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Behaviour change via monetary investments is a way to fighting climate change. Prior research has investigated the role of climate-change investments using a Collective-Risk-Social-Dilemma (CRSD) game, where players have to collectively reach a target by contributing to a climate fund; failing which they lose their investments with a probability. However, little is known on how variability in the availability of information about players' investments influences investment decisions in CRSD. In an experiment involving CRSD, 480-participants were randomly assigned to different conditions that differed in the availability of investment information among players. Half of the players possessed a higher starting endowment (rich) compared to other players (poor). Results revealed that investments against climate change were higher when investment information was available to all players compared to when this information was available only to a few players or to no one. Similarly, investments were higher among rich players compared to poor players when information was available among all players compared to when it was available only to a few players or to no one. Again, the average investment was significantly greater compared to the Nash investment when investment information was available to all players compared to when this information was available only to a few players or to no one. We highlight some implications of our laboratory experiment for human decision-making against climate change.
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Original Research
Collective Risk Social Dilemma: Role of
information availability in achieving cooperation
against climate change
Medha Kumar and Varun Dutt
Indian Institute of Technology Mandi, India
Behaviour change via monetary investments is a way to
fighting climate change. Prior research has investigated
the role of climate-change investments using a Collective-
Risk-Social-Dilemma (CRSD) game, where players have
to collectively reach a target by contributing to a climate
fund; failing which they lose their investments with a prob-
ability. However, little is known on how variability in the
availability of information about players’ investments in-
fluences investment decisions in CRSD. In an experiment
involving CRSD, 480 participants were randomly assigned
to different conditions that differed in the availability of
investment information among players. Half of the players
possessed a higher starting endowment (rich) compared
to other players (poor). Results revealed that investments
against climate change were higher when investment in-
formation was available to all players compared to when
this information was available only to a few players or to
no one. Similarly, investments were higher among rich
players compared to poor players when information was
available among all players compared to when it was avail-
able only to a few players or to no one. Again, the aver-
age investment was significantly greater compared to the
Nash investment when investment information was avail-
able to all players compared to when this information was
available only to a few players or to no one. We highlight
some implications of our laboratory experiment for human
decision-making against climate change.
Keywords: Collective Risk Social Dilemma, climate fund, informa-
tion availability, investments, Nash equilibrium
Climate change has been a topic of growing concern for
the entire world (Roberts, 2015). Earth’s average
surface temperature has already risen about 1.8 degrees
Fahrenheit (1.0 degree Celsius) since the late 19th century,
a change that is largely driven by increased Greenhouse
Gas (GHG) emissions into the atmosphere (IPCC, 2015).
Amidst increasing temperatures, real-world evidence shows
that people continue to show a waiting approach towards
climate change (Dutt & Gonzalez, 2012a; Dutt & Gonza-
lez, 2012b; Ricke & Caldeira, 2014).
Monetary investments against climate change, which are
one of the indicators of behaviour change, provide impor-
tant ways for our society to fight climate change (Webb,
2012). Climate negotiations are a way for deciding mon-
etary investments against climate change and they enable
us to reduce society’s impact on climate change (Doul-
ton & Brown, 2009; Sterman & Sweeney, 2007; Sterman,
2008). During negotiation process, there may be lower in-
vestments among negotiators. A likely reason for the lower
investments could be socio-political or geo-political moti-
vations (Barnett, 2007). For example, the United States
pulled out of the recent Paris Agreement and the Green
Climate Fund due to certain political motivations (Zhang,
Chao, Zheng, & Huang, 2017). However, another reason
for lower investments could be the information asymme-
tries present among negotiators. Due to information asym-
metries, some negotiators may possess untrue or impre-
cise information about investments of other negotiators;
whereas, some negotiators may possess accurate invest-
ment information.
An extreme form of information asymmetry may be
where it becomes difficult to obtain information on one’s
climate actions. For example, in the recent Paris agree-
ment, there was quite some debate over China’s stance
to not let international inspectors access their information
about carbon-dioxide emissions (Zhang et al., 2017). An
investigation of this extreme form of information asymme-
try, where information may be withheld and not known to
certain negotiators, is the primary focus of this paper.
Prior research has investigated climate negotiations
in the laboratory using a Collective-Risk-Social-Dilemma
(CRSD) game (Milinski, Sommerfeld, Krambeck, Reed, &
Marotzke, 2008; Tavoni, Dannenberg, Kallis, & Löschel,
2011). In CRSD, negotiating players are provided initial
endowments and they need to contribute money from their
endowments to reach a pre-defined collective goal over sev-
eral rounds of negotiations. If players fail to reach the col-
lective goal, then climate change could occur with a known
probability and negotiating players lose their leftover en-
dowments completely (Milinski et al., 2008).
Understanding negotiations in the CRSD game has been
an active area of research (Burton, May, & West, 2013;
Tavoni et al., 2011; Milinski, Röhl, & Marotzke, 2011).
However, existing literature involving CRSD has assumed
negotiating players to possess complete information about
investments made by opponents (i.e., no information asym-
metry was assumed to exist among players), which may
not be true in the real world. As discussed above, nations
may withhold information about their investments against
climate change in the real world (Zhang et al., 2017). Mo-
tivated by this observation, we investigate the influence of
such information asymmetries among players on decision-
making in the CRSD game in the laboratory.
Corresponding author: Medha Kumar, Indian Institute of Technology Mandi,
Mandi, India, e-mail: medha751@gmail.com
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Kumar & Dutt: Climate cooperation via monetary investments
Furthermore, in real world climate change negotiations,
it is likely that income inequalities may exist between nego-
tiators (UNO, 2018). For example, some negotiators may
belong to low-income nations and others may belong to
high-income nations (UNO, 2018). These income-level dif-
ferences may likely influence the decision-making during
negotiations (Burton et al., 2013; Milinski et al., 2011;
Dennig, Budolfson, Fleurbaey, Siebert, & Socolow, 2015).
Motivated from this literature, in this paper, we also inves-
tigate how income-level differences among players influence
their decision in CRSD.
In what follows, initially we discuss prior research involv-
ing the CRSD framework. Then, we discuss certain theo-
ries of decision-making that help motivate our hypotheses
concerning information asymmetries and income-level dif-
ferences. Next, we detail an experiment where we test our
hypotheses in the CRSD game. In the end, we detail our
results, discuss their theoretical underpinnings, and derive
implications of our findings for the real world.
Collective Risk Social Dilemma (CRSD)
Game
Prior research involving the Collective Risk Social
Dilemma (CRSD) game has tested the effects of probabil-
ity of climate change on investments made by negotiators
(Milinski et al., 2008; Tavoni et al., 2011). These stud-
ies have revealed that people invest more against climate
change when they are convinced that failure to invest will
cause grave financial losses (Milinski et al., 2008). Fur-
thermore, people invest more against climate change in
the CRSD game when probability of experiencing a cli-
mate catastrophe is high compared to low (Hagel, Milin-
ski, & Marotzke, 2017; Milinski et al., 2008). Studies have
investigated how individuals behave if a collective target
is missed under different risk situations. Results revealed
that the assessment of risk arising from missing a collective
target caused reduced contributions. However, risk reduc-
tion caused players to maximize their individual contribu-
tions (Hagel et al., 2017). Barrett and Dannenberg (2012)
showed that when players are provided with a dangerous
scenario of rise in global temperature in the CRSD game,
climate negotiations turned into a coordination game. Re-
search has also revealed that the presence of small groups
can help achieve collective goals under stringent conditions
(Santos, Vasconcelos, Santos, Neves, & Pacheco, 2012).
In addition, prior research has evaluated the effects of
inequalities in initial endowments and players’ pledges on
investments against climate change in the CRSD game
(Tavoni et al., 2011). Results showed that the initial en-
dowment inequality made it harder to succeed in the CRSD
game; however, players’ pledges increased success dramati-
cally (Tavoni et al., 2011). In this paper, we build upon this
literature to investigate the effects of information asymme-
tries and income-level differences among players in CRSD.
Thus, in some conditions in the CRSD game, all players
possess investment information about other players. How-
ever, in other conditions in the CRSD game, either none of
the players or only a subset of players possess investment
information about other players. In addition, we create
income-level differences between players by making some
players invest against climate change in the initial rounds
in CRSD (poor players), where other players do not in-
vest against climate change (rich players). We believe that
both information asymmetries and income-level differences
are likely to influence people’s investment decisions in the
CRSD game.
Theoretical underpinnings of
decision-making in CRSD
A number of theories in decision-making literature may
provide the theoretical underpinnings to understand the
resulting behaviour in CRSD in the presence of informa-
tion asymmetries (Gonzalez, Ben-Asher, Martin, & Dutt,
2015; Kumar & Dutt, 2015; Mitchell, 1995; Schultz, Nolan,
Cialdini, Goldstein, & Griskevicius, 2007; Voulevi & Van
Lange, 2012) and income-level differences (Burton et al.,
2013; Dennig et al., 2015; Kahneman & Tversky, 1979;
Milinski et al., 2011; Tversky & Kahneman, 1992). These
theories may be connected at the cognitive level; however,
they may also provide non-overlapping explanations about
the resulting behaviour.
The influence of information asymmetries on climate
change investments may be explained based upon certain
cognitive theories (Gonzalez et al., 2015; Kumar & Dutt,
2015). For example, on account of instance-based learn-
ing theory (IBLT; Gonzalez et al., 2015; Kumar & Dutt,
2015), a cognitive theory of decisions from experience, we
expect to find lower investments when information asym-
metries are present among negotiators compared to when
information asymmetries are absent. That is because, in
classical games like prisoner’s dilemma, cognitive mod-
els of decision-making based upon IBLT exhibit lower in-
vestments when information asymmetries are present com-
pared to when information asymmetries are absent (Gon-
zalez et al., 2015). Such models combine not only per-
sonal investments; but, also investments of other negoti-
ating partners (Gonzalez et al., 2015). When information
asymmetries are present, model players may not be able
to systematically combine their investments with those of
their opponents and they may be able to maximize only
their personal savings and not their public investments.
Furthermore, the influence of information asymmetries
on climate change investments may be explained based
upon theory of social norms (TSN; Schultz et al., 2007;
Voulevi & Van Lange, 2012). According to TSN, social
norms are a double-edged sword (Schultz et al., 2007;
Voulevi & Van Lange, 2012; Dutt, 2011): investments
could be higher or lower when players possess information
about investments of others in their group compared to
when they lack this information. For example, if oppo-
nents invest against climate change, then one expects this
investment information’s availability among players to in-
crease the overall investments of the group. However, if
opponents do not invest against climate change, then one
expects this investment information’s availability among
players to decrease the overall investments of the group.
That is because, according to TSN, people tend to fol-
low others while deciding their own actions (Schultz et al.,
2007; Voulevi & Van Lange, 2012).
The influence of information asymmetries on climate
change investments may also be explained based upon pic-
ture theory (Mitchell, 1995) and that people are conscious
about their public image (Fenigstein, Scheier, & Buss,
1975; Tajfel & Turner, 1979). According to picture the-
ory (Mitchell, 1995), visuals are believed to have a great
power to influence people’s decisions. Also, public image
of oneself may cause people to act differently compared to
their private self (Fenigstein et al., 1975). Overall, on ac-
count of the theories of cognition and social norms, and the
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Kumar & Dutt: Climate cooperation via monetary investments
picture theory, people are likely to become consistent in-
vestors when investment information about others is made
available to them. Thus, we expect
H1: Higher investments when information about invest-
ments of other players in a group is present compared to
when this information is absent.
Furthermore, certain theories may explain the influence
of income-level differences between rich and poor players on
decision-making during negotiations (Burton et al., 2013;
Milinski et al., 2011; Dennig et al., 2015). For example, us-
ing laboratory experiments, Milinski et al. (2011) showed
that rich players are willing to substitute for missing contri-
butions by the poor, provided the players collectively face
intermediate climate targets. Also, Dennig et al. (2015)
have demonstrated that poor people are more vulnerable to
climate change impacts compared to rich people. Further-
more, a number of economic theories (Kahnemann & Tver-
sky, 1979; Tversky & Kahnemann, 1992) and ethical theo-
ries (IPCC, 2015; Fleurbaey, 2008; Brown, 2013) may also
help explain the effects of income-inequality on people’s
decision-making during negotiations. Due to economic the-
ories on differences in reference levels of low and high in-
come negotiators (Kahnemann & Tversky, 1979; Tversky
& Kahnemann, 1992) as well as ethical theories of respon-
sibility and fairness (IPCC, 2015; Fleurbaey, 2008; Brown,
2013), one expects:
H2: Higher investments from high-income (rich) nego-
tiators compared to low-income (poor) negotiators in the
CRSD game.
In addition, when investment information is known to
all players, then we expect rich negotiators to contribute
more compared to poor negotiators on account of the phe-
nomena of reference dependence in prospect theory (Kah-
nemann & Tversky, 1979; Tversky & Kahnemann, 1992).
According to reference dependence (Kahnemann & Tver-
sky, 1979; Tversky & Kahnemann, 1992), in the presence
of investment information, those with higher reference lev-
els (or higher incomes) will likely invest more compared to
those with lower reference levels (or lower incomes). In the
presence of investment information, higher-income negotia-
tors may also contribute more compared to lower-income
negotiators due to a feeling of responsibility towards so-
ciety as well as a societal perception of fairness (Brown,
2013). Overall, we also expect:
H3: Higher investments from rich players compared to
poor players when information about investments of other
players in a group is present compared to when this infor-
mation is absent.
Finally, players possessing pro-environmental disposi-
tions have been shown to contribute more against climate
change (Burton et al., 2013). Pro-environmental disposi-
tions may measure people’s agreement or disagreement to
different statements about the environment. Overall, we
expect:
H4: Players with greater pro-environmental dispositions
to likely invest higher amounts against climate change com-
pared to players with smaller pro-environmental disposi-
tions.
In the next section, we detail an experiment involv-
ing CRSD where we evaluated different hypotheses stated
above.
Method
Participants
Students were recruited through an email advertise-
ment for a climate change study at the Indian Insti-
tute of Technology Mandi, India. There were 480 par-
ticipants (54 females; 426 males), who were divided
into 80 groups per condition with 6 participants per
group. Participants comprised of undergraduate and
graduate students in computer engineering, mechani-
cal engineering, electrical engineering, basic sciences,
and humanities and social sciences. Ages ranged from
18 to 30 years (M= 20 years; SD = 1.56 years). The
groups took 45-50 minutes to finish the study. Partic-
ipants were paid INR 30 (USD 0.5) as the base pay-
ment for participation. In addition, participants could
get a performance incentive based upon the units left
in their private account at the end of 13th round. The
performance incentive was calculated as 1 unit in the
private account = INR 0.5 in real money. On aver-
age across all conditions, participants earned 27 units
(INR 13) as payment.
Procedure
The experiment comprised of the following three se-
quential sections: Questionnaire; Instruction and De-
mographic Information; and Game Play. In the Ques-
tionnaire section, which preceded the Game Play sec-
tion, participants were given survey questionnaires
that tested their pro-environmental predisposition
(New Ecological Paradigm; Dunlap et al., 2000). In
the Instructions and Demographics section, partic-
ipants were asked to self-report their basic demo-
graphic information (like age, gender, and major) and
then asked to read instructions concerning the study.
The instructions were adapted from (Tavoni et al.,
2011), which formed the basis for our study. In the
Game Play section, participants were asked to play
the CRSD game within their group for 13 repeated
rounds.
Experimental Design
Four hundred and eighty participants were randomly
assigned to one of four between-subjects conditions
that differed in the amount of information possessed by
negotiating players (20 groups per condition): Info-all,
No-info, Info-rich, and Info-poor. In each condition,
a group of 6 randomly-matched players made mone-
tary investments in a climate fund to avert climate
change across 13 repeated rounds. All players in a
group started with an equal payoff of 52 units in their
private account. In each round, participants decided
an investment between 0, 2, and 4 units to put in a
climate fund with a goal of reaching 156 units by the
end of 13th round.
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Kumar & Dutt: Climate cooperation via monetary investments
Collective Risk Social Dilemma (CRSD) game
In CRSD, negotiating players are provided initial en-
dowments. Players need to contribute money from
their endowments to reach a pre-defined collective goal
over several rounds of negotiations. If players fail to
reach the collective goal, then climate change could
occur with a known probability and negotiating play-
ers may lose their leftover endowments completely. In
order to reach the collective target, players need to
make individual sacrifice, with benefits to all but no
guarantee that others will also contribute. From the
point of view of players, it seems tempting to con-
tribute less so as to save money to induce others to
contribute more. Hence, there is a dilemma and the
risk of failure (Milinski et al., 2008).
Figure 1 shows the investment screen used across
all conditions. As shown in Figure 1, the investment
screen displayed the current trial number, total en-
dowment left with the player, a timer, and different
investment options. The timer indicated the time left
for players to make their investment decisions. The
timer lasted for 30 seconds but the screen did not
switch after the timer expired until players made their
decisions. Players had to select one out of the three
options to indicate the amount they wanted to invest
into climate protection. Once players had selected the
amount, they pressed the NEXT button to proceed
to the next round. The first three rounds were auto-
mated, where the computer randomly made 3 players
to contribute 4 units (poor) and made the remaining
3 players contribute 0 units (rich). The description
about different conditions is presented in the next sec-
tion.
Information availability
In Info-all (No-info) condition, at the end of each
round, all players (none of the players) in the group got
feedback about other players’ individual investments
to the climate fund from the start of the game and
in the preceding round. In the Info-rich (Info-poor)
condition, at the end of each round, only the 3 rich
(poor) players got feedback about other players’ indi-
vidual investments to the climate fund from the start
of the game and in the preceding round. Figure 2 and
3 show the feedback screen presented to players in dif-
ferent conditions in the CRSD game. For example, as
shown in Figure 2, in Info-all condition, at the end of
a round, all players in the group got feedback about
other players’ individual investments to the climate
fund from the start of the game and in the preced-
ing round. Also, players were given information about
the total investment made by their group to the cli-
mate fund in the preceding round along with the total
cumulative investment made by their group since the
start of the game.
In the Info-rich condition, the rich players could see
the investments made by all other players (see Fig-
ure 2), but the poor players could not see the invest-
ments made by other players (see Figure 3). Similarly,
in the Info-poor condition, the poor players could see
the investments made by all other players (see Fig-
ure 2), but the rich players could not see the invest-
ments made by other players (see Figure 3). Across
all conditions, if the collective investment of a group
to the climate fund remained less than 156 units, then
the group failed to reach the collective goal and cli-
mate change occurred with a 50% chance. If climate
change occurred, then it made everyone lose their in-
comes that they had not invested in the climate fund
till the last round.
NEP-R questionnaire
Before performing in the CRSD game, partici-
pants were given the New Ecological Paradigm-
Revised (NEP-R) questionnaire that tested their pro-
environmental predisposition (Dunlap et al., 2000).
The NEP-R consists of 15 statements, which tests peo-
ple’s environmental pre-deposition on different issues.
Among the 15 statements, agreement on eight state-
ments reflect endorsement of the paradigm and agree-
ment of the remaining seven statements reflect the en-
dorsement of the popular world view. In addition to
NEP-R questionnaire, participants were given ques-
tions that tested their reasoning for making decisions.
For more information on these questions, please refer
to the supplementary material.
Nash Investment
Nash equilibrium is a term used in game theory to de-
scribe an equilibrium where each player’s strategy is
optimal given the strategies of all other players (Os-
borne & Rubinstein, 1994). Thus, Nash equilibrium
is a proposed solution of a non-cooperative game in-
volving two or more players in which each player is as-
sumed to know the equilibrium strategies of the other
players, and no player has anything to gain by chang-
ing only their own strategy (Osborne & Rubinstein,
1994). In the CRSD game, given 13 rounds, 6 players,
and a target of 156 units, a number of Nash equilib-
ria are possible as the contributions from players in a
group could be unequal – some may put 0s, some may
put 2s, while others may put 4s. However, a fair Nash
equilibrium in CRSD could be one that is symmet-
ric, i.e., where all players are assumed to contribute
equally and optimally to reach the target investment.
The symmetric Nash investment in the CRSD game
is assumed to be 2 units per player per round. That
is because, when each of the 6 players in a group con-
tributes 2 units per round across 13 rounds, the cumu-
lative investment results in 156 units.
Dependent Variables and Statistical Analyses
We used the average cumulative investments across
groups in different information conditions as one of
the dependent variables. For each group, the aver-
age cumulative investment after a certain round was
computed by averaging of the cumulative investments
made by all players in a group up to the chosen round.
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Kumar & Dutt: Climate cooperation via monetary investments
Figure 1. Investment screen across different information conditions in the CRSD game. The investment screen displayed the endowment
from which players had to invest between 0, 2 or 4 units into climate protection.
Figure 2. Feedback screen presented to all players in Info-all condition, rich players in Info-rich condition, and poor players in Info-poor
condition, respectively.
Figure 3. Feedback screen presented to all players in No-info condition, rich players in Info-poor condition, and poor players in Info-rich
condition, respectively.
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Kumar & Dutt: Climate cooperation via monetary investments
Figure 4. Success rates and average cumulative investments across different information conditions. Success rate and average cumulative
investment over 13 rounds by successful and failure groups in avoiding dangerous climate change. The blue section indicates success
rates of successful groups; whereas, the red section indicates success rates of failure groups. The numbers within each section indicates
average cumulative investments. Numbers after the “±” symbol indicate the standard deviation (the N/A value in Info-all condition is
because only one failure group existed in this condition).
For example, if the first, second, and third players in
a group contributed 0, 2, and 4 units, respectively, in
the first two rounds in CRSD, then after 2 rounds, the
cumulative investment of these players would be 0, 4
and 8 units, respectively. Thus, the average cumula-
tive investment would be 4 units [= (0 + 4 + 8) / 3).
If a group’s cumulative investment was greater than
or equal to 156 units at the end of the 13th round,
then the group was termed as successful; otherwise,
the group was termed as failure. Success rate was de-
fined as the proportion of groups out of all groups in
a condition where the groups’ cumulative investments
were greater than or equal to 156 units at the end of
the 13th round. For example, if there were 10 groups
out of a total of 20 groups in the Info-all condition
where the groups’ cumulative investments were greater
than or equal to 156 units at the end of the 13th round,
then the success rate would be 0.50 (= 10 / 20).
Results
In order to test our expectations regarding the in-
vestments across different conditions and rounds, we
performed one-way and mixed-factorial ANOVAs with
different dependent measures, and conditions and
rounds as the independent measures. We also com-
pared the average investment per player (found by av-
eraging the investments of all players) against Nash
investment per player. To test the expectations re-
garding rich and poor players, we performed one-way
ANOVAs with different dependent measures, and rich
and poor groups as the independent measure. Further-
more, we also performed correlation analyses where
we correlated NEP-R scores with cumulative invest-
ments. All statistical analyses were performed at an
alpha level of .05 and a power threshold of 0.8.
Success rates and average cumulative investments
across successful and failure groups
In order to test hypothesis H1, we performed a one-
way ANOVA to evaluate whether success rates were
influenced by the different information conditions. In-
formation availability had a significant effect on suc-
cess rates (F(3, 32) = 9.52, p< .05, ηp
2= .47). Fig-
ure 4 shows the success rates by successful and failure
groups in avoiding dangerous climate change.
Table 1. Post-hoc tests for success rates across different informa-
tion conditions.
Success rate in one condition versus the other con-
dition (Mean, Standard Deviation)
p
Info-all (0.95, 0.22) > No-info (0.20, 0.41) < .05
Info-all (0.95, 0.22) > Info-rich (0.40, 0.50) < .05
Info-all (0.95, 0.22) > Info-poor (0.25, 0.44) < .05
No-info (0.20, 0.41) Info-rich (0.40, 0.50) 0.18
No-info (0.20, 0.41) Info-poor (0.25, 0.44) 0.71
Info-rich (0.40, 0.50) Info-poor (0.25, 0.44) 0.32
Table 1 shows the post-hoc tests for comparing suc-
cess rates in different conditions. Post-hoc tests re-
vealed that success rates were significantly higher in
Info-all condition compared to No-info, Info-rich, and
Info-poor conditions. There was no significant differ-
ence in success rates between Info-rich and Info-poor
conditions and between Info-rich and No-info condi-
tions. Similarly, success rates were similar in No-info
and Info-poor conditions. As per our expectation in
H1, these results show that groups had higher success
rates when all players possessed investment informa-
tion about others’ investments compared to when ei-
ther this information was partially present with only
some players in the group or completely absent from all
players in the group. Success rates were similar when
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Kumar & Dutt: Climate cooperation via monetary investments
Figure 5. Average cumulative investments over rounds across different information conditions. Average cumulative investment across 13
rounds by successful groups (A) and failure groups (B) in avoiding dangerous climate change. The horizontal line shows the collective
goal of 156 units to be achieved by the end of 13th round in the task.
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Kumar & Dutt: Climate cooperation via monetary investments
Figure 6. Average cumulative investments by rich and poor players. Average cumulative investments were calculated across 10 rounds
(round 4th to round 13th).
the investment information was possessed by only the
rich or only the poor players.
Furthermore, we performed a one-way ANOVA to
check whether the average cumulative investments
were influenced by the different information condi-
tions. Figure 4 shows the average cumulative in-
vestments by successful and failure groups. Informa-
tion availability had a significant effect on the aver-
age cumulative investments for successful groups (F(3,
32) = 9.52, p< .05, ηp
2= .47); however, not for fail-
ure groups (F(3, 40) = 1.80, p= .16, ηp
2= .12). Ta-
ble 2 shows the post-hoc tests for average cumulative
investments among successful groups. Post-hoc tests
revealed that the average investment in Info-all con-
dition was significantly higher compared to Info-rich,
Info-poor, and No-info conditions. Furthermore, av-
erage cumulative investment in No-info condition was
similar to that in Info-rich and Info-poor conditions.
There was no significant difference in average cumula-
tive investments between Info-rich and Info-poor con-
ditions. As per our expectation in H1, these results
show that average cumulative investments were higher
when all players possessed information about others’
investments compared to when this information was
partially available to some players. Furthermore, av-
erage cumulative investments were similar when in-
vestment information was available to only the rich or
only the poor players.
Table 2. Post-hoc test for average cumulative investments for suc-
cessful groups across different information conditions.
Average cumulative investment in one condition
versus the other condition (Mean, Standard Devi-
ation)
p
Info-all (186.21, 19.10) > No-info (171.00, 8.72) < .05
Info-all (186.21, 19.10) > Info-rich (163.75, 9.59) < .05
Info-all (186.21, 19.10) > Info-poor (166.00, 6.16) < .05
No-info (171.00, 8.72) Info-rich (163.75, 9.59) 0.40
No-info (171.00, 8.72) Info-poor (166.00, 6.16) 0.55
Info-rich (163.75, 9.59) Info-poor (166.00, 6.16) 1.00
Average cumulative investments across rounds
among successful and failure groups
We wanted to investigate the average cumulative in-
vestments across rounds among successful and fail-
ure groups. We analysed the pattern of average cu-
mulative investments across rounds among successful
and failure groups using one-way repeated-measures
ANOVAs (see Figure 5A and 5B). Average cumulative
investments increased over rounds for both successful
groups (F(3, 12) = 1461.96, p< .05, ηp
2=.98) and
failure groups (F(3, 12) = 204.13, p< .05, ηp
2=.84).
Furthermore, we performed mixed-factorial ANOVAs
to evaluate whether the average cumulative invest-
ments across rounds among both successful and failure
groups were different in different information condi-
tions. ANOVA results revealed that the average cumu-
lative investments across rounds were indeed different
in different information conditions among both suc-
cessful groups (F(36, 384) = 6.97, p< .05, ηp
2=.40)
and failure groups (F(36, 480) = 2.15, p< .05,
ηp
2=.14). As seen in Fig 5(A), among successful
groups, although there was an overall increase in in-
vestments across all conditions, yet the rate of increase
was more in Info-all condition compared to all other
conditions. On average, participants reached the goal
in 10 rounds in Info-all condition compared to a higher
number of rounds in other conditions. Furthermore, as
seen in Fig 5(B), among failure groups, the rate of in-
crease of average cumulative investment was similar
in Info-all, No-info, and Info-rich conditions. How-
ever, average cumulative investments were lower in
Info-poor condition compared to that in other con-
ditions. Thus, in agreement with H1, the best case
for achieving the collective goal was when investment
information was present among all players. However,
when groups failed, then the worst case was when in-
vestment information was available to only the poor
players.
10.11588/jddm.2019.1.57360 JDDM | 2019 | Volume 5 | Article 2 | 8
Kumar & Dutt: Climate cooperation via monetary investments
Average cumulative investments among poor and
rich players
We expected rich players to invest more against cli-
mate change compared to poor players (H2). We anal-
ysed average cumulative investments between round
4th and round 13th by poor and rich players (see
Figure 6). In agreement with H2, average cumu-
lative investments for rich players were significantly
higher than those for poor players (58.4 > 51.2; F(1,
156) = 7.26, p< .05, ηp
2= .04).
Average cumulative investments among poor and
rich players across different information conditions
We expected information availability to influence the
investments of rich and poor players (H3). We per-
formed one-way ANOVAs to investigate whether infor-
mation availability influenced the average cumulative
investments among rich and poor players, respectively,
in different information conditions. Figure 7 shows
the average cumulative investments between 4th round
and 13th round by poor players (blue) and rich play-
ers (red) across different information conditions. Av-
erage cumulative investment was significantly higher
among rich players compared to poor players in Info-
all condition (79.20 > 68.50; F(1, 39) = 4.70, p< .05,
ηp
2= 0.11). However, average cumulative investment
for rich and poor players was similar in all other condi-
tions: No-info (49.20 47.20; F(1, 39) = 0.17, p= .68,
ηp
2= 0.00), Info-rich (57.70 49.90; F(1, 39) = 2.79,
p= .10, ηp
2= 0.07), and Info-poor (47.50 39.10;
F(1, 39) = 1.65, p= .21, ηp
2= 0.04). Thus, overall,
these results agree with our expectation H3 about rich
players contributing more compared to poor players
when information was available among all players.
Figure 7. Average cumulative investments by poor (blue) and rich
(red) players across different information conditions. Average cu-
mulative investment was calculated between the 4th round and the
13th round in the game.
Average cumulative investment and NEP-R across
different information conditions
We expected players’ pro-environmental attitudes to
influence their investments against climate change
(H4). We analysed players’ pro-environmental atti-
tudes by using the New Ecological Paradigm-Revised
(NEP-R) scale (Dunlap et al., 2000). Overall, in
agreement with H4, the NEP-R score was significantly
and positively correlated to cumulative investments
across 13 rounds (r(78) = .42, p< .001). Corre-
lations between NEP-R and cumulative investments
were not significant in Info-all condition (r(18) = .30,
p= .19); No-info condition (r(18) = .22, p= .36); and,
Info-rich condition (r(18) = .42, p= .06). However,
this correlation was significant for Info-poor condition
(r(18) = .50, p= .03).
Correlation between NEP-R and cumulative invest-
ments was positive and significant for both poor play-
ers (r(78) = .31, p= .01) and rich players (r(78) = .27,
p= .02). Overall, these results agree with our expec-
tation in H4.
Deviations of average investment per player from
Nash predictions
We analysed the deviations in players’ investments
from their Nash predictions between rounds 4 and
13. In the Info-all condition, the average investment
per player was significantly greater compared to the
symmetric Nash investment per player (2.35 > 2.00;
t(119) = 5.73, p< .05, r= .46). However, in other
conditions, the average investment per player was sig-
nificantly lower compared to the symmetric Nash pre-
diction: No-info (1.70 < 2.00; t(119) = 4.66, p< .05,
r= .39), Info-rich (1.84 < 2.00; t(119) = 2.31,
p< .05, r= .21) and Info-poor (1.57 < 2.00;
t(119) = 5.73, p< .05, r= .46). The average in-
vestment per player was significantly lower compared
to the symmetric Nash investment per player for both
rich players (1.95 < 2.00; t(239) = .91, p< .05,
r= .06) and poor players (1.70 < 2.00; t(239) = 5.14,
p< .05, r= .31).
Discussion and Conclusion
In today’s world, climate change is a pressing problem
and behaviour change is critically needed for fighting
climate change (Webb, 2012). Monetary investments
against climate change are important indicators of the
needed behaviour change (Doulton & Brown, 2009;
Sterman & Sweeney, 2007; Sterman, 2008). Our re-
sults revealed that possessing information about in-
vestments of other players produced higher invest-
ments against climate change and higher success rates
among successful groups (H1). Investments and suc-
cess rates were similar when the investment informa-
tion was possessed by only a subset of players (ei-
ther rich or either poor only). Also, the contributions
by rich players were more compared to poor play-
ers when investment information was present among
players (H2). Also, the NEP-R scores were positively
correlated with people’s investments against climate
change (H4).
A likely reason for higher investments when infor-
mation was present among all players is due to the
Theory of Social Norms (TSN; Schultz et al., 2007).
As per TSN, peer pressure plays a significant role in
10.11588/jddm.2019.1.57360 JDDM | 2019 | Volume 5 | Article 2 | 9
Kumar & Dutt: Climate cooperation via monetary investments
driving monetary investments towards climate change:
people are willing to contribute when they are able
to see others contribute. The influence of informa-
tion asymmetries on climate change investments may
also be explained based upon picture theory (Mitchell,
1995) and that people are conscious about their public
image (Fenigstein et al., 1975; Tajfel & Turner, 1979).
According to picture theory (Mitchell, 1995), visuals
are believed to have a great power to influence peo-
ple’s decisions. Thus, when people are able to visualize
the investment information about other players during
feedback, then this visualization causes them to invest
more against climate change. Also, public image of
oneself may cause people to act differently compared
to their private self (Fenigstein et al., 1975). In gen-
eral, players may not want to be portrayed publicly as
those contributing less as that is likely to hurt their
public image. Overall, players may tend to invest in
ways that reduce the possibility of hurting their public
image.
Still, another reason for higher investments in the
presence of information could be due to the learning
from investment outcomes of other players (Gonzalez
et al., 2015; Kumar & Dutt, 2015). As per instance-
based learning theory (IBLT), players maximize in-
vestments when they are able to combine their in-
vestment outcomes with investment outcomes of other
players (Gonzalez et al., 2015). Players are likely able
to activate investment instances in their memory when
they observe contributions of other players. When in-
formation is present among all players, the activation
of instances is relatively easy and this activation may
likely cause people to invest significantly higher in the
presence of information.
Interestingly, almost all groups were successful when
investment information was available to all players.
This result is in contrast to that found by Tavoni
et al. (2011) and Milinski et al. (2008) where only
20% and 10% of the groups were successful when in-
formation was present among all players. Although
we can only speculate about the reasons for the dif-
ferences, one likely reason for this difference could be
the fact that this study was run in India compared
to those of Tavoni et al. (2011) and Milinski et al.
(2008), where the latter studies were run in European
Union (EU) with a different population. Recent re-
search has shown that people in developing countries
(like India) perceive climate change a much greater
threat to themselves and to their families compared to
respondents in the developed countries (in EU; Lee et
al., 2015). Perhaps, the feeling of threat from climate
change made our participants contribute more against
climate change.
Furthermore, we found that the rich players’ invest-
ments were higher compared to the poor players’ in-
vestments. This result can be explained on the ba-
sis of reference-level dependence as part of prospect
theory (PT; Kahnemann & Tversky, 1992; Tversky &
Kahnemann, 1979). On account of PT, poor play-
ers’ smaller incomes likely pushed their reference-levels
lower compared to rich players’ reference-levels. A
higher reference-level of rich players compared to poor
players causes rich players to invest more compared to
the poor players. Another likely reason for rich players
to contribute more compared to poor players is due to
ethical theories of responsibility and fairness (IPCC,
2015; Fleurbaey, 2008; Brown, 2013). The higher
income-levels of rich players gives them a feeling of
responsibility towards reducing climate change. Also,
societal perception of rich players contributing more
portrays them to be fair (Fleurbaey, 2008; Brown,
2013).
In this paper, we used the Collective-Risk-Social-
Dilemma (CRSD) framework (Burton et al., 2013;
Dannenberg et al., 2015; Hagel et al., 2017; Jacquet
et al., 2013; Milinski, Hilbe, Semmann, Sommerfeld,
& Marotzke, 2016; Milinski et al., 2008; Tavoni et al.,
2011) in a laboratory setting and our results regard-
ing negotiations against climate change should be seen
with this limitation in mind. Our experimental design
in this preliminary study was canonical and the situ-
ation, where investment information may be withheld
from other players, may be less common in the real-
world. In real-world negotiations, information sharing
about investments may likely be present among ne-
gotiators; however, this information may not be true.
Thus, we plan to undertake future studies, where we
vary the level of truth of investment information while
people invest against climate change.
From our lab-based findings in this paper, our re-
sults are promising for negotiations against climate
change. Overall, investments are likely to be higher
when investment information is shared amongst all ne-
gotiating players. In the real-world, people are most
likely to possess investment information about their
opponents. In such situations, based upon our re-
sults, we expect investments against climate change
to be maximized. In addition, real-world negotia-
tions are likely to have negotiators from both nations
with higher and lower income levels. Based upon
our findings, again, the news is promising: We ex-
pect that in a mixed income-level environment, the
higher-income negotiators will contribute more com-
pared to the lower-income negotiators. In fact, the
higher-income negotiators are expected to be closer to
their optimal Nash investment levels. Also, we found
that pro-environmental attitudes were positively cor-
related with investments. Thus, for real-world negoti-
ations, investments are likely to be higher if negotia-
tors possess pro-environmental attitudes towards our
environment. Thus, choosing negotiators with pro-
environmental attitudes may be a key for success of
climate negotiations.
Overall, our results revealed that information asym-
metry is an important factor impacting investments
against climate change. However, there are several
other factors that are also likely to influence invest-
ments and negotiations against climate change. For
example, penalties for those contributing less are likely
to increase people’s investments. One way to increase
investments could be by making this activity damag-
ing to players, i.e., by giving players, who invest lit-
10.11588/jddm.2019.1.57360 JDDM | 2019 | Volume 5 | Article 2 | 10
Kumar & Dutt: Climate cooperation via monetary investments
tle, monetary penalties compared to those who do not
show this behaviour. However, another way to increase
investments could be by rewarding people’s contribu-
tory behaviours (i.e., rewarding those who do invest
more). Still, a third way could be to reward those who
do invest against climate change and penalize those
who do not invest against climate change. We plan
to undertake some of these ideas as part of our future
work involving CRSD.
Another factor that is likely to influence investments
against climate change is the presence or absence of in-
come disparity among players. In this paper, we did
not vary this factor as all players possessed income
disparity across all information conditions. However,
as part of our future research, we plan to systemati-
cally vary income disparity among different informa-
tion conditions to understand the interaction of these
factors.
In this paper, we adapted instructions from Tavoni
et al. (2011) and used them across all information
conditions. However, instructions provided to partici-
pants may influence their investment decisions in cer-
tain ways (Zizzo, 2010). Thus, as part of our future re-
search, we plan to frame instructions in different ways
to evaluate their influence on investments against cli-
mate change in conditions involving information asym-
metry. Some of these ideas form the immediate next
steps in our research program involving negotiations
against climate change.
Acknowledgements: This research is partially sup-
ported by Indian Institute of Technology Mandi and seed
grant (IITM/SG/VD/32) which provided necessary com-
putational and financial resources for this work.
Declaration of conflicting interests: The authors de-
clare that the research was conducted in the absence of
any commercial or financial relationships that could be
constructed as a potential conflict of interest.
Author contributions: Medha Kumar was the research
lead who designed the experiment and carried out data
collection for this work. Varun Dutt was the principal
investigator who served as a constant guiding light for
this work.
Supplementary material: Supplementary material
available online.
Copyright: This work is licensed under a Creative Com-
mons Attribution-NonCommercial-NoDerivatives 4.0 In-
ternational License.
Citation: Kumar, M. & Dutt, V. (2019). Col-
lective Risk Social Dilemma: Role of information
availability in achieving cooperation against climate
change. Journal of Dynamic Decision Making, 5, 2.
doi:10.11588/jddm.2019.1.57360
Received: 03 Dec 2018
Accepted: 03 May 2019
Published: 18 May 2019
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... Although the average surface temperature has been increasing and this increase poses a threat to mankind, people continue to show a waiting approach towards climate change [13], [14], [47]. This waiting approach has been prevalent in climate negotiations and a likely reason for this approach could be the dilemma that people face when they need to decide whether to keep their private wealth to themselves or to contribute some part of it for mitigating future climate change [30], [31], [38], [39], [43], [54]. ...
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Prior research has used reinforcement-learning models to investigate human decisions in choice games. However, research has not investigated how reinforcement-learning models Expectancy-Valence-Learning and Prospect-Valence-Learning would explain human decisions in applied judgment games where people face a collective-risk-social-dilemma against societal problems like climate change. In collective-risk-social-dilemma game, a group of players invested some part of their private incomes to a public fund over several rounds with the goal of collectively reaching a climate target, failing which climate change would occur with a certain probability making players lose their remaining incomes. In this paper, we propose Expectancy-Valence-Learning and Prospect-Valence-Learning models in the collective-risk-social-dilemma game and calibrate model parameters to aggregate and individual human decisions across four between-subjects information conditions, where half of the players in each condition possessed lesser wealth (poor) compared to the other half (rich). Results showed that model calibration to individual decisions provided a more accurate account compared to the calibration to aggregate decisions and the Expectancy-Valence-Learning model was better fit compared to Prospect-Valence-Learning model across most conditions. Both models outperformed symmetric Nash model across all conditions. Overall, moderate recency, loss-aversion, and exploration drove people's decisions. We present the implications of our model results for situations involving a collective-risk-social-dilemma.
... Although the average surface temperature has been increasing and this increase poses a threat to mankind, people continue to show a waiting approach towards climate change [2][3][4]. This waiting approach has been prevalent in climate negotiations and a likely reason for this approach could be the dilemma that people face when they need to decide whether to keep their private wealth to themselves or to contribute some part of it for mitigating climate change [5][6][7][8][9]. ...
... Prior research has used a collective risk social dilemma (CRSD) game to study climate negotiations in the laboratory [6][7]9]. In CRSD, a group of six-players are provided with initial private endowments that they can contribute for mitigating climate change across several rounds. ...
... Thus, after making decisions, each player knew other player's individual as well as cumulative investments in each round. Reference [6] extended this information limitation and tested how information availability influences investments in the CRSD game. Specifically, [6] presented two conditions to their participants, where the conditions differed in terms of information available about investments to different players (rich and poor). ...
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Prior research has used reinforcement-learning (RL) models like Expectancy-Valence-Learning (EVL) and Prospect-Valence-Learning (PVL) to investigate human decisions in choice games. However, currently little is known on how RL models would account for human decisions in applied judgment games where people face a collective risk social dilemma (CRSD) against societal problems like climate change. The primary objective of this research was to account for human decisions in a CRSD game for climate change via RL models like EVL and PVL. In CRSD game, a group of players invested some part of their private incomes to a public fund over several rounds with the goal of collectively reaching a climate target, failing which climate change would occur with a certain probability and players would lose their remaining incomes. In this paper, we propose EVL and PVL models in the CRSD game and calibrate model parameters to human decisions across two between-subjects information-feedback conditions (Info-all: N=120; No-Info: N=120), where half of the players in each condition possessed lesser wealth (poor) compared to the other half (rich). A symmetric Nash model was also run in both conditions as a benchmark. In Info-all condition, players possessed complete information on investments of other players after every round; whereas, in the No-info condition, players did not possess this information. Our results showed that for both rich and poor players, the EVL model performed better than the PVL model in No-info condition; however, the PVL model performed better than the EVL model in the Info condition. Both the EVL and PVL models outperformed the symmetric Nash model. Model parameters showed reliance on recency, reward-seeking, and exploitative behaviours. We highlight the implications of our model results for situations involving a collective risk social dilemma.
... The agents do not know the whereabouts of the tipping point. The uncertainty lies in personal knowledge (Tavoni et al. 2011;Barrett and Dannenberg 2012;Dannenberg et al. 2015;Hagel et al. 2016Hagel et al. , 2017Kumar and Dutt 2019). We adopt a deterministic threshold-based tipping point, unlike Milinski et al. (2008), where the environmental disaster would have happened with a certain probability if the agents had not reached the target. ...
... From Eqs. 4 to 6, it is clear that heterogeneity in agents' actions becomes inherently related to the variance in the lake pollution signal and the diversity in threshold beliefs. In other words, the inherent uncertainty in the environmental problem lies in the imperfect information the agents possess (Barrett and Dannenberg 2012;Dannenberg et al. 2015;Hagel et al. 2017;Kumar and Dutt 2019) and not in the consequences of surpassing the threshold, as in Milinski et al. (2008). Information coming from the environmental shocks (see next subsection) and channelled via networks plays a fundamental role in shaping the behavior of the agents. ...
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Increasingly visible climate change consequences challenge carbon-based economies worldwide. While expert knowledge on climate change percolates through political initiatives and public awareness, its translation into large-scale policy actions appears limited. Climate change consequences unequally target regions, countries and social classes, a vital issue for social cooperation. When facing an imminent ecological collapse , in which conditions can self-interested agents gain environmental awareness and settle on a sustainable path of actions when their knowledge of the imminent collapse is bounded? This cooperation emerges from the interaction between individuals and the interaction of various cognitive processes within individuals. This article develops an agent-based model for this emergence of cooperation enriched with the Agent Zero neurocognitive grounded cognitive architecture. We investigate when agents endowed with deliberative, affective and social modules can settle on actions that safeguard their environment through numerical simulations. Our results show that cooperation on sustainable actions is the strongest when the system is at the edge of collapse. Policy measures that increase the environment's resilience become internalized by the agents and undermine awareness of the ecological catastrophe. Depending on the cognitive channels activated, agent behaviors and reactions to specific interven-B Aymeric Vié tions significantly vary. Our analysis suggests that taking different cognitive channels, deliberative, affective, social, and others into account, significantly impact results. The complexity of agent cognition deserves more attention to assess parameter sensitivity in social simulation models.
... Thus, decisions made with other individuals in a group can affect an individual's decisions. Prior research suggests that the mere information that the majority of the group members are making pro-environmental decisions can motivate individuals to do the same (Kumar & Dutt, 2019). In contrast, it may also lead individuals to become free-riders since group decisions may allow people to hide among other individuals in the group. ...
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