We use the voluntary contribution mechanism to investigate whether smaller action sets lead to higher cooperation rates. We ?nd that this is the case for groups of four players.action set; voluntary contribution mechanism; prisoner?s dilemma

Lata Gangadharan

Nikos Nikiforakis

English

Research Papers in Economics

 
 
Working Paper Series
Department of Economics
 
 
 
  Does the Size of the Action Set Matter for 
Cooperation? 
 
 
 
 
 
 
 
 
 
Lata Gangadharan
Nikos Nikiforakis 
 
 
 
 
 
 
 
 
 
 
 
  May 2009  
 
 
 
 
  Research Paper Number 1072  
 
 
 
 
 
ISSN: 0819‐2642
ISBN: 978 0 7340 4036 7
 
 
 
Department of Economics 
The University of Melbourne 
Parkville VIC 3010 
www.economics.unimelb.edu.au 
 Does the Size of the Action Set Matter for Cooperation?
Lata Gangadharan and Nikos Nikiforakis￿
April 2008
Abstract
We use the voluntary contribution mechanism to investigate whether smaller action sets
lead to higher cooperation rates. We ￿nd that this is the case for groups of four players.
JEL Classi￿cation: C92, D70, H41
Keywords: action set, voluntary contribution mechanism, prisoner￿ s dilemma.
1 Introduction
The voluntary contribution mechanism (VCM) is often referred to as the "n-person Prisoner￿ s
Dilemma". The two games share similar payo⁄ properties and both have a unique Pareto-
ine¢ cient Nash equilibrium. However, despite this a¢ nity, actual behavior in the two games is
often di⁄erent: Sally (1995, p.62) writes that "there are numerous [prisoner￿ s dilemma] exper-
iments in which subjects cooperated very frequently and e⁄ectively". In contrast, cooperation
almost always unravels with repetition in VCM experiments (e.g. Zelmer, 2003).1
The most notable di⁄erence between the two games is the size of the action set. Players
in prisoners￿dilemmas (PDs) can select from two actions (cooperate, defect), while players in
the VCM usually have many more actions from which to choose. It is therefore possible that
the higher rates of cooperation in PD are (partly) due to the smaller size of the action set.
However, given that experiments tend to di⁄er on more than one dimension (e.g. group size,
relative payo⁄s) it is di¢ cult to draw robust conclusions from the existing data.
￿Department of Economics, The University of Melbourne, Victoria 3010, Australia, email:
latag@unimelb.edu.au, n.nikiforakis@unimelb.edu.au
1To the best of our knowledge, this di⁄erence has not been previously mentioned in the literature. Roth
(1988) writes: "Curiously, the experimental literatures concerned with the two problems seldom refer to each
other" (p.991).
1This paper investigates whether the size of the action set matters for cooperation. This
fundamental question has surprisingly been overlooked despite the large body of research ex-
amining factors that promote cooperative behaviour in social-dilemma experiments and inspite
of the observed di⁄erences in behaviour in PD and VCM. Our paper aims to ￿ll this gap in
the literature.
2 The Experiment
2.1 The Voluntary Contribution Mechanism
Individuals are randomly divided into groups of size G: Every participant is given an endow-
ment of 10 EMU (Experimental Monetary Units). Players decide simultaneously and without
communication how much of the endowment to contribute to a public account, ci, where
0 ￿ ci ￿ 10. The rest (10￿ci) remains in the player￿ s own account. In addition to the money
that player i keeps, i receives a ￿xed percentage of the group￿ s total contribution to the public
account, ￿, where 0 < ￿ < 1 < n￿. This implies that the earnings of player i are given by
￿i = 10 ￿ ci + ￿
n X
j=1
cj: (1)
The treatment variable is the size of the action set. In the (R)estricted treatments players
have two actions, ci￿f0;10g: In the (U)nrestricted treatments players have eleven actions,
ci￿f0;1;2;:::;10g: In essence, the former is a PD, while the latter is a standard VCM.
2.2 Procedures
The game is repeated 20 times without changing group composition. As PDs are typically
studied using groups of two individuals and VCMs using groups of four individuals we decided
to examine the impact of the action set under both group sizes. That is, G￿f2:4g: This provides
a robustness check for our ￿ndings.
In the treatments with G = 2, the return from the public account is ￿ = 0:7. In treatments
with G = 4; ￿ = 0:4: Note that our aim is to test the impact of the action set in two di⁄erent
cases and not to compare di⁄erences across group sizes. Hence, the experiment does not control
for the increase in the social bene￿t that results from the increase of the group size (see Isaac
et al., 1994). Table 1 summarizes the experimental design.
The number of repetitions, equation 1, the instructions, and the values of ￿ and G were
common knowledge amongst participants. The conversion rate used was 7 EMU= 1 Australian
2Dollar. Sessions lasted approximately one hour and average earnings were A$34.6 for group
size G = 2; and A$32.4 for G = 4. The experiment was conducted using z-Tree (Fischbacher,
2007).
3 Results
The fact that the choice variable is discrete in treatment U and binary in treatment R raises the
question of how to compare behavior across treatments. One way of doing this is by comparing
the number of times that subjects contributed zero (or ten) in each treatment. This comparison
is however not informative as there are many cases in U where 0 < ci < 10. As a result, the
number of observations where ci = 0 and ci = 10; is higher in treatment R for all G.
Another way to compare behavior is to transform the discrete variable to a binary one. We
de￿ne variable C
U;t
i which takes the value 1 if individual i in treatment U contributes more
than 5 EMU in period t and the value 0 otherwise. Variable C
R;t
i is constructed analogously
taking the value 1 if individual i in treatment R contributes 10 and the value 0 otherwise. This
de￿nition is simple and we believe it is the most appropriate for comparing behavior across
treatments. The idea is that an individual who contributes more than 5 EMU in treatment U
would be more likely to have contributed all of his endowment in treatment R than not.
Figures 1 and 2 plot the frequency of C
T;t
i ; T￿fU;Rg; over time in each treatment for
G = 4 and G = 2, respectively. For simplicity, we say that an action is "cooperative" when
C
T;t
i = 1. Figure 1 shows that, despite the lax de￿nition in treatment U, there are more
cooperative actions when the action set is restricted (p-value= :04; Fisher￿ s exact test). In
total, 24 percent of the actions in R can be classi￿ed as cooperative, in contrast to 12 percent
in U: The frequency of C
T;t
i declines over time in both treatments.
Figure 2 shows that the size of the action set does not a⁄ect behavior when G = 2. While
there seems to be some di⁄erence in the number of cooperative actions in the ￿rst 5 periods
this di⁄erence disappears over time (p-value= :67; Fisher￿ s exact test).2 In both treatments,
nearly half of the actions can be classi￿ed as cooperative for most of the experiment.3
Table 2 reports the results from probit regressions with group random e⁄ects. The re-
gression analysis allows us to control for time e⁄ects, individual characteristics and also the
decisions of other group members in the previous period. The dependent variable is C
T;t
i . As in-
2This is consistent with the ￿ndings in Huck et. al. (2002) who study two-person Stackelberg experiments
and action sets of size three and ￿fteen:
3For completeness we report that the average group contribution to the public account across periods was
9.45 in R and 8.04 in U when G = 4; and 10.37 in R and 10.53 in U when G = 2: These di⁄erences are not
signi￿cant accordin to a Mann-Whitney test (p-value> :3).
3dependent variables we include Average Contribution of Others in t-1￿ (
Pn
j=1;j6=i ct￿1
j )=(n￿1),
and the interaction of this variable with treatment dummy U, a gender dummy (Male), a
dummy for academic background in Economics (Economics), and period dummies.
The results show that when G = 4; individuals are more likely to take a cooperative action
in treatment R the higher the contribution of their peers is. However, this is not the case in
treatment U as indicated by the negative coe¢ cient of the interaction term. When G = 2; the
likelihood that an individual takes a cooperative action increases with the contribution of his
peers irrespective of the size of the action set. This likelihood is even greater in treatment R.
4 Discussion
The results from the experiment show that groups of four individuals cooperate more with a
restricted action set. No e⁄ect is observed for groups of two individuals. What could explain
these ￿ndings?
One explanation could be that individuals make errors. Restricting the available choices,
however, should help subjects in discovering their dominant strategy. Moreover, the "error"
of contributing the full endowment is more costly than the "error" of contributing a smaller
fraction of the endowment. Therefore, quantal-response equilibrium predicts that subjects in
our experiment should be less likely to err when the action set is restricted (see Palfrey and
Prisbrey, 1996). Hence, this explanation cannot account for our results.
Another explanation is that individuals are forward-looking and believe that their contri-
butions have a signaling value. Hence, by contributing they might encourage others to do the
same. Isaac et al. (1994) propose such a model in which each individual is assumed to have
a subjective probability function about the likelihood of his signal being successful.4 A well-
known fact is that many individuals are willing to contribute to the public account as long as
others do the same (Croson, 2007; Fischbacher et al., 2001). Conditional cooperators should be
more likely to respond to a signal when there are only two actions available (contribute fully,
don￿ t contribute), as they know that all conditional cooperators will take the same action if
they decide to reciprocate the signal. This is supported by the regression results for groups of
four players in Table 2. Consequently, forward-looking individuals will also be more willing to
signal when the action set is restricted.
The size of the action set is less important in the special case of two-player groups: player
i can signal his intention to cooperate simply by continuing to contribute a high fraction of
4Players consider their signal to be successful if their pro￿t in the following period exceeds their pro￿t when
everyone free rides.
4his endowment even after player j contributes little to the public account. That is, player j￿ s
response does not depend on her expectation about how other￿ s will react to the signal. In fact,
for two-player groups, the unrestricted action set permits players to send less costly signals
and makes responding less risky. This is supported by Figure 2 and the positive coe¢ cient of
the interaction term in Table 2.
Our results suggest that the size of the action set should not be ignored when analyzing
situations where signaling could occur: larger action sets could make collusion more di¢ cult,
but also lead to e¢ ciency losses in gift-exchange relations and social dilemmas.
Acknowledgements: We thank Marco Casari, Tim Cason, Jacob Goeree, Ste⁄en Huck,
Hans-Theo Normann, and Brian Wallace for comments.
References
[1] Croson, R., 2007. Theories of Commitment, Altruism and Reciprocity: Evidence From
Linear Public Goods Games, Economic Inquiry 45, 199-216.
[2] Fischbacher, U., 2007. z-Tree: Zurich Toolbox for Readymade Economic Experiments -
Experimenter￿ s Manual, Experimental Economics 10, 171-178.
[3] Fischbacher, U., Gaechter, S. and Fehr, E., 2001. Are People Conditionally Cooperative?
Evidence from a Public Goods Experiment, Economics Letters 71, 397-404.
[4] Huck, S., Mueller, W., and Normann, H.T., 2002. To Commit or Not to Commit: En-
dogenous Timing in Experimental Duopoly Markets, Games and Economic Behavior 38,
240￿ 264.
[5] Isaac, R.M., Walker, J.M. and Williams, A.W., 1994. Group size and the voluntary provision
of public goods: experimental evidence utilizing large groups. Journal of Public Economics
54, 1￿ 36.
[6] Palfrey, T., and Prisbrey, J., 1996. Altruism, Reputation, and Noise in Linear Public Goods
Experiments,", Journal of Public Economics 61, 409-27.
[7] Roth, A.E., 1988. Laboratory Experimentation in Economics: A Methodological Overview,
The Economic Journal 98, 974-1031.
5[8] Sally, D., 1995. Conversation and Cooperation in Social Dilemmas: A Meta-Analysis of
Experiments from 1958 to 1992, Rationality and Society 7, 58-92.
[9] Zelmer, J., 2003. Linear Public Goods Experiments: A Meta-Analysis, Experimental Eco-
nomics 3, 299-310.
6  7
 
 
 
Table 1 — Experimental Design 
Treatment
Action set 
size
Group size 
(G) 
MPCR 
(α)
# of groups
% of cases in 
which c i =0
% of cases in 
which c i =10
1 2 2 0.7 15 48.2 51.8
21 12 0 . 7 1 42 3 . 43 4 . 8
3 2 4 0.4 10 76.4 23.6
41 14 0 . 4 1 05 2 . 9 5 . 8
 
 
 
 
 
Table 2 — Determinants of 
, Tt
i C  
Dependent variable: 
, Tt
i C   G=2 G=4 
Average Contribution of Others in t-1 0.06***  0.01* 
 (0.02)  (0.01) 
U * Average Contribution of Others in t-1 0.08*** -0.03** 
 (0.03)  (0.01) 
Male 0.30**  -0.07 
 (0.14)  (0.10) 
Economics -0.27  -0.25*** 
 (0.17)  (0.10) 
Constant -0.22  -0.61** 
 (0.34)  (0.22) 
N  1102 1520 
Log likelihood  -458.53 -608.24 
Probit regressions with group random effects, Standard errors in parentheses, 
Regression includes period dummies, ** significant at 5%; *** significant at 1% 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   8
Figure 1 — Frequency of 
, Tt
i C  when G=4 
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
123456789 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0
Period
%
 
o
f
 
c
o
o
p
e
r
a
t
i
v
e
 
a
c
t
i
o
n
s
(R)estricted
(U)nrestricted
 
 
 
 
Figure 2 — Frequency of 
, Tt
i C  when G=2 
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
123456789 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0
Period
%
 
o
f
 
c
o
o
p
e
r
a
t
i
v
e
 
a
c
t
i
o
n
s
(R)estricted
(U)nrestricted
 
 

Does the Size of the Action Set Matter for Coorperation

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Does the Size of the Action Set Matter for Coorperation

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