Download Research on Free-riding Behavior under Different Punishment

2015 International Conference on Management Science & Engineering (22th)
October 19-22, 2015
Dubai, United Arab Emirates
Research on Free-riding Behavior under Different Punishment Conditions
ZHOU Yan，GUO Cai-cai，QIU Ling，ZHAO Chun-qi
School of Management, Harbin Institute of Technology, Harbin 150001, P.R.China
Abstract: In recent years, free-riding behavior is an
urgent problem which has caused a heated debate. The
existence of the free-riding behavior will reduce the
efficiency of the group and loss the benefits. The
different punishment mechanisms are introduced to this
paper. The control mechanism of free-riding behavior is
analyzed by different punishment levels and punishment
decision-making mechanisms. First, we use z-tree
software to collect experimental data. Second, we use the
nonparametric test and the regression analysis to test the
experimental results. Third, through the analysis of the
degree of punishment and the method of group
decision-making, we get the optimal mechanism to
control free-riding behavior. We find that our results can
be applicable to control free-riding behavior in reality.
We can control free-riding behavior by three methods.
First, the free-riding behavior can be reduced by the
increasing of the punishment. Second, the free-riding
behavior can be reduced by the collective
decision-making. Third, we can combine the two
methods to reduce free-riding behavior.
Keywords: free-riding behavior, punishment
conditions, group decision-making, control mechanisms
1 Introduction
Free-riding behavior will reduce the efficiency of
the team and decrease the collective benefits. How to
control free-riding behavior is an important problem. The
traditional solution is supervision and the method of
privatization. However, these methods can only solve
parts of the public goods dilemma and lack the universal
applicability. Therefore, as the number of domestic and
foreign research is growing, experimental economics is
becoming a method to solve the free-rider problem. This
paper uses experimental economics to explore the
individual decisions in different punishment conditions.
The phenomenon of free-riding has been studied for a
long time. The resources will achieve the Pareto optimal
in the market conditions. However, some items are not
privacy goods to some extent; it will cause social
dilemma in the provision of public goods.
Olson points that free-riding means the individual
Supported by the National Natural Science Foundation of
China(71203046)
978-1-4673-6513-0/15/$31.00 ©2015 IEEE
obtains others’ benefits without paying the cost.
Therefore, free-riding has been a heated topic in many
fields. Walk and Issac (1984) find that as the marginal
income of public goods provision by the individual is
increasing, the motivation of free-riding is decreasing [1].
Andreoni (1995) finds that compared with incentive
mechanism, the punishment mechanism is more effective
to reduce free-riding behavior[2]. Saijo and Nakamura use
the voluntary contribution mechanism to prove that
punishment can reduce the free-riding behavior [3]. Laury
(1999) presents that people who have a wealthy asset are
more likely to invest on public goods accounts [4].
Stimulation can alter free-riding behavior and people will
adjust their behaviors by comparing with the gains and
the losses of others [5].
After their study in this area, punishment
mechanism has been studied in many aspects. As the
introduction of voluntary supply punishment mechanism,
the level of voluntary supply is increasing. Fehr and
Gchter (2000) have confirmed that punishment can
effectively reduce free-riding behavior [6]. Masclet and
Noussair (2003) repeat their study and extend the
experiment, they also explore the effect of punish other
group members [7]. Carpenter(2007) studies the cost of
punishment as an important factor to solve free-riding
problems[8]. Gunnthorsdottir and Houser [9]point out that
contributions have played an important role in
decision-making, people will adjust their contributions
according to the benchmark. Nikiforakis (2010) point out
that the appropriate punishment can increase the level of
cooperation [10]. Price(2005) indicates that the altruistic
punishment can effectively promote the evolution of
cooperation even in very weak conditions[11]. Both the
asymmetric punishment institutions and the symmetric
institutions are efficient in generating cooperation [12].
Whether a free rider is a moral person or not is
determined by others’ behavior [13]. Sympathy is a main
factor to analyze punishment when people consider
morality and justice [14]. Muller and Sefton design an
experiment, they find that the contribution in stage one is
higher than stage two [15]. What’s more, the response of
individual to other people is still need to be considered in
public goods experiment [16].
Punishment can improve group members’
contributions, but it can not enhance the welfare [17].
Decker and Stiehler (2003) design an experiment and
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study the individual regulation and three kinds of
collective regulations [18]. Group size is an important
factor and the efficient gains are higher in the large group
[19]
.Ohtsubo and Masuda find that nearly half of the
people punish the dishonest but fair trustee [20]. When
group members can be expelled by others, the
endowment will be significantly increased [21]. The
participants can be mainly divided into three types,
including strong free riders, conditional cooperators of
reciprocators and strong cooperators [22]. Even the
information is inaccurate, people still willing to reveal
punishment [23]. Punishment and communication can
have an effect on contributions [24]. The network has a
mediating effect on contribution [25]. People will continue
to choose cooperation when punishment can not be
observed [26]. Free rider behavior has many kinds of
factors that need to be considered in the experiment [27].
The test is very important to make a faithful conclusion
[28]
. The presence of leader can increase the contributions
on public accounts [29]. Donation can be applicable to the
analysis of public goods, so public goods can be studied
in many aspects [30].
We find that punishment is an important factor to
control free-riding behavior, so we focus on the
punishment mechanisms. Our research can offer some
suggestions for further study in designing the experiment.
First, we can test the applicability of the existing
classical theory. We use experiment to simulate the
individual’s behavior which more apt to the reality. Our
behavior is not only determined by the rational analysis,
but also is affected by the individual environment.
Second, we study the relevant influence factors on
free-riding and analyze the most effective mechanisms to
reduce free-riding behavior. Third, we make relevant
policy recommendations by analyzing the individual’s
reflection on different punishment level and
decision-making method.
2 Experimental design and procedures
2.1 The design of individual punishment experiment
In the individual punishment experiment, we design
five kinds of experiment scenarios. The degrees of
punishment include 0, 1, 2, 3and 4. When the degree of
punishment is equal to 0, the experiment is the standard
voluntary experiment. N is the number of the group
(n>2), I represents the participant I. y is the endowment,
which means the initial experimental currency. The
participants can decide how much to invest on the public
accounts. Ci means the tokens which are invested on the
public account by participant I (0≤Ci≤y). ( y − Ci ) means
that the residential money will be invested on the private
account. a represents the marginal rate of the return, so
the revenue of participant (Ri) is as following:
n
Ri = y − Ci + a ∑ Ci
i =1
(1)
The improved experiments are more complicated
than the basic experiment. This paper adds a new stage to
the experiment. The degrees of punishment include 1, 2,
3 and 4. In the punishment experiment, there is a second
stage. In this period, the participants can get the
information about others’ contributions. They can buy
punishment point to reduce others’ income. However, the
punishment point is not free, each punishment point costs
one unit of experimental currency and the money will be
deducted from their income. Pij means the punishment
point of participant j that is given by participant i. e
means the marginal rate of punishment. The income of
participant I ( Ri ) is as following:
n
Ri = y − Ci + a ∑ Ci − ∑ Pij − e∑ Pji
(2)
The maximum of punishment point is the income of
the first stage. E represents the degree of punishment. In
the basic experiment, when the degree of punishment is
equal to 0, it is a standard basic experiment.
i=
1
j ≠i
j ≠i
2.2 The design of collective experiment
Compared with the individual experiment, the
collective experiment has an extra stage. The basic
design of experiment is similar to the individual
experiment. However, there is a group discussion about
whether enter the punishment period or not after the
investment period. Every group has four participants. If
the number of participants who choose the punishment
period outweighs two, the whole group will enter the
second stage. If the number of participants who choose
the punishment period less than two, the whole group
will not enter the second stage. If the number of
participants who choose the punishment period is equal
to two, the stage will be determined randomly by the
computer.
There is a punishment cost. If the participant wants
to punish others, each punishment point will reduce one
unit of experimental currency. Pij means the punishment
point of participant j which is given by participant i. e
means the marginal rate of punishment. The income of
participant I ( Ri ) is as following:
n
Ri = y − Ci + a ∑ Ci − ∑ Pij − e∑ Pji
i=
1
j ≠i
e ∈ {1, 2,3, 4}
j ≠i
(3)
. We design four
In this experiment,
kinds of scenarios. We use e to express the four degree of
punishment. When e is equal to 0, it is the basic
experiment. In this stage, the punishment decision is
made by all the groups and the degree of punishment can
be changed. In the last period, the experimental currency
can be converted to RMB as the income of experiment.
3 Assumptions
3.1 The individual punishment experiments
According to the evidence and experiment, most
people hate to be deceived by others and unfair treated
under the condition of social dilemma. Therefore, even if
they have to pay for the cost, those people who cooperate
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with others would like to punish free riders. If the
punishment can not give them long-term benefits, they
will still choose punishment. Therefore, we can make an
assumption based on the above analysis. Even if the
participants need to pay for the cost when they punish
others who do not choose cooperation, they still want to
punish the people. What’s more, if they cannot get
substantial benefits, these people will not change their
decisions.
The level of free-riding behavior is more deviated
from the level of cooperation. We can find that the
degree of punishment is related to their income. However,
the potential free riders can reduce the punishment by
improving their level of cooperation. In this experiment,
as the degree of punishment is growing, the participants
will offer more penalty points to the people who invest
more money on the private account, it will reduce the
income of the people who is be punished. Therefore, it
will affect the contribution on public accounts in the next
period and reduce the free-riding behavior. Therefore, we
can make the assumption 1:
H1: As the degree of punishment is growing, the
participant will invest more money on the public
account.
3.2 The collective punishment experiment
Compared with the individual punishment
experiment, the collective punishment experiment adds a
procedure of group choice by voting. According to the
hypothesis of the rational people, when the participants
enter the punishment procedure, the individual benefits
will decrease. When individual punish other members,
the individual needs to pay the cost. In collective
punishment, people can decide whether to punish others
or not. At the beginning, people don’t like to punish other
people. As the time is increasing, people would like to
punish free riders in order to meet an equal consideration.
Based on the above analysis, we can make the hypothesis
2.
H2: In the collective punishment experiment, as the
experiment repeated, the participants spend more money
on punishing others.
On the one hand, with the beginning of the
experiment, the income level will change differently. The
income of public account is closely related to the
individual behavior. The information of individual
income is public. It will give the participants some
information about whether others choose free-riding or
not. On the other hand, if the individual enter the
punishment period, it is difficult for them to give up
punishment. Therefore, we can make the hypothesis 3.
H3: Once the participants enter the period of
punishment, they will keep punishing others and rarely
choose not to punish again.
In a mild punishment experiment, as the degree of
punishment is increasing, the benefits of participants will
decrease due to the punishment. Overall, they don’t like
to punish others. According to the rational consideration,
they will choose punishment in a mild punishment.
Overall, we can make the hypothesis 4.
H4: As the degree of punishment is increasing, the
level of cooperation will increase in the mild punishment
mechanism.
We consider the degree of punishment and the
method of decision-making as two dimensions to analyze
the free-riding behavior. Overall, we get ten kinds of
punishment mechanisms. In these punishment
mechanisms, there is an optimal mechanism to control
free-riding behavior.
4 Results
4.1 Description statistics
We use the experimental economics to study
free-riding behavior, we recruited 144 volunteers by the
method of online publicity posters and community
notices. We analyze the careers of the participants in
order to make sure the universality of volunteers. The
distribution of participants is as following:
Farmer
Worker
Soldier
Student and teacher
White-collar workers
Others
Fig.1 The classification proportions
of participant’s occupation
We can see that the experiment has many kinds of
participants. Student and teacher are accounted for the
largest proportion. This is mainly because that students
and teachers have a lot of spare time. So they are more
willing to participate in the experiment. However, we
still collect the data of other participants with different
occupations.
We find that people whose age between 20 and 30
more likely to participate in this experiment. It also
indicates the students are most likely to attend this
experiment to some extent. The university student has
the most intensive curiosity. However, the elderly people
are the weakest.
What’s more, we still statistic the education of the
participants, we find that undergraduates are most likely
to attend this experiment. The participants include people
in junior high school and senior middle school. There are
also people who already graduate from university.
4.2 The result of individual punishment experiment
(1) Statistical test
We collect the observations of 72 participants; each
participant needs to attend the free-riding experiment
five times. In the individual decision-making experiment,
the participant needs to maintain independence. There is
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The average contribution retains flat when the degree of
punishment is equal to 1. In the residential scenarios,
there is an increase tendency. When people spend more
money on punishment, the contribution will become
higher. What’s more, the total income of the whole group
will become higher. Thus, we can verify that the
hypothesis 2 is correct.
Contribution on public
accounts
no communication among team members. Overall, we
obtain 18 groups. We use the Kruskal-Wallis test to
analyze that whether the experiment currency invested
on the public accounts obey the same distribution or not.
As shown in Tab.1, in the punishment experiment, the
data in the general level is significant and obey the same
distribution. It indicates that there is no sample problem
due to the different sessions and times which may cause
samples inaccurate.
Tab.1 Results of Kruskal-Wallis test
in individual decision-making
a,b
Test Statistics
Experiment
Chi-Square
df
Asymp. Sig.
“0”
4.390
“1”
22.246
“2”
20.454
“3”
34.526
“4”
38.201
71
0.96
71
0.02
71
0.04
71
0.00
71
0.00
"0"
15
"1"
"2"
10
"3"
5
"4"
0
0
5
10
Fig.3 The average contribution of participants in 10 rounds
Through the observation of the sample, we can find
the change of the experimental currency that invested on
the public accounts. We can get the average contribution
according to the contribution in ten repeated experiments.
As shown in Fig.2, e∈{0,1,2,3,4}= {3.15, 7.06, 12.08,
15.49,17.10}. As the degree of punishment is increasing,
the average contribution of public account will increase
at the same time. All in all, the control effect of
free-riding behavior will be improved at the same time.
(2)Regression analysis
During the course of the experiment, we need to
statistic the average contribution. At the same time, there
is a cost to punish others. We can establish the model
when the degree of punishment is different. We use the
Stata software to establish regression model to analyze
the free-riding behavior. The result of this regression
mode is shown as follows:
α 0 + α1 punfi ,t −1 + α 2 punsi ,t −1
pubi ,t − pubi ,t −1 =
+α 3 resi ,t −1 + e i ,t
pubi ,t − pubi ,t −1
20
The average of
contribution
5
(4)
In this formula,
represents the
increment of contribution that individual invest on public
accounts
in
t
period
compared
with
t-1
punf i , t −1
is the cost of penalty point that
period.
individual punishes others in the t-1 period.
15
10
15
Rounds
a. Kruskal Wallis Test
b. Grouping Variable: subject
0
20
punsi ,t −1
"0" "1" "2" "3" "4"
Fig.2 The average contribution of different punishment
degree
As we can see from Fig.2, the average contribution
in the punishment experiment is higher than experiment
without punishment.
According to the repeated game theory, each
experiment will repeat 10 times. We can see the change
of free-riding behavior through the change of investment
on public accounts during each round. As shown in Fig.3,
the experimental currency invested on public accounts in
punishment experiment is larger than the money invested
on the basic experiment.
As shown in Fig.3, the average contribution is
different during the different periods. In the first round,
the average contribution of participants is between 40%
and 63%. This data is consistent with the previous study.
After the first round, the average contribution is
decreasing when the degree of punishment is equal to 0.
is the total point that individual I is
resi ,t −1
punished by others.
represents the income of
individual i. According to the measurement equation,
when the degree of punishment is different, we can
acquire the change of the contribution on public
accounts.
As we can see from Tab.2, there is a positive
relationship between the penalty point in the last round
and the contribution in the next round when the degree of
punishment is equal to 1. When the degree of punishment
is high, participants will invest more money on public
accounts. As the income of participants is increasing,
people may invest more money in the next period. When
a person is punished by others, they are more likely to
change their free rider behavior.
As we can see from Tab.3, there is a positive
relationship between the penalty point in the last round
and the contribution in the next round when the degree of
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Tab.2 The cross-round change of public accounts when e=1
pub
Coef.
Std.Err.
t
P>|t|
punf
0.3741765
0.3556607
3.19
0.029
puns
0.8235584
0.2585624
1.05
0.002
res
0.7505912
0.1379658
5.44
0.000
_cons
(17.7910200)
3.2964870
5.40
0.000
Prob > F = 0.0000
R-squared= 0.3734
Adj R-squared = 0.3457
Tab.3 The cross-round change of public accounts when e=2
pub
Coef.
Std.Err.
t
P>|t|
punf
0.0426695
0.3462072
0.12
0.037
puns
1.1296320
0.2861325
3.95
0.000
res
0.1741345
0.1988131
0.88
0.038
_cons
4.9117750
5.3101240
0.92
0.358
As we can see from Tab.5, there is a positive
relationship between the penalty point in the last round
and the contribution in the next round when the degree of
punishment is equal to 4. When the degree of punishment
is high, participants will invest more on public accounts.
Similarly, there is a positive relationship between the
contribution on public accounts in the next round and the
income in the last period. As the income of participants
increasing, the individual will more likely to choose
cooperation. If a person like to punish others, they will
more likely to invest on public accounts for
demonstration.
Tab.5 The cross-round change of public accounts when e=4
pub
Prob > F = 0.0000
Coef.
Std.Err.
t
P>|t|
punf
0.2363969
0.1606476
1.47
0.014
puns
0.7723857
0.4148102
1.86
0.068
res
0.2303133
0.1114697
2.07
0.043
_cons
6.1017900
3.3123840
1.84
0.071
Prob > F = 0.0249
R-squared= 0.3601
R-squared= 0.2213
Adj R-squared = 0.3319
Adj R-squared = 0.2135
punishment is equal to 2. When the degree of punishment
is high, participants will invest more on public accounts.
Similarly, there is a positive relationship between the
contribution on public accounts in the next round and the
income in the last period. As the income of participants is
increasing, the individual will more likely to choose
cooperation. If a person is punished by others, they will
reduce free rider behavior in the next period. What’s
more, they will try to change this situation in the next
period in order to avoid the punishment.
As we can see from Tab.4, there is a positive
relationship between the penalty point in the last round
and the contribution in the next round when the degree of
punishment is equal to 3. When the degree of punishment
is high, the participant will invest more money on public
accounts. Similarly, there is a positive relationship
between the contribution on public accounts in the next
round and the income in the last period. As the income of
participants is increasing, the individual is more likely to
choose cooperation. It is means that the punishment is
effective to reduce free rider behavior.
Tab.4 The cross-round change of public accounts when e=3
pub
Coef.
Std.Err.
t
P>|t|
punf
0.3305715
0.1722184
1.92
0.049
puns
0.8877443
0.4469820
1.98
0.041
res
0.3182380
0.1528158
2.08
0.041
_cons
(8.8143500)
4.4225310
1.99
0.050
Prob > F = 0.0269
R-squared= 0.2228
Adj R-squared = 0.2187
4.3 The result of collective punishment experiment
4.3.1 Nonparametric test
The experimental design of the collective
punishment experiment is similar to the individual
experiments. What’s more, in the collective experiment,
there is a decision-making process by the group
members.
Tab.6 Results of Kruskal-Wallis test in punishment
experiment
Test Statisticsa,b
Chi-Square
df
Asymp. Sig.
“0”
7.3683
“1”
21.378
“2”
22.493
“3”
34.136
“4”
32.368
71
0.82
71
0.03
71
0.02
71
0.00
71
0.00
a. Kruskal Wallis Test
b. Grouping Variable: subject
We have collected the experimental data from 72
subjects and made 18 groups. Kruskal-Wallis test is used
to test the distribution of the contribution on the public
accounts. The result is shown as Tab. 6. In the
punishment experiment, the experimental data obey the
same distribution. The results suggest that there is no
problem due to the different sessions and times. It also
means that our samples are accurate. It also indicates that
the sample is fit for this experiment. Although people
may have different characteristics, it has no effect on this
experiment.
4.3.2 The results of the collective experiment
As we can see from Tab.7, there are seldom groups
that choose to enter the punishment stage at the
beginning of the experiment. Only one group enters the
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punishment stage when the degree of punishment is
equal to 1. However, as the experiment repeated, there
are more groups choose the punishment stage. We can
also see that people are more likely to choose the mild
punishment when the degree of punishment is different.
When the degree of punishment is higher, the
participants need more time to choose punishment.
15
10
Tab.7 The number and percentage when the group choose
the punishment period
Period
1
Period
2
Period
3
Category
“1”
“2”
“3”
“4”
number
1
0
0
0
percentage
5.5%
0
0
0
number
5
2
1
0
percentage
27.8%
11.1%
5.5%
0
number
7
2
0
1
percentage
38.9%
11.1%
0
5.5%
number
8
4
0
0
percentage
44.4%
22.2%
0
0
Period
5
number
11
7
2
1
percentage
61.1%
38.9%
11.1%
5.5%
Period
6
number
10
8
3
2
percentage
55.6%
44.4%
16.7%
11.1%
"1"
"2"
"3"
"4"
20
"0"
15
"1"
10
"2"
5
"4"
"3"
0
0
Period
7
number
11
6
3
2
percentage
61.1%
33.3%
16.7%
11.1%
Period
8
number
13
8
5
1
percentage
72.2%
44.4%
27.8%
5.5%
number
15
10
4
3
percentage
83.3%
55.6%
22.2%
16.7%
number
15
11
3
2
percentage
83.3%
61.1%
16.7%
11.1%
Period
10
"0"
Fig.5 The average contribution of different punishment
degree
Period
4
Period
9
0
Contribution on public accounts
Round
The average of
contribution
5
According to our statistics, there are 18 groups to
take part in ten rounds experiments. The number of
punitive rounds is 187. Once, a group chooses the
punishment mechanism, it is difficult for them to enter
the non-punitive period. As shown in Fig. 5, when the
degree of punishment is increasing, more money will be
invested on public accounts. In a word, the free-riding
behavior will be reduced. The conclusion is similar to the
individual decision-making.
As shown in Fig.6, the average contribution on
public account in the punishment experiment is higher
than the contribution in non-punitive experiment. People
invest more money on public accounts when the degree
of punishment is equal to 4. As the degree of punishment
is increasing, the average contribution is increasing. The
result is the same as the individual decision-making
mechanism.
5
10
15
The repeated times
Fig.6 The average contribution of participants in 10 rounds
4.4 The Comparative analysis of different punishment
mechanism
According to the analysis of the experiment, no
matter what kind of decision-making method, the
contribution is higher in punishment experiment than in
non-punitive experiment. As the experiment is repeated,
more money will be invested on public accounts. Next,
we will study the relationship between different
decision-making and free-riding behavior when the
degree of punishment is constant.
As shown in Fig. 7, when the degree of punishment
is equal to 1, the number of tokens be invested on public
accounts in collective decision-making period is higher
than in individual decision-making period. When the
collective use ballot to decide whether to enter
punishment period or not, all the participants will have
binding force and less likely to choose free-riding
behavior. What’s more, the level of collective
cooperation will be improved. People will invest more
money on public accounts when they are faced with the
group decision-making. It indicates that the group
decision-making is more useful to reduce free rider
behavior.
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contribution on
public accounts
20
10
0
0
5
Rounds
10
15
Individual decision-making
0
5
10
Group decision-making
15
Fig.9 The average contribution of different decision-making
mechanisms when the punitive effect is equal to 3
Rounds
Individual decision-making
Group decision-making
Fig.7 The average contribution in different decision-making
mechanisms when the punitive effect is equal to 1
As shown in Fig.8, the tokens be invested on public
account in individual decision-making experiment are
similar to the collective decision-making experiment.
The two lines are intertwined and increased in the same
direction. We can see that the participants are seldom
affected by different ways of decision-making. The two
kinds of decision-making methods have a similar impact
on free-riding behavior. It is difficult to judge which
method is more useful in this circumstance.
As shown in Fig10, when the degree of punishment
is equal to 4, we can obtain a similar conclusion as the
degree of punishment is equal 3. The difference is that
the average contribution is generally raised two tokens.
The result is also consistent with the foregoing
conclusion. As the degree of punishment is increasing,
the number of tokens that be invested on the public
accounts is increasing. The individual decision-making
may be a good way to reduce free rider behavior.
Contribution
on public
accounts
Contribution on public
accounts
12
10
8
6
4
2
0
20
10
0
0
5
10
15
Contribution on
public accounts
Rounds
15
Individual decision-making
Group decision-making
10
Fig.10 The average contribution of different
decision-making mechanisms when the punitive effect is
equal to 4
5
0
0
5
10
5 Conclusions
15
Rounds
Individual decision-making
Group decision-making
Fig.8 The average contribution of different decision-making
mechanisms when the punitive effect is equal to 2
As shown in Fig.9, when the degree of punishment
is equal to 3, compared with the collective
decision-making period, the number of tokens be
invested
on
public
accounts
in
individual
decision-making period is larger. The basic trend of the
two lines is the same. As the degree of punishment is
increasing, the participants are more likely to choose to
invest on public accounts. Therefore, the level of
cooperation will increase at the same time. In addition,
compared with the individual decision-making method,
the fluctuations of average contribution in collective
decision-making is larger. We find that it is better to
choose the individual decision-making method in order
to reduce free rider behavior.
In this paper, we judge the punishment mechanism
of free-riding behavior from the decision-making
mechanism and the degree of punishment aspects. We
study the optimal punishment mechanisms by analyzing
the eight different experiments.
In the individual punishment experiment, the
introduction of punishment mechanism can significantly
control the occurrence of free-riding behavior. What’s
more, as the degree of punishment is increasing, the
contribution on public accounts is increasing. To be
specific, the degree of punishment has a linear effect on
the average contribution of participants.
In the collective decision-making experiment, most
participants will choose the non-punitive experiment at
the beginning of experiment. Once they enter the
punishment period, they will keep punishing others and
rarely choose not to enter the punishment period again.
When the degree of punishment is different, the majority
of participants will choose the mild punishment
mechanism. In the mild punishment mechanism, the
level of cooperation is larger. However, when the degree
of punishment is increasing, people may tend to choose
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free-riding behavior. When the degree of punishment and
the decision-making method are considered as two
dimensions, we can get ten different punishment
mechanisms. There is an optimal combination of
mechanisms to control the free-riding among these
mechanisms.
When the degree of punishment is limited to a
constant extent, there is a negative relationship between
the degree of punishment and the free-riding behavior in
individual and collective decision-making experiment. In
other words, the punishment mechanism of free-riding
behavior is the most optimal mechanism when the degree
of punishment is equal to 4.
If the degree of punishment is only considered as an
important factor, we can make some conclusions. The
collective decision-making is more powerful to control
free-riding behavior when the degree of punishment is
equal to 1. The collective and the individual
decision-making sometimes have a similar impact on the
controlling of free-riding behavior and both equally valid.
However, as the degree of punishment is increasing, the
conclusion will change. The individual decision-making
method is more powerful than the collective
decision-making method to control free-riding behavior
when the degree of punishment is 3 or 4. In these two
situations, the individual decision-making method is the
most optimal mechanism.
According to the conclusion of this paper, we can
propose some suggests on controlling free-riding
behavior.
First, we can control free-riding behavior by
increasing the degree of punishment. Advocacy and
education can induce social preferences. These methods
can reduce self-interest of the individual and increase the
cooperation in public areas. However, the individual
self-interest may also reduce individual social
preferences, so cooperation cannot sustain. Therefore,
the maintaining of cooperation cannot simply rely on
some form of strategy and systems. It needs to consider
the complex preferences of participants and adopt the
institutional intervention and behavioral interventions.
Secondly, we can control free-riding behavior by
collective constraints. Take the industrial reputation as an
example, the industrial reputation has the vulnerability of
public goods. How can we manage this reputation
vulnerability? The most common practice is to
implement the punishment. There are two types of
punishment: one is exogenous punishment mechanism,
such as reporting; the other is some sorts of agreement
within the industry enterprises. Enterprises once violate
the agreement, it is necessary to apply the punishment.
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