Discussion Paper No. 690

BELIEF UPDATING IN INDIVIDUALAND SOCIAL LEARNING:
A FIELD EXPERIMENT ON THE INTERNET

Shunichiro Sasaki
andToshiji Kawagoe

May 2007

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Belief updating in individual and social learning:
A field experiment on the Internet
Shunichiro Sasaki a,∗, Toshiji Kawagoe baInstitute of Social and Economic Research, Osaka University,
6-1 Mihogaoka, Osaka 567-0047, Japanb

Future University – Hakodate,116-2 Kamedanakanocho, Hakodate, Hokkaido 041-8655, Japan

Abstract
We conducted a field experiment on the Internet and investigated the participants’ belief
updating in an individual learning environment where they observe a sequence ofprivate signals and in a social learning environment where they observe a sequence ofother people’s actions. We observed that participants do not update their posterior
beliefs as efficiently as Bayesian, and that participants rely more on private signals thanon other people’s actions even when the informativeness of both is identical.
Furthermore, we confirmed that participant’s trust in other people’s actions and theirconformity to other people’s actions are affected by their demographic characteristics.
JEL Classification Codes: C93; D83
Keywords: Belief updating, individual learning, social learning, field experiment,
conformity∗

Corresponding author. Phone: +81-6-6877-5111 (ex. 9179), Fax: +81-6-6878-2766

Email addresses: ssasaki@iser.osaka-u.ac.jp (S. Sasaki), kawagoe@fun.ac.jp (T. Kawagoe)

1. Introduction
We conducted a field experiment on the Internet and investigated how participants
update their posterior beliefs on the underlying state of the world in an individual
learning environment, where they can observe a sequence of private signals, and in asocial learning environment, where they can observe a sequence of other people’s
actions. We analyzed whether participants’ belief-updating behaviors are consistent
with Bayesian theory and whether they differ depending on the learning environment,
comparing with the results of the previous laboratory experiments on informationalcascades. In addition, by measuring participant’s degree of trust in other people’s
actions and their conformity to other people’s actions, we examine whether they areaffected by participant’s demographic characteristics.
We observed that participants do not make use of private signals and other people’s
actions as efficiently as Bayesian theory assumes. We also found that participants reporthigher posterior beliefs in an individual learning environment than in a social learning
environment, even when the theoretical informativeness of the observed sequence ofprivate signals and other people’s actions is identical. In addition, we confirmed thatparticipant’s trust in other people’s actions and their conformity to other people’s
actions are affected by some of their demographic characteristics.
In the following section, we outline the field experiment and show the framework ofindividual and social learning. In Section 3, we present the behavioral hypotheses
derived from Bayesian theory. In Section 4, we examine whether participants’belief-updating behaviors are consistent with the behavioral hypothesis and whether
they differ depending on the learning environment. In Sections 5 and 6, we measureparticipant’s degree of trust in other people’s actions and their conformity to other1people’s actions and investigate whether they are affected by their demographic
characteristics. In Section 7, we conclude the discussion.
2. Individual and social learning in the field experiment
The field experiment was conducted from February 23 to February 24, 2007, for theregistered monitors of ‘goo research’, a polling agency in Japan. The monitors’ ages
ranged from 20 to 49 and their various occupations were (1) managers in a privatecompany, (2) employees in an administrative position, (3) teachers, (4) lawyers, CPAs,
and tax accountants, or (5) students in junior colleges, universities, or graduate schools1.
The field experiment was conducted on the Internet. We sent emails that notified themof the URL at which the experiment would be conducted and 1033 monitors
participated in the experiment 2 . When they logged onto the web site, they wererandomly categorized into four groups G ∈{1,2,3,4} .
The following situation related to decision making under uncertainty was describedto the participants in the experiment. There are two states of the world ω ∈{ A, B} andeach state is realized with the commonly known priors Pr( A) = Pr( B) = 1 / 2 . Participants
do not know which state will be realized. However, they can infer the state of the world
by observing either a ‘private signal’ σ (ω ) of which preciseness is Pr(σ (ω ) | ω ) = 2 / 3 orother people’s predictions π (ω ) . The role of participant i in group G in thisdecision-making problem is to submit a prediction about which state will be realized1

For details of participants’ demographic characteristics, see Table 1.

2

They were paid 50 points (equivalent to 50 Japanese yen), which can be pooled and can be exchanged

for a cash voucher in payment for participation.

2Π (ω ) ti,G and its subjective posterior probability μ ( A) ti ,G and μ ( B) ti ,G = 1 − μ ( A) ti ,G inround t = 1,..., n based on observed sequences of private signals or other people’s
predictions.
We define the process of this decision-making problem as individual learning orsocial learning depending on the difference in information they observe as follows.
In the individual learning environment, participant i in group G submitsΠ (ω ) ti,G , μ ( A) ti ,G and μ ( B) ti ,G = 1 − μ ( A) ti ,G after observing the sequence of private

signals σ (ω ) for n = G + 1 rounds. In round t < n , participant i in group G faces aquestion QAGt , which presents the private signal σ ( A) t and asks the participant tosubmit Π (ω ) ti,G and μ ( A) ti ,G 3 . Thus, by question QAGt , the sequences of private
signals that participants in groups 1, 2, 3, and 4 observed from all rounds except the
final

round

were

(σ ( A)1 ) ,(σ ( A)1 , σ ( A) 2 ) ,(σ ( A)1 , σ ( A) 2 , σ ( A) 3 ) ,and

4(σ ( A)1 , σ ( A) 2 , σ ( A) 3 , σ ( A) 4 ) , respectively . We name these sequences SA1 , SA2 , SA3 ,and SA4 . On the other hand, in the final round t = n , participant i in group G faces
a question QAGn , which presents the private signal σ ( B) n and asks the participant tosubmit Π (ω ) in,G and μ ( A) in,G . Thus, by questions QAGt and QAGn , the sequences of

3

Although participants were told that the computer program would automatically generate hints σ ( A)or σ ( B ) in each round based on the realized state, we presented a predetermined sequence of hints
because we wanted to set up a situation where all the participants in each group observed the same
sequence of hints.
4

In round t , participants could also refer to the sequence of hints (σ (ω )1 ,..., σ (ω ) t −1 ) that they had

already observed.

3

private signals that participants in groups 1, 2, 3, and 4 observed from all rounds were(σ ( A)1 , σ ( B) 2 ),(σ ( A)1 , σ ( A) 2 , σ ( B ) 3 ),(σ ( A)1 , σ ( A) 2 , σ ( A) 3 , σ ( B) 4 ),and(σ ( A)1 , σ ( A) 2 , σ ( A) 3 , σ ( A) 4 , σ ( B) 5 ) , respectively. We name these sequences SA5 , SA6 ,SA7 , and SA8 . Using these sequences of private signals, we designed an individual

learning environment where participants in each group observed the agreeing private
signals in round t < n and observed the contradicting private signals in the final roundt = n as summarized in the third column of Table2.

In the social learning environment, participant i in group G submits Π (ω ) ti ,G ,μ ( B)ti ,G , and μ ( A) ti ,G = 1 − μ ( B) ti ,G after observing the ‘artificial’ sequence of otherpeople’s predictions π (ω ) or private signals σ (ω ) for n = G + 1 rounds 5 . In eachround t < n , participant i in group G faces a question QBGt that presents the otherpeople’s predictions π ( B) t and asks the participant to submit Π (ω ) ti,G and μ ( B) ti ,G 6.
Thus, by question QBGt , the sequences of other people’s predictions that participants in

5

Participants were told that several other people had already answered the same question that theparticipant was about to answer and that they had submitted their predictions after observing their private
signals or ‘their’ other people’s predictions in the same way as the participant would do. However, there
were no ‘other people’ and no one had submitted predictions earlier than any participants. Instead, we
presented ‘artificial’ sequences of other people’s predictions and private signals to participants because
we wanted to set up a situation where all the participants in each group observed the same sequence ofother people’s predictions and private signals.
6

As in the individual learning environment, in round t , participants could also refer to the sequence ofother people’s predictions (π (ω )1 ,..., π (ω ) t −1 ) that they had already observed.

4

groups 1, 2, 3, and 4 observed from all rounds except the final round were (π ( B)1 ) ,(π ( B)1 , π ( B ) 2 ) , (π ( B )1 , π ( B ) 2 , π ( B ) 3 ) , and (π ( B )1 , π ( B ) 2 , π ( B ) 3 , π ( B ) 4 ) , respectively.

We name these sequences SB1 , SB2 , SB3 , and SB4 . On the other hand, in the final
round t = n , participant i in group G faces a question QBGn that presents theprivate signal σ ( A) n and asks the participant to submit Π (ω ) in, G and μ ( B) in,G . Thus,
by questions QBGt and QBGn , the sequences of other people’s predictions and private
signals that participants in groups 1, 2, 3, and 4 observed from all rounds are(π ( B)1 , σ ( A) 2 ),(π ( B)1 , π ( B) 2 , σ ( A) 3 ),(π ( B )1 , π ( B) 2 , π ( B ) 3 , σ ( A) 4 ),and(π ( B)1 , π ( B) 2 , π ( B ) 3 , ( B) 4 , σ ( A) 5 ) , respectively. We name these sequences SB5 , SB6 ,SB7 , and SB8 . Using these sequences of other people’s predictions and private signals,

we designed a social learning environment where participants in each group observe
other people’s agreeing predictions in round t < n and observe the contradicting
private signal in the final round t = n as summarized in the third column of Table 2.
In explaining the general structure of the situation in the individual and the social
learning environments defined above, we presented the following description toparticipants. We paraphrased the state of the world as the situation where one of theboxes, either A or B, contains a piece of paper and a private signal as a hint.
“There are two boxes, A and B. One of the two boxes contains a piece of
paper on which “You Win” is printed, but the other one contains nothing.
The probability that Box A contains the piece of paper and that Box B
contains it are 50% and 50%, respectively. You are not informed which box
contains the piece of paper, but you can observe ‘hints’ or ‘other people’s
predictions’. Your role is to submit a prediction about which box contains
5the piece of paper and its probability for several rounds based on observed
sequences of hints or other people’s predictions.”
The question QAGt is presented in the beginning of round t as follows.
“ QAGt Hint: Box A contains the piece of paper.
Which box contains the piece of paper? Please submit your prediction. Then,estimate the probability that Box A contains the piece of paper and theprobability that Box B contains the piece of paper and choose one of the
combinations below that is the closest to your estimation. Note that this hint
reports the correct answer with probability 2/3 and the incorrect answer withprobability 1/3. Note also that the hints you observe may not always report
the correct answer, but the box containing the piece of paper does notchange from the first round to the final round.”After reading QAGt , participants submit Π (ω ) ti ,G and μ ( A) ti ,G in each round7.
Question QBGt is presented in the beginning of round t < n as follows, althoughquestion QBGn is presented in exactly the same way as QAGt .
“ QBGt The other person # t ' s prediction: Box B contains the piece of
paper.

7

Participants submit the prediction Π (ω ) ti,G by choosing one of the two buttons indicating ‘Box A’ and‘Box B’. Participants submit the subjective posteriors μ ( A) ti ,G and μ ( B) ti ,G = 1 − μ ( A) ti ,G bychoosing one of the 20 combinations of probabilities that “the probability that Box A contains the piece of
paper” and “the probability that Box B contains the piece of paper” from 0% to 100% at 5% intervals.

6

Which box contains the piece of paper? Please submit your prediction. Then,estimate the probability that Box A contains the piece of paper and theprobability that Box B contains it and choose one of the combinations belowthat is the closest to your estimation. Note that the other person # t has
submitted his/her prediction after observing other people’s predictions andhints in the same way as you do.”After reading QBGt , participants submit Π (ω ) ti,G and μ ( B) ti ,G in each round.

3. Behavioral hypotheses
In this section, we consider the belief-updating behavior by a rational Bayesianparticipant in an individual and a social learning environment.
Let PB(ω ) tG be the Bayesian posterior belief that the state of the world ω would
be realized in round t evaluated by participants in group G ∈{1,2,3,4} . In theindividual learning environment consisting of questions QAGt , each hint thatparticipants observe is informative, so that they can update their posterior probability ina Bayesian way in each round. For example, if participant i observes the sequence ofhints (σ ( A)1 ,..., σ ( A) t ) , the posterior probability should be:

PB( A) tG = Pr( A | σ ( A)1 ,..., σ ( A) t ) =Pr( A) Pr(σ ( A)1 ,..., σ ( A) t | A)
Pr( A) Pr(σ ( A)1 ,..., σ ( A) t | A) + Pr( B ) Pr(σ ( A)1 ,..., σ ( A) t | B )Given the sequence of hints we presented to participants, PB( A) tG in each group ateach round should be those summarized in the eighth column in Table 2.

7

For the prediction of the state Π (ω ) , participants except group 1 at round 2 should
always submit Π ( A) because they observe more σ ( A) than σ (B) .
However, in the social learning environment consisting of questions QBGt , otherpeople’s predictions observed by participants in each round may not be informative if
participants believe that other people update their beliefs in a Bayesian way as follows.
Suppose that the other person in round 1 observes σ (B )1 . Then, she would submitΠ ( B)11,G because her posterior belief is Pr( B | σ ( B)1 ) = 2 / 3 . Having observed her

prediction, if the other person in round 2 observes σ ( B ) 2 he would submit his
prediction Π ( B) 22,G because his posterior belief is Pr( B | π ( B )1 , σ ( B ) 2 ) = 4 / 5 . Having
observed these predictions, the other person in round 3 would submit Π ( B) 33,G
whichever

hint

she

observes

Pr( B | π ( B )1 , π ( B) 2 , σ ( B) 3 ) = 8 / 9

because

if

she

her

posteriorobserves

beliefσ (B ) 3

isandPr( B | π ( B )1 , π ( B) 2 , σ ( A) 3 ) = 2 / 3 if she observes σ ( A) 3 . Because the other person inround 4 knows that the other person in round 3 always submits Π ( B ) 3 , her prediction
does not convey any information about the state of the world. Thus, the other person inround 4 inevitably ignores the prediction by the other person in round 3 and he submits
his prediction in exactly the same way as by the person in round 3. In this way, people
after round 4 ignore their predecessors’ predictions and behave as if they were in round3 if the first two people’s predictions happen to correspond. Informational cascades,
formulated by Bikhchandani et al. (1992), are said to occur if all the individuals in asociety choose an identical action regardless of their private signals as a consequence ofrational Bayesian belief updating. In our configuration of sequences of other people’s
8

predictions, π (B ) 3 and π (B) 4 do not reflect σ ( B ) 3 and σ ( B ) 4 . Thus, participantscannot update their posterior beliefs in the sequences SB3 , SB4 , SB7 , and SB8 , andinformational cascades occur in these sequences. Specifically, their posterior beliefs
PB( B ) tG stay constant at 0.8 in the sequences SB3 and SB4 , and stay constant at 0.67

in the sequences SB7 and SB8 , as summarized in the eighth column of Table 2. Notethat in the sequences where informational cascades can occur, PB( B ) tG in the social
learning environment is always lower than PB( A) tG in the individual learning
environment because participants no longer update their posterior beliefs.
On the other hand, other people’s predictions are informative in the sequences where
informational cascades cannot occur in the social learning environment because otherpeople’s predictions should reflect their observed hints. Thus, participants update theirposterior beliefs in SB1 and SB2 . In addition, hints are always informative as in thecase of the individual learning environment and participants update their posterior
beliefs also in SB5 and SB6 . In such sequences, PB( B ) tG in the social learning
environment is the same as PB( A) tG in the individual learning environment because
participants can update their posterior beliefs in exactly the same way as in theindividual learning environment.
Given the sequence of other people’s predictions we presented to participants,
PB( B ) tG in each group at each round should be those summarized in the eighth column

in Table 2.
For the prediction of the state Π (ω ) , participants except group 1 at round 2 should
always submit Π ( B ) because they observe more π ( B ) than σ ( A) .

9

4. Belief-updating behavior in an individual and a social learning environment
From the predictions Π (ω ) ti ,G and the subjective posterior beliefs μ ( A) ti ,G andμ ( B ) ti ,G that all participants in group G had submitted for n = G + 1 rounds in each of
QAGt and QBGt , we collected data in a total of 16 different sequences. In this section,

we examine whether participants’ belief-updating behaviors are consistent with the
behavioral hypotheses by a rational Bayesian proposed in the previous section, andwhether they differ depending on the learning environment.
The sixth column of Table 2 reports the observed proportions of predictions Π ( A) ti ,Gand Π ( B) ti ,G . When participants observed the agreeing hint, other people’s agreeing
prediction, or the contradicting hint in the most recent round ( SA1 ,..., SA8 and
SB1 ,...SB4 ) , more than half of them submitted Π ( A) ti ,G ( Π ( B ) ti ,G ) for question QAGt

( QBGt ). When participants observed other people’s contradicting prediction in the mostrecent round (from SB5 to SB8 ), more than half of them submitted Π ( A) ti ,G forquestion QBGt , although they should have submitted Π ( B) ti ,G if they rationally update
their posterior beliefs. These results are inconsistent with behavioral hypotheses by arational Bayesian in that participants in QAGt ( QBGt ), except group 1 at round 2, should
always submit predictions Π ( A) ti ,G ( Π ( B ) ti ,G ). In the laboratory experiments on
informational cascades, such Bayesian inconsistent behaviors are often observed. In fact,
Anderson and Holt (1997), Dominitz and Hung (2004), and Hung and Plott (2001)
found that not all subjects submitted Π ( B) ti ,G when they had observed exactly the same10

sequence of the other people’s predictions and private signals as in SB6 and SB7 8.
However, the proportions of Π ( B) ti ,G in their laboratory experiments are higher thanthose observed in our field experiment.
For participants’ subjective posteriors, the average μ ( A) ti ,G increases as the numberof σ ( A) t increases in QAGt and the average μ ( B) ti ,G increases as the number ofπ ( B) t increases in QBGt as summarized in the seventh column in Table 2. However,

all of the observed average subjective posteriors are lower than the Bayesian posteriors
PB( A) tG or PB( B ) tG derived in the previous section. The t-tests shown in the ninth

column of Table 2 report that the observed differences between the average participants’posteriors and the Bayesian posteriors are statistically significant in almost all
sequences of hints and other people’s predictions. This result indicates that participants
certainly use hints and other people’s predictions in their probabilistic inferences, butthey cannot update their posterior beliefs as efficiently as Bayesian theory assumes evenwhen they sequentially observe informative hints in the individual learning environment.
In the laboratory experiments on informational cascades, Dominitz and Hung (2004),
Sasaki and Kawagoe (2006), and Stiehler (2003) observed that participants’ posterior

8

Anderson and Holt (1997) reported that the proportion of Π ( B ) ti ,G was 0.75 and 0.84 in the same

sequence of SB6 and SB7 . Dominitz and Hung (2004) reported that it was 0.52 and 0.80 in the same
sequence of SB6 and SB7 . Hung and Plott (2001) reported that it was 0.61 and 0.67 in the same
sequence of SB6 and SB7 . The preciseness of the private signal in their laboratory experiments is

Pr(σ (ω ) | ω ) = 2 / 3 , the same as the hints we used in our field experiment.

11beliefs were lower than the Bayesian posteriors in the same sequence of SB1 ,..., SB8 9.
However, as for the case of the proportion of Π (ω ) ti ,G , averages of μ (ω ) ti ,G observedin their laboratory experiments were higher than those observed in our field
experiment10.
The two cells between the bold lines in the seventh column of Table 2 compare
participants’ average posterior beliefs in the same sequences of hints and other people’s
predictions between the individual and the social learning environment. As we can see,the average posterior beliefs are higher in the individual learning environment ( QAGt )than in the social learning environment ( QBGt ). The Wilcoxon matched pair sign rank
tests shown in the right-most column of Table 2 report that the observed differences
between the average posterior beliefs in the individual learning environment ( QAGt ) and9

Dominitz and Hung (2004) reported that the average μ ( B ) t was 0.61, 0.68, 0.74, and 0.78 in thesame sequence of SB1 , SB2 , SB3 , and SB4 , respectively. Sasaki and Kawagoe (2006) reported thatthe average μ ( B) t was 0.49, 0.56, 0.58, and 0.59 in the same sequence of SB5 , SB6 , SB7 , andSB8 , respectively, and Stiehler (2003) reported that the average μ ( B) t was 0.47, 0.58, 0.63 in the same
sequence of SB6 , SB7 , and SB8 , respectively. The preciseness of the private information in Dominitz
and Hung (2004) and in Sasaki and Kawagoe (2006) was Pr(σ (ω ) | ω ) = 2 / 3 , and in Stiehler (2003)
was Pr(σ (ω ) | ω ) = 3 / 5 .
10These differences may reflect the different design and procedure between the laboratory experimentand the field experiment. Participants in a laboratory informational cascades experiment are typically paid
according to the correctness of their predictions and posterior beliefs, whereas participants in our field
experiment were paid regardless of the correctness of their predictions.

12those in the social learning environment ( QBGt ) are statistically significant even in thesequences where μ ( A) ti ,G and μ ( B) ti ,G should be equal ( SA1 and SB1 , SA2 and SB2 ,SA5 and SB5 , and SA6 and SB6 ).

This result indicates that participants rely more on their private signals than on otherpeople’s actions even when the informativeness of both is identical. This is clear
evidence of the existence of participants’ cognitive biases such as overconfidence on
private signals or distrust of other people’s actions, which are frequently found inlaboratory experiments on informational cascades (e.g., Anderson and Holt, 1997, andNöth and Weber, 2003). These studies typically argue participants’ overconfidence on
the grounds of their Bayesian inconsistent behavior in discrete choice problems.
However, we argue that our result is more robust than theirs because we elicited allparticipants’ posterior beliefs in both the individual and the social learning
environments and found that even the same participant, on average, submits higher
posterior beliefs in the individual learning environment than in the social learning
environment for all sequences.
5. Participant’s trust in other people’s actions and their demographic
characteristics
We observed that participants, on average, do not trust other people’s predictions asmuch as their own private signals in the social learning environment. Then, what type of
person is more likely to trust other people’s decisions in their probabilistic inferences?
In this section, we measure each participant’s degree of trust in other people’s
decisions and examine whether it is affected by their demographic characteristics.

13

We define TRUSTit,G = μ ( B) ti ,G − PB( B) tG as the degree of trust in other people’s
decisions for participant i at round t by using the data of posterior beliefs that eachparticipant submitted in each sequence of the QBGt questions. Note that TRUSTit,G ispositive if participant i trusts other people’s predictions and puts too much weight on
π Bt , and is negative if she distrusts them and puts too much weight on σ At , compared to
11

PB( B ) tG .

From the data on participants’ demographic characteristics, we consider participant
i ’s age ( AGEi ), his/her gender (a dummy variable MALE i , which equals 1 if

participant i is male and 0 otherwise), his/her educational background (a dummy
variable DEGi which equals 1 if participant i has (or is expected to have) a
university degree and 0 otherwise). For occupational variables, we use MAN i , whichequals 1 if participant i is a manager in a private company and 0 otherwise, ADMIN i ,which equals 1 if participant i is an employee in an administrative position and 0
otherwise, TEAi , which equals 1 if participant i is a teacher and 0 otherwise, and
LAWi , which equals 1 if participant i is a lawyer, a CPA, or a tax accountant and 0

otherwise. We pooled data for TRUSTit,G where participants submitted the posterior
beliefs μ ( B) ti ,G in each round for eight different sequences ( SB1 ,..., SB8 ). Then, we

11

We can check that averages of TRUSTi t,G in all sequence ( SB1 ,...SB8 ) are negative by looking at thevalues of μ ( B ) t and PB ( B ) tG in Table 2 as we have confirmed that participants, on average, distrust
other people’s actions in the previous section.

14

regressed TRUSTit,G against AGEi , MALEi , DEGi , MAN i , ADMIN i , TEAi , and
LAWi using the random effects model.

Table 3 reports the result of the regression. The estimated coefficient of MALEi issignificantly positive, suggesting that male participants are more likely to trust otherpeople’s decisions than female participants. For the occupational variables, theestimated coefficients of MAN i , ADMIN i , and LAWi are significantly negative.
Because we do not use a dummy variable for students, we argue that managers in a
private company, employees in an administrative position, and lawyers, CPAs, and tax
accountants are more likely than students to distrust other people’s decisions.
6. Participant’s conformity to other people’s actions and their demographic
characteristics
In this section, we measure participant’s conformity to other people’s decisions andexamine whether it is influenced by their degree of trust in other people’s decisions andtheir demographic characteristics.
To do this, we consider a situation where participants make decisions on the sameproblems with and without reference to other people’s decision making. If participant imakes an arbitrary decision when he cannot refer to other people’s decisions, but makes
the same decision as that chosen by some influential people when he can refer to theirdecisions, we regard such decisions as conformity to other people’s decisions.
A series of questions in QC are developed to investigate whether participants’decisions on an uncertain event are influenced by authoritative people in the social
learning environment. Participants were asked to predict which movie would win the79th Academy Award 2007 with and without reference to the result of the 64th Golden
15

Globe Award 2007, which is one of the most prestigious movie awards in the world12. If
participant i chooses a nonawarded movie if he/she cannot refer to the result of theGolden Globe Award and chooses the awarded movie if he/she can refer to it, we regard
this participant as conforming to the authoritativeness of the award.
First, in QC − 1 , we asked participants: “Which film will win the Best Picture award
in the 79th Academy Award 2007 among the following nominees: ‘The Departed’,‘Babel’, ‘Letters from Iwo Jima’, ‘Little Miss Sunshine’, or ‘The Queen’?”Second, in QC − 2 , we asked participants: “‘Babel’ won the Best Motion
Picture—Drama award in the 64th Golden Globe Award 2007, which is often considered
the preliminaries for the Academy Award. Which film will win the Best Picture award
in the 79th Academy Award 2007 among the following nominees: ‘The Departed’,‘Babel’, ‘Letters from Iwo Jima’, ‘Little Miss Sunshine’, or ‘The Queen’? We asked
you the same question in QC − 1 , but you can choose either the same or a different
answer based on how you feel right now.”
From the answers to these questions we define the variable OSCARi , which equals 1
if participant i chooses anything other than “Babel” in QC − 1 and “Babel” in
13
QC − 2 and 0 otherwise . Then, we regress OSCARi against TRUSTi t,G , AGEi ,12

Note that the answers to the questions in the experiment were accepted until February 24th, 2007, theday before the Academy Award ceremony. No participants knew the results of the Academy Award.
13

Of course, some of the participant would know the result of the Golden Globe Award or they could

look it up before answering QC − 1 . Such participants might not change their answers between

QC − 1 and QC − 2 . However, the proportion of participants who chose “Babel” increases from
QC − 1 to QC − 2 as summarized in Table 4.1.

16

MALEi , DEGi , MAN i , ADMIN i , TEAi , and LAWi using the random effects logit

model. The result of the regression is shown in the second and third columns of Table 5.
The estimated coefficient of TRUSTit,G is significantly positive. We argue that theparticipants who relatively trusted other people’s decisions in QBGt are more likely toconform to authoritative people’s decisions. The estimated coefficient of AGEi issignificantly positive and that of MALEi is significantly negative, implying that older
and female participants are more likely to conform to authoritative people’s decisions.
For the occupational variables, the coefficients of MAN i , ADMIN i , and LAWi are
significantly negative. Thus, we argue that participants working in these occupations areless likely to conform than students.
A series of questions in QD are developed to investigate whether participants’
preferences are influenced by other people in the social learning environment.
Participants were asked to answer whether they support the Abe cabinet in Japan withand without reference to various opinion polls. If participant i changes his/her answer
in accordance with the representative result of opinion polls if he/she can refer to them,
we regard such participants as conforming to other people’s preferences.
First, in QD − 1 , we asked participants: “Do you support the Abe cabinet?”
Second, in QD − 2 , we presented the results of four opinion polls, which show adecline in the approval rates of the Abe cabinet from 63%–70% to 39–51% in fourmonths. Then, we asked participants: “The figure below shows changes in the approvalrates of the Abe cabinet from four opinion polls. As you can see, the approval rates of
the Abe cabinet have declined from September 2006 when the Abe cabinet was
inaugurated. Do you support the Abe cabinet? We asked you the same question in17

QD − 1 , but you can choose either the same answer or a different answer. Please makeyour decision based on how you feel right now.”
From the answers to these questions we define the variable CABINETi , which equals
1 if participant i answered “I support the Abe cabinet” in QD − 1 and answered “I donot support the Abe cabinet” in QD − 2 and 0 otherwise 14 . Then, we regress
CABINETi against TRUSTit,G , AGE i , MALE i , DEGi , MAN i , ADMIN i , TEAi , and
LAWi using the random effects logit model. The results of the regression are shown in

the fourth and fifth columns of Table 5.
The estimated coefficient of TRUSTit,G is significantly positive. We argue that theparticipants who relatively trusted other people’s decisions in QBGt are more likely toconform to other people’s preferences. The estimated coefficients of AGEi and DEGi
are significantly negative, implying that older and educated participants are less likely
to conform. The estimated coefficients of MAN i , ADMIN i , and TEAi are significantly
positive; thus we argue that participants working in these occupations are more likely toconform than students.
Although the degree of trust in other people’s decisions in QBGt positively
affects the conformity in both questions of QC and QD , effects of participants’demographic characteristics on the conformity are not consistent for these two questions.
In order to explain this inconsistency, we may have to consider the possibility that theconformity in QC and QD is caused by different mechanisms. In the literature of

14

The proportion of participants choosing “not support” increases from QD − 1 to QD − 2 assummarized in Table 4.2.

18

social psychology, Deutsch and Gerard (1955) distinguished two types of social
influence which causes the conformity. They refer to the informational social influenceas the influence “to accept information obtained from another as evidence about
reality.” They also refer to the normative social influence as the influence “to conformwith the positive expectations of another.” In QC , if participants make use of the resultof the Golden Globe Award in predicting which movie would win the Academy Award,
we can interpret that their conformity is caused by the informational social influence.
On the other hand in QD , if participants feel that they should comply with other people
after recognizing the fact that many other people do not support the Abe cabinet, we caninterpret that their conformity is caused by the normative social influence.
7. Concluding remarks
This study examines belief-updating behavior in individual and social learning
environments. We found that participants certainly use the sequences of private signals
and other people’s predictions in their probabilistic inferences, but they cannot update
their posterior belief as efficiently as Bayesian theory assumes because their posterior
beliefs are always lower than Bayesian posteriors even when they sequentially observe
informative private signals in the individual learning environment. In addition, theposterior beliefs that participants submitted in the individual learning environment arealways higher than those in the social learning environment even when theinformativeness of the sequences of private signals and other people’s predictions isexactly the same. This observation is a clear evidence of participants’ overconfidence
on their own private signals or distrust of other people’s actions. Furthermore, we
confirmed that participant’s trust in other people’s actions is affected by their19demographic characteristics. For the analysis of participant’s conformity, although theirtrust in other people’s actions positively affects the conformity for the two different
situations, effects of participants’ demographic characteristics on the conformity are notconsistent between them. In order to explain this inconsistency, we need further
investigation on the mechanism how different social influences cause the conformity inthe social learning environment as Deutsche and Gerard (1955) pointed out.

20

Acknowledgements
This research was supported by the Grant-in-Aid for Scientific Research 18830037 of
the Ministry of Education, Culture, Sports, Science, and Technology in Japan and the
21st Century COE program at Osaka University.

References
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Bikhchandani, S., Hirshleifer, D., Welch, I., 1992. A theory of fads, fashion, custom,and cultural change as informational cascade. Journal of Political Economy 100,
992–1026.
Deutsch, M., Gerard, H. B., 1995. A study of normative and informational social
influences upon individual judgment. Journal of Abnormal and Social Psychology 51,
629-639.
Dominitz, J., Hung, A. A., 2004. Homogeneous actions and heterogeneous beliefs:
experimental evidence on the formation of information cascades. Working Paper.
Carnegie Mellon University.
Hung, A. A., Plott, C. R., 2001. Information cascades: replication and an extension to
majority rule and conformity-rewarding institutions. American Economic Review 91,
1508–1520.

21

Nöth, M., Weber, M., 2003. information aggregation with random ordering: cascades
and overconfidence. Economic Journal 113, 166–189.
Sasaki, S., Kawagoe, T., 2006. Can you believe your neighbors’ behaviors? Economics
Bulletin 3(11), 1−11
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Systems.

22

Table 1. Participants’ demographic characteristics
Characteristics
GenderAge

Occupation

Observations
Male

678 (65.63%)

Female355 (34.37%)

20–29

506 (48.98%)

30–39

197 (19.09%)

40–49

330 (31.95%)

Managers in private company

125 (12.10%)

Employees in an administrative position

173 (16.75%)

Teachers

143 (13.84%)

Lawyers, CPAs, Tax accountants

12 (1.16%)

Students in junior colleges, universities, or

479 (46.37%)

graduate schools
Educational

Junior high school (graduated)

2 (0.19%)

Background

High school (dropped out)

6 (0.58%)

High school (graduated)

104 (10.07%)

Junior college (dropped out)

5 (0.48%)

Junior college (graduated or will graduate)

76 (7.36%)

University (dropped out)

35 (3.39%)

University (graduated or will graduate)

617 (59.73%)

Graduate school – Master’s course (dropped out)

5 (0.48%)

Graduate school – Masters’ course (graduated or

121 (11.71%)

will graduate)
Graduate school – Doctoral course (dropped out)

11 (1.08%)

Graduate school – Doctoral course (graduated or

41 (3.97%)

will graduate)
Not answered

10 (0.97%)

23

Table 2. Proportion of Π (ω ) ti,G , average μ (ω ) ti,G , PB(ω ) tG , and results of tests

24

Table 3. Participant’s trust in other people’s actions and their demographic
characteristics
Dependent variablep>|z|

TRUSTi t,G

AGEi

0.13850.180

MALE i3.1803

0.010

DEGi

1.1803

0.375

MAN i

–4.3092

0.086

ADMIN i–5.1482

0.032

TEAi

0.0099

0.962

LAWi

–9.00240.078

Constant–23.8344

0.000

Number of observations

3577

Number of participants

1023

R2

0.0125Wald Chi2(7)18.48

Prob>Chi2

0.010025

Table 4.1. The proportion of answers in QC − 1 and QC − 2
QC − 1

Answers

QC − 2Babel

0.2197

0.3359The Departed

0.09490.0842

Letters from Iwo Jima

0.55370.4695

Little Miss Sunshine

0.06970.0591The Queen

0.06200.0513

Table 4.2. The proportion of answers in QD − 1 and QD − 2
Answers

QD − 1QD − 2

Support0.30400.2865

Not support

0.69600.713526

Table 5. Participant’s conformity to other people’s actions and their demographic
characteristics
Dependent variable

p>|z|

OSCARip>|z|CABINETi

TRUSTi t,G0.0086

0.096

0.020

0.028

AGEi

0.04200.094–0.1651

0.000

MALE i–0.6217

0.036–0.0952

0.846

DEGi

–0.50320.125–1.0434

0.022

MAN i

–1.97460.001

2.7566

0.003

ADMIN i–1.5848

0.007

3.2674

0.000

TEAi

–0.27250.590

1.9052

0.015

LAWi

–2.05700.062–17.8440

1.000

Constant–3.5477

0.000–1.57170.131

Number of

3577

3577

1023

1023

Wald Chi2(8)

35.44

28.81

Prob>Chi20.00000.0003

observations
Number ofparticipants27