***Risk aversion(14.3315)
Female0.0165(0.0124)
Individual effect
YES
YES
NO
NO
N
1963
1963
1963
1963
Pseudo R2
0.78390.78960.62550.6201Wald chi2
350.95341.84451.01
460.35Log pseudolikelihood -268.378-261.329-465.161-471.936
Robust standard errors are in parentheses. *** 1% significance, ** 5% significance, * 10%
significance. The dependent variable represents one if a subject chooses to stop searching,regardless of whether the search is actually ended. Game B-1 (common value + one-voterule), Game B-2 (common value + majority rule), Game B-3 (common value + unanimity
rule), Game C-1 (different values + one-vote rule), Game B-2 (different values + majorityrule), Game C-3 (different values + unanimity rule). Game A(8) is the single-agent searchgame that subjects played in the last trial, which captures the anchoring effect, comparedwith the first trial of Game A (A(1)). We calculate the absolute risk aversion index using thewilling -to-pay price for a lottery with a 25% chance of winning JPY2,000 (EUR15.8) but a
75% chance of receiving JPY0 (EUR0).

39

Table 7: Tests for Threshold Effects
Difference in the
Null hypotheses
Std. Err.
z valuep-valueTestcoefficients[1] Coef(C-1)-Coef(B-1)≤00.07450.03072.42640.0078
Reject[2] Coef(C-2)-Coef(B-2)≤00.06280.01873.35910.0000
Reject[3] Coef(C-3)-Coef(B-3)≤00.0011
0.02260.05050.3050
Not Reject
Note: The coefficients on Games B and C show the average threshold effects compared with the first trial of Game A from column [2] of Table 6.
The magnitude of the coefficients on Game B is attributable to the heterogeneity among group members in terms of their preferences. The magnitudeof the coefficients on Game C is attributable to the heterogeneity of preferences among group members plus the heterogeneity in terms of what value
the other group members draw. Therefore, the differences between the coefficients between Games B and C indicate the average threshold effects
derived only by the second heterogeneity in terms of what value the other members draw, after controlling for the first heterogeneity among groupmembers.

Table 8: Comparisons of the Reservation Values among Plurality Voting Rules
Difference in the
Null hypotheses
Std. Err.
z valuep-valueTestcoefficients[1] [Coef(C-1)-Coef(B-1)]-[Coef(C-2)-Coef(B-2)]≤00.01170.03560.32920.3707
Not Reject[2] [Coef(C-2)-Coef(B-2)]-[Coef(C-3)-Coef(B-3)]≥00.06160.02852.16340.9846
Not Reject
Note: The coefficients on Games B and C also show the average probability effects of voting to stop searching, compared with the first trial of GameA from column [2] of Table 6. Similarly to Table 7, the differences between the coefficients between Games B and C indicate the average
probability effects of voting to stop searching derived only by the second heterogeneity in terms of what value the other members draw, after
controlling for the first heterogeneity among group members. In other words, [1] tests the null hypothesis that the "reservation value" is equal to or
lower in the majority rule than in the one-vote rule. [2] tests the null hypothesis that the "reservation value" is equal to or larger in the unanimity rulethan in the majority rule.

40

Figure 1: The probability of continuing the search under each plurality voting rule

Note: [1-P(x, 1, 1)] and [1-P(x, 3, i)] represent the probability of stopping the search.
Figure 2: Threshold and vote aggregation effects under the one-vote rule41

Figure 3: Threshold and vote aggregation effects under the unanimity ruleFigure 4: Threshold and vote aggregation effects under the majority rule42