Available via license: CC BY
Content may be subject to copyright.
TYPE Original Research
PUBLISHED 11 September 2024
DOI 10.3389/frbhe.2024.1456436
OPEN ACCESS
EDITED BY
Daniel Lee,
University of Delaware, United States
REVIEWED BY
Johannes Lohse,
University of Birmingham, United Kingdom
Mehdi Farsi,
Université de Neuchâtel, Switzerland
*CORRESPONDENCE
Sarah Van Driessche
sarah.van-driessche@univ-lorraine.fr
RECEIVED 28 June 2024
ACCEPTED 23 August 2024
PUBLISHED 11 September 2024
CITATION
Van Driessche S, Boun My K and Brunette M
(2024) Risk and ambiguity in a public good
game. Front. Behav. Econ. 3:1456436.
doi: 10.3389/frbhe.2024.1456436
COPYRIGHT
©2024 Van Driessche, Boun My and
Brunette. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
Risk and ambiguity in a public
good game
Sarah Van Driessche1*, Kene Boun My2and Marielle Brunette3
1BETA, University of Lorraine, CNRS, Nancy, France, 2BETA, University of Strasbourg, CNRS, Strasbourg,
France, 3BETA, University of Lorraine, University of Strasbourg, AgroParisTech, CNRS, INRAE, Climate
Economics Chair Paris, Nancy, France
Introduction: Ambiguity is part of most of the daily life decisions. It can aect the
way people deal with environmental threats, especially when they face a social
dilemma.
Method: We run an experiment where every group of four subjects is exposed
to a risk that may result in a loss for each member. Subjects must decide
on the allocation of their resources between mitigation strategies that allow
them to decrease the probability of a disaster occurring for the group, and
adaptation strategies that allow them to reduce the magnitude of that disaster
for themselves only. In a first treatment (called Risk), subjects perfectly know the
probability of occurrence of the event. We introduce ambiguity with regard to
that probability in a second treatment (called Ambiguity), and in a third treatment
(called Information Acquisition), subjects have the possibility to pay to obtain
information allowing them to eliminate ambiguity.
Results and discussion: The results show that the introduction of ambiguity has
no impact on average contributions compared to the Risk treatment. However,
individual decisions to mitigate or to adapt are aected by subjects’ attitude
toward risk and ambiguity. In more than half of the cases, subjects are willing
to pay to obtain information, which argues in favor of greater dissemination of
information.
KEYWORDS
climate change, experiment, mitigation, adaptation, social dilemma, risk, ambiguity
1 Introduction
The last report of the Intergovernmental Panel of experts on IPCC (2021) emphasizes
the urgent need to limit climate change. Societies must strengthen their mitigation efforts,
just as they need to become more resilient by adopting effective adaptation policies. Dealing
with climate change also comes with the uncertainties that surround this global issue which
make it even more challenging (Bramoullé and Treich, 2009;Boucher and Bramoullé, 2010;
Raihani and Aitken, 2011;Etner et al., 2020). Societal choices and actions implemented to
tackle climate change in the immediate future will determine the next state of the world.
The five Shared Socio-economic Pathways (SSP) scenarios of the IPCC are specifically
designed to describe different plausible evolutions of the future society. Based on diverse
socio-economic hypotheses and on different levels of greenhouse gas (GHG) emissions,
they represent uncertain future situations. In this sense, these scenarios constitute a
common basis to make decisions but are still ambiguous with regard to the likelihood of
each one of them.1Individuals are therefore not aware of the future climate conditions
even though they need to address climate change promptly, through the implementation
of mitigation and adaptation policies. This also raises the question of investments in more
and better information in order to allay the uncertainties related to climate change (Ingham
et al., 2007;Morath, 2010;Kuusela and Laiho, 2020).
1 We refer to ambiguity as unknown probabilities and use the following definition: “ambiguity is
uncertainty about probability, created by missing information that is relevant and could be known”
(Camerer and Weber, 1992, p. 330). This is actually what Knight (1921)called “uncertainty”.
Frontiers in Behavioral Economics 01 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
In this ambiguous context, one may wonder what type of
effort individuals are willing to make to cope with global warming.
We consider that individuals may undertake two types of actions:
mitigation or adaptation practices.2Mitigation policies aim at
curtailing the emissions of greenhouse gas in the atmosphere to
lower the probability of bad climate states occurring (e.g., use of
cleaner energy). Adaptation policies, however, serve to decrease the
vulnerability of a person to the adverse consequences of climate
change (e.g., installation of a drain system around one’s house).3,4
It becomes apparent that mitigation can be compared to a global
public good wherein each individual faces a cost to reduce GHG
emissions while everyone can benefit from this reduction regardless
of one’s own contribution (Hasson et al., 2010,2012). The trade-
off between mitigation and adaptation therefore translates into a
collective action problem. In addition to the conflicting interests
between what is individually rational and what is socially optimal,
risk and ambiguity preferences of people may also alter this choice.
It is likely that decisions in terms of mitigation and adaptation
change according to individual preferences.
In this paper, we propose to analyse how subjects deal with
the threat of a climate event in a risky and in an ambiguous
situation. For that purpose, we implement an experiment with
context-framed instructions with students. The experimental
design supposes a climatic risk that can entail a loss at the group-
level (Hasson et al., 2010,2012;Lefebvre and Van Driessche, 2022).
In a first step, subjects are members of a group and they have
to decide how much to invest in mitigation in order to reduce
the probability of occurrence of the disaster for the whole group,
and how much to allocate to adaptation in order to reduce the
severity of the loss for themselves only. In the first treatment (Risk),
subjects are in a risky situation. They know the probability of a
climate event occurring. In the second treatment (Ambiguity), we
introduce ambiguity with regard to the probability of occurrence of
the event, so that subjects do not know in which state of nature they
are. We also have a third treatment (Information Acquisition) in
which we question subjects about their willingness to pay to obtain
information, allowing them to go from an ambiguous context to a
risky one. In a second step, subjects take individual decisions, which
gives us a measure of their risk and ambiguity preferences.
Our paper is closely related to several other studies. Hasson
et al. (2010,2012) are the first to model the mitigation-
adaptation trade-off as a public good experiment. Nevertheless, our
experimental design differs from theirs in the sense that the authors
consider a one-shot public good game with discrete choice. That
is, subjects could choose to invest their whole endowment in either
2 There has been a vast debate in the literature on the kind of relationship
that exists between mitigation and adaptation strategies (i.e., substitutes or
complements) (see e.g., Kane and Shogren, 2000;Tol, 2005;Ingham et al.,
2013;Greenhill et al., 2018). However, it is now unequivocal that an optimal
climate policy should include a mix of both strategies (Parry, 2007).
3 Mitigation and adaptation refer to the concepts of Ehrlich and Becker
(1972), respectively self-protection which is a decrease in the probability of
a loss and self-insurance which is a reduction in the magnitude of a loss.
4 In this paper, we do not consider potential side eects of adaptation, such
as maladaptation (e.g., installing an air conditioning system and therefore
using more energy) (Scheraga and Grambsch, 1998).
mitigation or adaptation but not in both measures at the same time.
In a first paper, Hasson et al. (2010) study the impact of changing
the vulnerability of subjects (i.e., the size of the climate event). They
do not find any difference between the low-vulnerability and the
high-vulnerability treatments. They explain this result by the role of
trust (i.e., beliefs). In another paper, Hasson et al. (2012) compare a
deterministic model (the climate hazard occurs with certainty) and
a stochastic one (there is a risk of a climate event occurring). The
results indicate that there is no difference between the two models
in terms of mitigation and that the level of cooperation is rather
low. Lefebvre and Van Driessche (2022) experimentally examine
the effects of income inequality on the mitigation-adaptation trade-
off. They find out that group contributions are not affected by
the degree of inequality. McEvoy et al. (2022) look at the effect
of non-binding pledges when subjects only have the possibility
to mitigate, and when they can both mitigate and adapt. Their
results suggest that pledges increase mitigation contributions only
when both climate policies are available. Blanco et al. (2020) also
study social dilemmas but in a wider context than climate change.
Subjects are given three possibilities to face potential losses at the
group-level: public insurance (reduction in the group probability),
private insurance (reduction in the individual size of the loss), and
no insurance (increase in the individual payoff). They investigate
the impact of varying the size of the loss and find that investments
in public insurance decrease as this size decreases. Keser and
Montmarquette (2008) examine the behavior of group members
who can collectively contribute to reduce the probability of a
common loss. They show that introducing ambiguity has a negative
impact on the level of voluntary contributions.
There is, however, a growing body of literature which considers
adaptation as a global public good. Khan and Munira (2021)
argue that reframing adaptation as a global public good would
be beneficial for its funding. Adaptation has long been regarded
as a local or national good which has led to insufficient financial
backing. Yet, adaptation measures provide indirect benefits such
as maintaining world trade, reducing migratory flows, avoiding
pandemics, etc., which are likely to benefit everyone (Kartha et al.,
2006). Banda (2018) considers adaptation as a multi-level (i.e.,
domestic, transboundary, and global) public good. She explains
that, given this particularity, the unilateral effort of individual
nations will not be enough to ensure the provision of this good.
It will require international legal and governance frameworks as
well as a more consistent adaptation finance. Nevertheless, for this
experiment, we consider adaptation as a private action so as to
confront subjects with a social dilemma.
This paper is also related to another strand of the literature,
namely public good games with uncertainty. There is no clear
picture that emerges from that literature. Some experiments have
focused on introducing uncertainty about the marginal per capita
return (see e.g., Fisher et al., 1995;Levati et al., 2009;Fischbacher
et al., 2014;Bjök et al., 2016;Boulu-Reshef et al., 2017;Théroude
and Zylbersztejn, 2020), while others have looked at the effect
of introducing uncertainty about actually receiving the benefits
from the public good (see e.g., Dickinson, 1998;Gangadharan
and Nemes, 2009;Levati and Morone, 2013). Fisher et al. (1995),
Bjök et al. (2016), Boulu-Reshef et al. (2017), and Théroude and
Zylbersztejn (2020) find no effect of uncertainty on the level of
contributions. However, Gangadharan and Nemes (2009), Levati
Frontiers in Behavioral Economics 02 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
et al. (2009), and Fischbacher et al. (2014) show evidence of a
negative effect on cooperation. Levati and Morone (2013) state
that this negative impact is due to the payoff parameterization.
Other papers have looked at the effect of uncertainty in threshold
public good games. In this kind of games, the provision of the
public good must attain a certain level in order to avoid a collective
loss. Barrett and Dannenberg (2012,2014) introduce uncertainty
regarding the location of the threshold, that is, the threshold lies
within a known range of values. Dannenberg et al. (2015) consider
an ambiguous threshold. The results show that cooperation is
hindered by uncertainty and even more by ambiguity.
The role of risk and ambiguity preferences has also been
carefully studied in the economic literature.5Risk aversion has long
been recognized as a key determinant of individual behaviors (Pratt,
1964). Its effect on various decisions has been considered: insurance
(Mossin, 1968), self-insurance and self-protection (Ehrlich and
Becker, 1972), the level of effort (i.e., preventing activity) (Jullien
et al., 1999), timber harvesting (Brunette et al., 2017b), etc. In
particular, De Pinto et al. (2013) show that considering risk neutral
farmers whereas they are risk averse leads to miscalculate the
incentives required to induce participation to a mitigation policy,
like carbon sequestration programs. Truong and Trück (2016)
show that assuming risk neutrality rather than risk aversion results
in an unnecessary delay of investments in adaptation policies.
However, Lades et al. (2021) questioned the capacity of different
economic preferences (i.e., risk taking, patience, present bias,
altruism, negative and positive reciprocity, and trust) to predict
pro-environmental behaviors in everyday life. They found that
altruism is the only preference which is related to environmentally
friendly attitudes. This suggests that individuals do not consider or
do not perceive the inherent riskiness of environmental decisions.
They rather focus on the pro-social aspects of pro-environmental
behaviors.
Regarding ambiguity preferences, Ellsberg (1961) is the first
to identify this tendency to avoid ambiguous situations and to
prefer risky ones. As for risk preferences, ambiguity aversion
has been widely considered to characterize numerous individual
decisions: value of a statistical life (Treich, 2010), portfolio
choices (Gollier, 2011), insurance and self-protection (Alary
et al., 2013), etc. Especially, Berger et al. (2017) show how
ambiguity aversion influences the optimal level of mitigation.
They proposed an integrated assessment model that generates
quantitative estimates of the impact of ambiguity aversion on
optimal emissions reduction. Brunette et al. (2017a) analyse the
relevancy of considering adaptation efforts in an insurance contract
to lower the financial cost of the insurance premium when climate
change makes the probability of the natural event occurring
uncertain. They show that including adaptation efforts in the
insurance contract leads to an increase in the adaptation efforts of
risk-averse and ambiguity-averse agents. In the same vein, Brunette
et al. (2020), combining an elicitation method and survey questions,
show that risk aversion has a significant and negative impact on
the probability to adapt and on the intensity of adaptation, whereas
ambiguity aversion has no effect. Alpizar et al. (2011) study the role
5 There also exists an abundant literature on the link between risk and
ambiguity preferences (see e.g., Boun My et al., 2024).
of ambiguity aversion in the choice to adapt to climate change. By
means of a framed field experiment with coffee farmers in Costa
Rica, they find that ambiguity aversion fosters the adoption of
technologies for adaptation.
Preferences toward risk and ambiguity have been found to
explain various individual choices and, in particular, mitigation and
adaptation decisions. Our study aims at looking at the effects of
these preferences on the choice of one strategy or the other. To the
best of our knowledge, this is the first experiment that examines
the trade-off between mitigation and adaptation by considering
a risky context and an ambiguous one. Anticipating our results,
we find that: (i) there is no difference in average contributions
between the Risk and Ambiguity treatments; (ii) risk and ambiguity
preferences impact the decisions to mitigate or to adapt; (iii)
ambiguity preferences explain the willingness to pay to obtain
information.
The rest of the paper is organized as follows. In Section 2,
we present the experimental design. In Section 3, the results
are described. Section 4provides a discussion of the results and
concludes.
2 Method
2.1 Main game
The experiment consists of a repeated game played for ten
periods. Subjects are divided into groups of four. The groups
remain unchanged throughout all the periods of the game. At the
beginning of each period, subjects receive an endowment of 250
ECUs6(Experimental Currency Units) that allows them to recover
from a potential loss of 200 ECUs, and a climate budget of 25 tokens
which has to be entirely spent on the two strategies (i.e., mitigation
and adaptation). Following Hasson et al. (2010,2012), we do not
give subjects the opportunity to “do nothing,” that is, to ignore
threats from climate change and keep their tokens for themselves.
While the experiment would gain in external validity with that
third option, it would also prevent us from analyzing the real-life
trade-off between adaptation and mitigation in the management of
threats. Indeed, if most of the subjects preferred this third option,
there would be no trade-off to analyze and therefore we would be
unable to address our research question.
In each period, every group faces a risk of incurring a
climate-related event that can inflict a loss of 200 ECUs on each
member. Subjects must decide, at the same time and without
communicating with each other, on the allocation of their climate
budget between adaptation and mitigation strategies. Relying on
Alekseev et al. (2017), we use context-framed instructions in order
to ease the comprehension of the tasks for the subjects and avoid
confusion. Thus, rather than using abstract terms, we employ words
like “climate change,” “mitigation,” “greenhouse gas,” etc., which
are supposed to be more evocative (see the instructions in the
Supplementary material). At the beginning of the game, we ask
subjects to imagine that they are part of a small community which
is threatened by climate-related events (such as rising water leading
to floods, extreme heat waves, storms, etc.). We also give them
6 The conversion rate is currently 100 ECUs to 4e.
Frontiers in Behavioral Economics 03 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
concrete examples of the two strategies. We explain that mitigation
actions can consist in sorting and reducing waste or choosing
public transport rather than the car to move around. We illustrate
adaptation by replacing the windows with laminated glass in order
to face high winds. However, still in an attempt to facilitate the
understanding of the game, we explain that the occurrence of a
climate event depends on a random draw of a ball from an urn filled
with balls of two different colors.
The climate budget must be divided between mitigation and
adaptation. A token invested in mitigation reduces the probability
of the climate event occurring for all the group members. This
probability is represented by an urn filled with black balls (event)
and white balls (no event). Depending on the treatment (see below),
we use either one or all of the following mitigation urns:
•Urn A contains a total of 150 balls. It is initially comprised
of 145 black balls and five white balls, such that the initial
occurrence probability equals 96.7%.
•Urn B contains a total of 150 balls. It is initially comprised
of 125 black balls and 25 white balls, such that the initial
occurrence probability equals 83.3%.
•Urn C contains a total of 150 balls. It is initially comprised
of 105 black balls and 45 white balls, such that the initial
occurrence probability equals 70%.
Every token contributed to mitigation replaces a black ball with
a white ball in the urn, thus it reduces the probability by 0.67%.
Therefore, the probability decreases as group members allocate
their tokens to mitigation and is given by the following function:
P=Bu−P4
i=1xi
150
where xiis the number of tokens contributed to mitigation by
subject i,i∈ {1, 4}, and Buis the initial number of black balls in
the mitigation urn (u= A, B, C).
However, a token allocated to adaptation reduces the size of
the potential loss only for the individual who decides to adapt. The
reduction of the loss follows this linear function:
L(xi)=(1 −0.013(25 −xi))200
where (25 −xi) represents the number of tokens invested in
adaptation by subject i. In order to help subjects make their
decisions, a table displaying the amount of the loss for every token
invested in adaptation was included in the instructions (see the
Supplementary material).
While the marginal benefit of a token contributed to mitigation
does not depend on the urn, the marginal cost (i.e., giving up on
the possibility to reduce the size of the damage) does. The higher
the occurrence probability of the climate event, the higher the
marginal cost of a token invested in mitigation. A risk-neutral and
self-interested subject has two strategies to maximize their payoffs
which depend on the number of tokens the other group members
invest in mitigation. As long as there are strictly less than:
•69 tokens invested in mitigation in urn A,
•49 tokens invested in mitigation in urn B,
•29 tokens invested in mitigation in urn C,
the marginal benefit of a token allocated to mitigation is always
lower than the marginal cost. Therefore, the subject should not
invest in mitigation. When the mitigation fund exceeds the
aforementioned levels, the subject should invest all their tokens in
mitigation.
The random draw of a ball by the computer arises at the end
of each period. We acknowledge that it would make more sense in
terms of external validity to consider that the benefits of mitigation
occur in the long term (i.e., a few periods later). However, it would
mean considering a dynamic public good game which is outside the
scope of this paper.
2.2 Treatments
In this subsection, we present the three different treatments
implemented in this experiment: Risk (hereafter R), Ambiguity
(hereafter Amb), and Information Acquisition (hereafter IA).
2.2.1 Risk (R)
In the Risk treatment (R), subjects are in a risky situation. They
face the mitigation urn B. They know the initial composition of the
urn and are thus aware that if no token is contributed to mitigation,
the probability of an event occurring for the group is 83.3%, while
if the four group members invest their entire climate budget in
mitigation, the probability decreases down to 16.7%.7In order to
facilitate the subjects’ decision making, we give them access to two
sliders. The first one allows them to simulate their own level of
contribution to mitigation, while the second one represents the
total investment of the three other group members. According to
the position of the sliders, the resulting probability is displayed in a
pie chart (see Figure 1).
2.2.2 Ambiguity (Amb)
In the Ambiguity treatment (Amb), subjects are in an
ambiguous situation with regard to the probability of a climate
event occurring. Indeed, they face the three mitigation urns (A,
B, C) without knowing which one will be selected. They make
their allocation decisions considering the three different possible
states of nature, with urn A representing the most adverse state and
urn C corresponding to the most favorable one. They are aware
of the initial composition of each urn, so that they know that the
probability of:
•Urn A goes from 96.7% if no one contributes to mitigation,
to 30% if the four group members invest their 25 tokens in
mitigation.
•Urn B goes from 83.3% if no one contributes to mitigation,
to 16.7% if the four group members invest their 25 tokens in
mitigation.
7 There is still a climatic risk even if all members mitigate at the maximum
possible level (25 tokens). This reflects the fact that, as stated by the IPCC
sixth assessment report, climate hazards will multiply in the near term when
global warming reaches 1.5◦C.
Frontiers in Behavioral Economics 04 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
FIGURE 1
Resulting probability in risk (screenshot).
•Urn C goes from 70% if no one contributes to mitigation,
to 3.3% if the four group members invest their 25 tokens in
mitigation.
In order to facilitate the subjects’ decision making, they also
have access to the two sliders in order to simulate their own level
of contribution to mitigation and the total investment of the three
other group members. According to the position of the sliders, the
resulting probability for each mitigation urn is displayed in a pie
chart (see Figure 2).
Following Attanasi et al. (2014), we use a two-stage lottery to
determine the occurrence of a climate event. To that end, in each
period, an opaque big urn which contains one hundred mitigation
urns of three different compositions (urn A, urn B, and urn C) is
generated. Subjects do not know the proportion of urns A, B and
C in the big urn nor the mitigation urn which is randomly drawn
from the big opaque one. Therefore, they do not precisely know
the probability of a climate damage occurring in the Ambiguity
treatment.
2.2.3 Information acquisition (IA)
In the Information Acquisition treatment (IA), subjects are
initially confronting the three mitigation urns and they have the
possibility to buy information in order to know which of the three
urns they will actually face. To do so, they can use up to 50 ECUs
from their endowment of 250 ECUs.8Subjects are asked to indicate
the maximum price at which they are willing to buy information
about the selected urn.9This price should give us an approximation
8 This way, they still have at least 200 ECUs to cover the risk of a climate
damage.
9 They have to choose an amount of ECUs included in
{0, 5, 10, 15, ..., 45, 50}. If they indicate 0, it means that they do not which to
have access to information.
of the subjects’ Willingness To Pay (WTP) to eliminate ambiguity,
and thus to make their decision in a risky context rather than
in an ambiguous one. In every period, the computer randomly
generates a number for each group which determines the actual
price of information. If the price indicated by a subject is equal to
or higher than the one set by the computer, the subject learns the
selected mitigation urn and pays the computer’s price. Otherwise,
the subject does not get information and pays nothing.10
2.3 Gains and feedback
In each treatment, subjects make the same decision, that is,
they decide how many tokens they want to allocate to mitigation
and how many they want to invest in adaptation. In the Amb
and IA treatments, prior to this decision, subjects have to indicate
which urn they think they will face (A, B, C, or “I do not know”).
Then, in IA, subjects must reveal their willingness to pay to obtain
information. If they get information, they take their allocation
decision in a risky context, facing either urn A, B, or C. If they do
not receive information, they make their decision in an ambiguous
context (as in Amb). Regardless of the treatment condition, subjects
also have to declare how many tokens they think the three other
members will invest in mitigation. Following Gächter and Renner
(2010) and Blanco et al. (2010), we incentivize subjects’ beliefs. They
are rewarded according to the precision of their beliefs. They earn
25 additional ECUs if they correctly (±7 tokens) predict the total
investment of the three other members.
10 This procedure can be assimilated to a Becker et al. (1964)(Becker-
Degroot-Marschak, BDM) mechanism. However, instead of asking the
smallest amount of cash subjects are willing to accept in exchange of their
wager, they must state the highest price at which they are willing to buy
information.
Frontiers in Behavioral Economics 05 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
FIGURE 2
Resulting probabilities in Ambiguity (screenshot).
The gains of subjects, for each period, depend on whether
or not an event occurs. It is determined by a random draw
of a ball (black or white) from the mitigation urn B in R and
from the selected mitigation urn (i.e., either A, B, or C) in Amb
and IA. If there is a climate event, in R and Amb, subjects get
their endowment of 250 ECUs minus the amount of the loss
whose size depends on their investment in adaptation. In IA,
they get their endowment of 250 ECUs minus the amount of
the loss, minus the price of information if they had access to
it. If no climate damage occurs, in R and Amb, subjects get
their endowment of 250 ECUs, and in IA, they receive their
endowment of 250 ECUs minus the price of information if they
got it.
At the end of each period, subjects are informed of the drawn
urn in Amb and IA, the total investment of their group in
mitigation, the resulting probability, the occurrence of the climate
event, and their own payoffs.
2.4 Subjects’ preferences
After the ten periods of the main game, we use two
questionnaires to assess the environmental sensitivity of subjects.
The first one investigates subjects’ pro-environmental behaviors.
They must answer to the fifteen statements by indicating the
frequency (between 1 “never” and 5 “always”) with which
they adopt pro-environmental attitudes (see the instructions in
Supplementary material). The second one is the New Ecological
Paradigm (NEP) scale (Dunlap et al., 2000). It aims to assess the
ecological consciousness of subjects. They have to indicate (using
a Likert scale from 1 “strongly disagree” to 5 “strongly agree”)
whether or not they agree with the fifteen sentences regarding
limits to growth, anti-anthropocentrism, fragility of balance,
rejection of exemptionalism, and ecocrisis (see the instructions in
Supplementary material).
We also measure Social Value Orientation (SVO) of subjects
following Murphy et al. (2011).11 To do so, subjects are
randomly grouped in pairs and have to decide how they want
to allocate resources between themselves and the other person.
For the six different propositions, subjects must indicate which
distribution of resources they prefer (see the instructions in
Supplementary material). According to their answers to the six
propositions, it is possible to classify them into four categories:
altruist, prosocial, individualist, and competitive (we use them as
explanatory variables in the regressions, see below).
Finally, following Halevy (2007), we ask subjects for their
certainty equivalent of two lotteries: a known lottery and an
unknown lottery.12 This allows us to elicit subjects’ risk and
ambiguity attitude within a Klibanoff et al. (2005) (KMM)
framework. Before completing these two tasks, subjects have to
select their winning color (yellow or blue). In the first task,
they must choose between a lottery with known probabilities
(represented by an urn comprised of five yellow balls and five blue
balls, Lknown) and ten fixed amounts of money. More specifically,
they have to make a choice between the Left option which is
a lottery with two outcomes (0eand 10e) and 50% chance of
getting either 0eor 10e, and the Right option which provides
a safe amount of money, from 1ein the first proposition, 2ein
the second one, to 10ein the tenth one (see the instructions in
Supplementary material). For the ten propositions, subjects have to
indicate whether they prefer the Left or the Right option.13 Then,
we use the subjects’ switching point between the two options to
classify them as either risk averse, risk neutral or risk lover (we
use this classification in the regressions, see below). Similarly, in
the second task, subjects also have to choose between the Left and
11 This task is incentivized, see Subsection 2.5.
12 One of these two tasks is incentivized, see Subsection 2.5.
13 We impose monotonicity so that individuals can only have one switching
point.
Frontiers in Behavioral Economics 06 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
the Right option. The only difference is that they do not know the
composition of the urn. Therefore, they have to decide between a
lottery with two outcomes (0eand 10e) and unknown probabilities
(Lunknown), and a sure amount of money, from 1ein the first
proposition, 2ein the second one, to 10ein the tenth one (see the
instructions in Supplementary material). Relying on d’Albis et al.
(2020), we use their definition of value-ambiguity attitude in order
to classify subjects into three categories: value-ambiguity averse,
value-ambiguity neutral, and value-ambiguity lover (we use them
as explanatory variables in the regressions, see below). To do so,
we compare the subjects’ switching point for the known lottery
(Lknown) and the unknown lottery (Lunknown ).
2.5 Procedure
A total of 192 subjects participated in 12 sessions (four
sessions per treatment) in September, October and November
2021 in Strasbourg and in Nancy. Each subject participated in
one treatment only. Half of the subjects were recruited from
a list of experimental subjects maintained at the Laboratory of
Experimental Economics of Strasbourg (LEES) using the ORSEE
software (Greiner, 2015). We contacted the other half by mail
because we targeted students who were pursuing environmental
studies. Since those students are supposed to be more committed
to the environmental cause, we wanted to see whether they would
behave differently than students from other disciplines. Therefore,
three sessions were run with students from the National School for
Water and Environmental Engineering of Strasbourg (ENGEES)
and three others with students from AgroParisTech (APT) in the
campus of Nancy. Apart from the location, the conditions of the
experiment were the same for each session. Table 1 summarizes the
number of sessions and subjects per treatment for all subjects (All)
and for the two subcategories of subjects: subjects whose studies
are not specifically environment-related (hereafter called Classic)
and those who pursue environmental studies (hereafter called
Environment).14 The latter category corresponds to the students
from ENGEES and APT.
All subjects completed the experiment using tablet computers.
Each session followed an identical procedure. The instructions
were read aloud by the experimenter and, before starting, subjects
had to respond to a comprehension questionnaire in order to
check that they properly understood the rules. The experiment
could start only after all subjects had cleared the control questions.
After the ten periods of the main game, subjects completed the
questionnaire about their environmental habits and the NEP.
The last parts of the experiment consisted of the SVO and the
elicitation of risk and ambiguity preferences. Finally, subjects
answered a post-experiment questionnaire (see the instructions in
Supplementary material).
At the end of the experiment, one period from the main game
was randomly selected for actual payment. For the SVO, a random
draw was made to determine which of the six propositions would
actually be paid out. Either the risk elicitation task or the ambiguity
14 We provide a brief comparison of the two subcategories of subjects in
the first section of the Supplementary material.
elicitation task was rewarded and it also depended on a random
draw. Independently of the task selected at random, the computer
selected the proposition which would be compensated. If, for that
proposition, a subject chose the Left option, then the computer
randomly drew a ball from the urn. If the ball was the same color
as the subject’s winning color, the subject got 10e. Otherwise, the
subject got 0e. However, if a subject chose the Right option, the
subject got the amount corresponding to the proposition selected
by the computer. The conversion rate was 100 ECUs to 4efor the
main game and the SVO. Subjects were paid their earnings privately
at the end of the session. A session lasted 100 min on average and
the average earnings were 20.85e(SD = 4.77). The next section
presents the results.
3 Results
Recall that subjects have a climate budget of 25 tokens to fully
allocate between mitigation and adaptation. We choose to consider,
as our variable of interest, the amount of tokens contributed to
the mitigation measure. However, the opposite results are true for
adaptation. We proceed in two steps to analyse the results. First,
we focus on average contributions to mitigation, then we study the
individual decisions to mitigate or to adapt and we run a series of
regressions.
3.1 Mean contributions
3.1.1 Comparisons of R and Amb, and of Classic
and Environment subjects
In the second column of Table 2, we present the average
contributions to mitigation per treatment for all subjects (All) and
for the two subcategories of subjects (Classic and Environment).
In the Risk treatment, the average contribution amounts to 17.28
tokens which represents 69.12% of the climate budget. This figure
is relatively high compared to the initial average contribution
of 40%–60% usually observed in traditional public good games
(Villeval, 2012). Moreover, we do not see a decline in average
contributions over time for the two subcategories of subjects, as
illustrated in the top-left corner of Figure 3. Unlike traditional
public good games, recall that zero contribution is not a dominant
strategy in R. Subjects may have an interest in investing all
their tokens in mitigation, depending on the number of tokens
in the mitigation fund. This can explain why subjects maintain
cooperation throughout the periods of the game, and thus why our
results depart from what is generally seen in public good games.
The same observation is true in Amb. Subjects invest on average
16.49 tokens in mitigation, that is, 65.96% of their climate budget.
The top-right corner of Figure 3 also shows no decrease in mean
contributions over time for the two subcategories of subjects.
In order to assess the effect of introducing ambiguity in the
game, we now compare the Risk and Ambiguity treatments.15 For
the whole sample of subjects, in R, average contributions equal
15 We will not compare IA with any other treatment on the basis of average
contributions. Indeed, in IA, subjects can either be in a risky situation or in an
ambiguous one when making their decisions.
Frontiers in Behavioral Economics 07 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
TABLE 1 Number of sessions and subjects per treatment.
Treat.
Categ. All Classic Environment
Sessions Subjects Sessions Subjects Sessions Subjects
R 4 64 2 32 2 32
Amb 4 64 2 32 2 32
IA 4 64 2 32 2 32
Total 12 192 6 96 6 96
17.28 tokens and in Amb, subjects invest on average 16.49 tokens.
However, this difference is not significant according to a two-sided
Mann-Whitney (MW) ranksum test taking group averages as units
of observation (p= 0.4177).16 The same holds true for Classic
subjects. There is no significant difference in average contributions
between R (17.34) and Amb (16.28; p= 0.5632). For Environment
subjects, mean contributions in R (17.21) and in Amb (16.70) are
not statistically different either (p= 0.5992). In the public good
games’ literature, a series of papers that attempts to assess the
effect of uncertainty about the marginal per capita return also finds
zero effect on contributions (see e.g., Fisher et al., 1995;Boulu-
Reshef et al., 2017;Théroude and Zylbersztejn, 2020). It should be
noted that looking at average contributions does not allow us to
identify the effect of individual preferences. This will be studied in
subsection 3.2 when looking at individual decisions. It ensues from
the above the following result.
RESULT 1. Introducing ambiguity with respect to the probability
of a climate-related event occurring does not affect average
contributions to mitigation.
Then, we compare Classic and Environment subjects. We only
find a marginally significant difference in mean contributions in
the Information Acquisition treatment (p= 0.0929). The results
suggest that Classic subjects invest less on average (13.31) than
Environment ones (17.40). There is no significant difference for the
other treatments.17
In the last two columns of Table 2, we also report the
percentages of time subjects do not invest in mitigation (0 token
contributed) and the percentages of time they contribute their
entire climate budget (25 tokens) per treatment and for the different
categories of subjects. If we consider the Risk and Ambiguity
treatments, we see that, for the whole sample of subjects (All), the
percentages of zero contribution are around 4% (respectively 4.22
and 4.06%, p= 64.17).18 Regarding the proportions of maximum
contributions, subjects invest all their tokens 27.03% of time in
R and 22.65% of time in Amb. The difference is not statistically
16 In this paragraph and the next one, unless specifically noted, we report
the significance levels of a two-sided MW ranksum test taking group averages
as units of observation.
17 Classic vs. Environment in R (p= 0.6742), in Amb (p= 0.7527).
18 In this paragraph and the next one, whenever we consider minimum
contributions, we report the significance levels of a two-sided MW ranksum
test taking the number of times 0 token is invested in mitigation by individuals
as units of observation.
TABLE 2 Mean (in tokens), minimum and maximum (in %) contributions
to mitigation (SD in parentheses).
Treatment Mean
contrib.
(in tokens)
% of no
Contrib.
% of full
Contrib.
All
R 17.28 (7.18) 4.22% (20.12) 27.03% (44.45)
Amb 16.49 (7.07) 4.06% (19.76) 22.66% (41.89)
IA 15.35 (8.98) 11.09% (31.43) 29.84% (45.79)
Classic
R 17.34 (7.38) 4.38% (20.49) 29.69% (45.76)
Amb 16.28 (7.24) 5.63% (23.08) 23.75% (42.62)
IA 13.31 (9.38) 15.63% (36.37) 24.69% (43.19)
Environment
R 17.21 (6.99) 4.06% (19.77) 24.38% (43.00)
Amb 16.70 (6.91) 2.5% (15.64) 21.56% (41.19)
IA 17.40 (8.06) 6.56% (24.80) 35% (47.77)
significant (p = 0.6056).19 For Classic subjects, the proportion of
minimum contributions in R (4.38%) is not statistically different
from this proportion in Amb (5.63%; p= 0.9764). The same holds
true for the percentages of full contributions, there is no difference
between R (29.69%) and Amb (23.75%; p= 0.6842). If we now look
at Environment subjects, there is no difference in the percentages of
zero contribution between R (4.06%) and Amb (2.5%; p= 0.4492),
nor is there a difference in the proportions of full contributions
between R (24.38%) and Amb (21.56%; p= 0.7539). On the basis
of the above, we formulate the next result.
RESULT 2. Introducing ambiguity has no effect on the
proportions of minimum and maximum contributions.
Between the two subcategories of subjects, the only difference
lies in the proportions of minimum contributions in IA. Classic
subjects tend to contribute 0 token more often (15.63%) than
Environment ones (6.56%; p= 0.0119). This may provide an
explanation as to why average contributions seem to be lower for
19 In this paragraph and the next one, whenever we consider maximum
contributions, we report the significance levels of a two-sided MW ranksum
test taking the number of times 25 tokens are invested in mitigation by
individuals as units of observation.
Frontiers in Behavioral Economics 08 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
FIGURE 3
Mean contributions to mitigation over period per treatment.
Classic subjects than for Environment ones in IA. The bottom-left
corner of Figure 3 which represents the average contributions to
mitigation per period for the two subcategories of subjects, shows
that average contributions of Classic subjects tend to decrease over
time, especially in the last periods of the game. Data analysis shows
that the number of free-riders (who invest 0 token in mitigation)
doubled in the last three periods of the game.20
3.1.2 Information acquisition treatment
Remember that, in the IA treatment, subjects have the
possibility to buy information in order to know which urn they
will face. The average contribution, in this treatment, amounts
to 15.35 tokens (see Table 2). For the analysis, we distinguish
between subjects who wish to obtain information about the selected
urn (i.e., WTP >0) and those who do not (i.e., WTP =0)
(respectively wish yes/wish no). We also identify subjects who
actually get information (i.e., WTP ≥price) and those who do
not (i.e., WTP <price) (respectively info yes/info no). The first
panel of Table 3 summarizes the number of times subjects wish
to receive information and the number of times they get it, as
well as their willingness to pay to eliminate ambiguity. In 52.97%
of the cases, subjects wish to obtain information. In particular,
Classic subjects wish to get information in 56.25% of the cases
and Environment subjects wish to obtain it in 49.69% of the cases.
The difference between Classic and Environment subjects is not
statistically significant according to a MW ranksum test taking
the number of times individuals wish to obtain information as
units of observation (p= 0.4590). Subjects actually get information
20 In the second section of the Supplementary material, we present the
evolution of the beliefs over periods in the IA treatment.
in 21.88% of the cases: 17.5% for Classic subjects and 26.25%
for Environment subjects. The difference between Classic and
Environment subjects is not statistically significant according to a
MW ranksum test taking the number of times individuals receive
information as units of observation (p= 0.4379).
Regarding the WTP of subjects, Classic subjects offer on average
9.64 ECUs (SD = 10.98) to obtain information and Environment
subjects offer 11.78 ECUs (SD = 13.92).21 However, the difference is
not statistically significant according to a MW test taking individual
averages as units of observation (p= 0.8759). We can see from
Figure 4 which represents the distributions of the WTP for the
two categories of subjects, that the mode of both distributions is 0
(43.75% for Classic subjects and 50.31% for Environment subjects).
A Kolmogorov–Smirnov test taking individual averages as units of
observation indicates that the two distributions are not statistically
different (p= 0.968). If we only consider strictly positive offers, the
average WTP of Classic subjects equals 17.14 ECUs (34.28% of the
50 ECUs) and it amounts to 23.71 ECUs (47.42% of the 50 ECUs)
for Environment subjects. On the basis of the above, we present the
next result.
RESULT 3. Classic and Environment subjects do not behave
differently in IA.
In the second panel of Table 3, we look at the average number of
tokens subjects contribute to mitigation when they get information
about the selected urn. Subjects invest the least in mitigation
(14.68) when they face urn A (unfavorable state). Classic subjects
contribute 13.83 tokens on average and Environment subjects
21 A two-tailed t-test indicates that those values are statistically dierent
from 0 for both Classic subjects (p= 0) and Environment subjects (p= 0).
Frontiers in Behavioral Economics 09 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
TABLE 3 WTP (in ECUs) and mean contributions (in tokens) according to the urn (SD in parentheses).
All Classic Environment
Obs. WTP (in
ECUs)
Obs. WTP (in
ECUs)
Obs. WTP (in ECUs)
Wish No 301 0 140 0 161 0
Yes 339 20.22 (12.53) 180 17.14 (10.87) 159 23.71 (13.39)
Yes Info Yes 140 28.46 (14.39) 56 24.20 (14.29) 84 31.31 (13.82)
Yes No 199 14.42 (6.40) 124 13.95 (6.90) 75 15.20 (5.42)
Mean contrib. Mean contrib. Mean contrib.
Obs. (in tokens) Obs. (in tokens) Obs. (in tokens)
Info Yes
Urn
A 28 14.68 (9.94) 12 13.83 (10.83) 16 15.31 (9.53)
Yes B 69 18.35 (6.57) 28 16.96 (8.40) 41 19.29 (4.85)
Yes C 43 20.23 (7.10) 16 17.69 (8.62) 27 21.74 (5.68)
invest 15.31 tokens. Mean contributions are higher when subjects
are aware that urn B is drawn (18.35). Classic subjects invest an
average of 16.96 tokens and Environment subjects contribute 19.29
tokens on average. When urn C is selected (favorable state), mean
contributions are the highest (20.23). Classic subjects invest 17.69
tokens on average and the mean contribution of Environment
subjects is 21.74 tokens. It seems that subjects use the information
they receive and that they mitigate more when they are in the most
favorable state (urn C).
3.2 Individual decisions
We now turn to the analysis of individual decisions in order
to look at the determinants of the choice to mitigate or to adapt
in the different treatments. We estimate tobit models with random
effects since the dependent variable (the number of tokens invested
in mitigation) is left-censored at 0 and right-censored at 25. Table 4
presents the different variables that are used in the regressions along
with some descriptive statistics and the results are presented in
Tables 5,6.
Specification (1) of Table 5 focuses on the Risk treatment. In
this treatment, the subjects’ preferences toward risk and ambiguity
play a role in the decision to mitigate or to adapt, as evidenced
by the coefficients of Risk averse and V-ambiguity averse22 which
are statistically significant and negative. For risk averse subjects
facing urn B, zero contribution can become a dominant strategy
depending on their degree of risk aversion if we consider the
Constant Relative Risk Aversion utility function. Indeed, risk
averse subjects need a higher level of investment in mitigation
than risk neutral subjects (whose level is 49) in order to have
an interest in investing all their tokens in mitigation. This can
explain why risk averse subjects invest less than risk neutral
subjects. Value-ambiguity aversion also has a negative effect on
mitigation investments, even though there is no ambiguity per se in
this treatment. However, there is strategic ambiguity which refers
to situations where the behaviors of others cannot be precisely
predicted (Eddai and Guerdjikova, 2023). Gangadharan and
Nemes (2009) studied the effects of strategic and environmental
uncertainty on the provision of public and private goods. The
authors found evidence of aversion from strategic uncertainty, that
is, even when subjects know that either the probability of return
from the private good is low or the probability of return from the
public good is high, they prefer to invest their tokens in the private
good. In our case, strategic uncertainty matters a great deal since
the strategy of a particular subject depends on what the others
will do. This may explain why value-ambiguity averse subjects
prefer to adapt more than value-ambiguity neutral subjects. Among
other results, we see that the coefficient of Contributions belief
is positive and significant. In a similar experiment, Lefebvre and
Van Driessche (2022) found the same result. This is a well-known
result in public good games. Fischbacher and Gächter (2010)
explained this finding by the fact that individuals are willing
to cooperate in order to generate high beliefs and therefore to
ensure high contributions. The total number of losses incurred by
22 For both types of preferences, the baseline is neutrality.
Frontiers in Behavioral Economics 10 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
FIGURE 4
Distributions of WTP (in ECUs) for Classic and Environment subjects.
individuals negatively affects the mitigation level. It means that the
more losses people incur, the less they invest in mitigation, and
thus the more they adapt. Lefebvre and Van Driessche (2022) and
Blanco et al. (2020) also found a negative effect of the number of
losses. We also see from specification (1) that there is no decline
in contributions over time, as shown by the coefficient of Period
which is not statistically different from zero. This corroborates
what we already observed in the top-left corner of Figure 3. The
coefficient of Environment subject is not statistically significant
which confirms the non parametric result obtained in the previous
subsections and indicates that there is no difference in behavior
between Classic subjects and Environment ones. Based on the
above, we formulate the next result.
RESULT 4. In the Risk treatment, risk and value-ambiguity
averse subjects mitigate less than (respectively) risk and value-
ambiguity neutral subjects.
In specification (2) of Table 5, we look at the Ambiguity
treatment. What is interesting is that value-ambiguity averse
subjects invest less in mitigation than value-ambiguity neutral
subjects, as evidenced by the negative and significant coefficient
of V-ambiguity averse. In this treatment, subjects do not know
which urn they will face so that the probability of a climate damage
occurring is ambiguous. Ambiguity averse agents are expected to
put more weight than ambiguity neutral agents on unfavorable
priors (Alary et al., 2013). It is thus reasonable to believe that
value-ambiguity averse subjects have less incentives to invest in
mitigation since they think that urn A is more likely to be drawn.
Still from specification (2) of Table 5, we notice that the beliefs
about the contributions of the other members and the number of
losses incurred have the same effect as in specification (1). However,
in this treatment, subjects also rely on the occurrence of a loss in
the previous period. It negatively impacts the level of contributions
to mitigation. Keser and Montmarquette (2008) showed that zero
contribution to reducing the probability of a common loss is more
likely to happen after experiencing a loss. The subjects’ beliefs about
the drawn urn play a role in the decision to mitigate or to adapt.
Indeed, we see that subjects who believe that Urn B23 will be drawn,
those who believe that urn C (favorable state) will be drawn, and
those who do not know which urn will be drawn invest more
than subjects who believe that Urn A (unfavorable state) will be
selected. Thus, if they think they will be in the most adverse state
(greater chances of damages), subjects mitigate less than in any
other situations, and thus adapt more. On the basis of the above,
we present the next result.
RESULT 5. When there is ambiguity with regard to the
probability of a climate event occurring, value-ambiguity averse
subjects invest less in mitigation than value-ambiguity neutral
subjects.
Finally, in specification (3) of Table 5, we focus on the
Information Acquisition treatment where subjects can pay in order
to know the drawn urn. Regarding risk and ambiguity preferences,
we notice that they do not affect the level of mitigation. In Table 6,
we investigate whether those preferences influence the WTP to
obtain information. Still from specification (3), we see that, as
in specifications (1) and (2), the subjects’ beliefs about the total
contribution of the other group members positively affect the level
of mitigation. However, the number of losses incurred and the
occurrence of a loss in the previous period no longer impact the
23 The dummy Urn A belief is not included in regression (2) nor (3) since it
is the baseline.
Frontiers in Behavioral Economics 11 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
TABLE 4 Variables definition and descriptive statistics per treatment.
Variable Definition Mean (SD)
RAmb IA
Risk averse 1 if subject’s switching point for Lknown <5; 0 otherwise 0.16 (0.36) 0.19 (0.39) 0.28 (0.45)
Risk neutral 1 if subject’s switching point for Lknown =5; 0 otherwise 0.38 (0.48) 0.38 (0.48) 0.28 (0.45)
Risk lover 1 if subject’s switching point for Lknown >5; 0 otherwise 0.47 (0.50) 0.44 (0.50) 0.44 (0.50)
V(alue)-ambiguity
averse
1 if switching point Lknown >switching point Lunknown;
0 otherwise
0.44 (0.50) 0.48 (0.50) 0.52 (0.50)
V(alue)-ambiguity
neutral
1 if switching point Lknown =switching point Lunknown;
0 otherwise
0.41 (0.49) 0.34 (0.48) 0.31 (0.46)
V(alue)-ambiguity
lover
1 if switching point Lknown <switching point Lunknown;
0 otherwise
0.16 (0.36) 0.17 (0.38) 0.17 (0.38)
Contributions
belief
Subject’s belief about the total contribution to mitigation of
the 3 other group members
52.95 (14.32) 49.14 (14.79) 47.58 (16.44)
Partnersp−1Contributions to mitigation of the 3 other group members
in the previous period
51.94 (15.66) 49.67 (14.55) 46.56 (17.16)
Nb. of min.
contrib.
Number of times subject invests 0 token in mitigation 0.42 (1.53) 0.41 (1.25) 1.11 (2.75)
Nb. of max.
contrib.
Number of times subject invests 25 tokens in mitigation 2.70 (4.07) 2.27 (3.70) 2.98 (3.58)
Nb. of losses Number of losses incurred over the 10 periods 3.38 (1.62) 4.25 (1.72) 4.25 (1.95)
Lossp−11 if a loss occurred in the previous period; 0 otherwise 0.33 (0.47) 0.42 (0.49) 0.41 (0.49)
Period 1 in period 1, 2 in period 2, ..., 10 in period 10 5.50 (2.87) 5.50 (2.87) 5.50 (2.87)
Environment
subject
1 if subject’s studies are environment-related; 0 otherwise 0.50 (0.50) 0.50 (0.50) 0.50 (0.50)
Urn A belief 1 if subject believes that urn A will be drawn; 0 otherwise 0.21 (0.41) 0.23 (0.42)
Urn B belief 1 if subject believes that urn B will be drawn; 0 otherwise 0.23 (0.42) 0.25 (0.44)
Urn C belief 1 if subject believes that urn C will be drawn; 0 otherwise 0.27 (0.44) 0.27 (0.44)
No urn belief 1 if subject does not know which urn will be drawn;
0 otherwise
0.29 (0.45) 0.25 (0.43)
Urn Ap−11 if urn A was drawn in the previous period;0 otherwise 0.27 (0.44) 0.26 (0.44)
Urn Bp−11 if urn B was drawn in the previous period; 0 otherwise 0.37 (0.48) 0.42 (0.49)
Urn Cp−11 if urn C was drawn in the previous period; 0 otherwise 0.36 (0.48) 0.32 (0.47)
Info 1 if subject obtains information about the urn; 0 otherwise 0.22 (0.41)
Age Age of subject 22.16 (2.65) 21.83 (2.34) 21.48 (2.81)
Female 1 if subject is female; 0 otherwise 0.52 (0.50) 0.64 (0.54) 0.56 (0.53)
NEP Average of the 15 individual answers to the NEP (responses
to even questions have been reversed)
3.29 (0.29) 3.27 (0.30) 3.19 (0.35)
ENV Average of the 15 individual answers to the questionnaire on
pro-environmental behaviors
3.12 (0.29) 3.04 (0.31) 3.03 (0.27)
Altruist 1 if subject maximizes the other’s payoffs; 0 otherwise 0.02 (0.12) 0 0
Prosocial 1 if subject maximizes joint payoffs; 0 otherwise 0.59 (0.49) 0.69 (0.46) 0.52 (0.50)
Individualist 1 if subject maximizes their own payoffs; 0 otherwise 0.38 (0.48) 0.30 (0.46) 0.48 (0.50)
Competitive 1 if subject maximizes the payoffs’ difference; 0 otherwise 0.02 (0.12) 0.02 (0.12) 0
level of contributions in this treatment. There is a negative effect
of time which we already observed in the bottom-left corner of
Figure 3 but only for Classic subjects. Indeed, the coefficient of
Period is negative and significant, indicating that contributions
to mitigation tend to decline over time. However, there is no
econometric evidence of a difference between Environment and
Classic subjects since the coefficient of Environment subject is not
statistically significant. The possibility to obtain information wipes
Frontiers in Behavioral Economics 12 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
TABLE 5 Tobit regressions per treatment.
Dependent
variable: RAmb IA
Contributions
to
mitigation
(1) (2) (3)
Risk averse –6.914∗(3.014) –2.873 (2.905) –5.433 (4.642)
Risk lover –3.414 (2.210) 2.603 (2.042) –0.971 (3.997)
V-ambiguity averse –6.281∗∗ (2.291) –6.603∗∗ (2.179) –2.947 (4.206)
V-ambiguity lover –5.227 (3.023) –4.012 (2.792) –5.879 (4.876)
Contributions
belief
0.175∗∗∗ (0.029) 0.249∗∗∗ (0.03) 0.388∗∗∗ (0.036)
Nb. of losses –2.636∗∗∗ (0.682) –1.029∗(0.496) –0.840 (0.857)
Lossp−1–0.238 (0.415) –1.352∗∗ (0.456) –0.342 (0.853)
Period –0.055 (0.072) –0.051 (0.083) –0.362∗∗ (0.13)
Environment
subject
–0.157 (2.420) –2.778 (1.967) 4.181 (3.476)
Urn B belief 1.913∗∗ (0.669) 0.227 (1.116)
Urn C belief 2.103∗∗∗ (0.639) 1.686 (1.055)
No urn belief 3.372∗∗ (1.036) 1.349 (1.675)
Urn Bp−1–0.037 (0.56) –0.048 (0.887)
Urn Cp−1–0.512 (0.548) –0.312 (1.019)
Info 2.457∗(0.978)
Age –0.321 (0.355) –0.399 (0.395) –0.389 (0.598)
Female 0.882 (2.012) –2.099 (1.926) –3.015 (3.362)
NEP 6.161 (3.545) –0.710 (2.923) –0.743 (4.690)
ENV –0.609 (3.569) –1.067 (3.161) 4.382 (6.372)
Individualist 2.105 (2.501) –2.409 (1.926) –1.024 (3.191)
Constant 13.103 (19.078) 30.363∗(15.479) 5.223 (24.723)
Obs. 576 576 576
Left-censored obs. 25 24 66
Right-censored
obs.
155 132 171
Standard errors are in parentheses.
∗p<0.05.
∗∗ p<0.01.
∗∗∗ p<0.001.
out the effects of the beliefs about the mitigation urns. Indeed,
these dummy coefficients are no longer significant in contrast with
specification (2). What is of particular interest in specification (3)
is that subjects who receive information actually use it. Indeed,
when subjects are aware of the drawn urn, they mitigate more than
subjects who do not know which urn they face, as evidenced by the
positive and significant coefficient of Info.24 The fact that subjects
use information is not systematic. Indeed, Gangadharan and Nemes
(2009) found that, in a public good game where either the return
24 Unfortunately, we are not able to distinguish between subjects who
actually face urn A, urn B and urn C due to the small amount of data.
TABLE 6 Probit and tobit regressions on the WTP in IA.
Control
Dependent variable Probability
(1)
Intensity
(2)
Risk averse 0.047 (0.067) 2.379 (7.894)
Risk lover –0.070 (0.123) –6.004 (6.892)
V-ambiguity averse 0.377∗∗∗ (0.086) 25.076∗∗∗
(7.485)
V-ambiguity lover 0.421∗∗∗ (0.096) 24.674∗∗ (8.646)
Partnersp−1–0.001 (0.001) –0.098 (0.057)
Nb. of min. contrib. –0.109∗∗∗
(0.011)
–7.949∗∗∗
(1.890)
Nb. of max. contrib. –0.034∗∗∗
(0.009)
–1.884∗(0.832)
Nb. of losses 0.036 (0.028) 2.136 (1.497)
Lossp−10.028 (0.035) 0.466 (1.263)
Period –0.008 (0.005) 0.190 (0.192)
Environment subject –0.090 (0.089) –3.144 (6.544)
Urn B belief 0.010 (0.044) –0.744 (1.691)
Urn C belief –0.046 (0.037) –1.533 (1.671)
No urn belief –0.038 (0.061) –3.444 (2.471)
Urn Bp−10.006 (0.033) 0.713 (1.336)
Urn Cp−10.036 (0.051) 1.156 (1.557)
Age –0.007 (0.009) 0.074 (0.992)
Female 0.004 (0.079) 0.594 (6.220)
NEP –0.407∗∗∗
(0.076)
–22.064∗
(8.915)
ENV 0.248∗(0.124) 22.033∗
(10.571)
Individualist 0.027 (0.056) –3.361 (5.372)
Constant –1.750 (41.614)
Obs. 576 576
Left-censored obs. / 271
Right-censored obs. / 32
Standard errors are in parentheses.
Average marginal effects and robust standard errors are reported in (1).
∗p<0.05.
∗∗ p<0.01.
∗∗∗ p<0.001.
from the public good or the private good is unknown, even when
subjects learn that return, they do not take it into account in their
decision making. The authors explained this finding by the subjects’
aversion to strategic uncertainty. This leads us to the next result.
RESULT 6. When there is the possibility to eliminate ambiguity,
subjects who receive information actually use it. They mitigate more
than subjects who do not know the urn they face.
Following Brunette et al. (2020), we proceed in two steps
to analyze the subjects’ willingness to pay to obtain information
about the drawn urn in the Information Acquisition treatment.
Frontiers in Behavioral Economics 13 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
Firstly, we focus on the probability to make a positive offer to
obtain information25 using a probit model with random effects.
Secondly, we explore the intensity with which subjects buy
information using a tobit model with random effects since the
subjects’ WTP is left-censored at 0 and right-censored at 50.
In specification (1) of Table 6, we look at the probability that
subjects buy information. We see that value-ambiguity averse and
value-ambiguity lover subjects are more likely to buy information
than value-ambiguity neutral subjects. While it makes sense that
subjects who dislike ambiguity (i.e., V-ambiguity averse) try to
get rid of it by buying information, this result is more surprising
for subjects who show value-ambiguity proneness. If we take a
careful look at the probabilities to buy information by category
of ambiguity preferences,26 we notice that the mean probability
of value-ambiguity neutral subjects is relatively low (0.38, SD =
0.49) compared to the probability of the whole sample (0.53, SD
= 0.5). Still from specification (1) of Table 6, we see that subjects
who contribute 0 token to mitigation more often are less likely to
buy information, just as it is less likely that those who invest their
entire climate budget a larger number of times buy information.
The rationale may be that subjects who often contribute either the
minimum or the maximum make their decisions irrespective of the
drawn urn. They do not consider the state of nature in which they
may be. Therefore, they do not need to buy information. Subjects
who obtain a higher NEP score (i.e., subjects with a stronger
pro-environmental orientation) are less likely to buy information.
However, subjects who engage in pro-environmental behaviors
more often, that is, those who have a higher ENV score, are more
likely to buy information. Indeed, the coefficient of ENV is positive
and statistically significant.
If we now focus on specification (2) of Table 6, that is, on
the intensity of the WTP, we see that ambiguity preferences also
matter, as evidenced by the coefficients of V-ambiguity averse and
V-ambiguity lover which are positive and significant. In other
words, value-ambiguity averse and value-ambiguity lover subjects
pay more to obtain information than value-ambiguity neutral
subjects. It is reasonable to believe that subjects who are averse
to value-ambiguity will pay more in order to know the urn
they will face. Snow (2010) theoretically proved, using the KMM
model, that the WTP for information that resolves ambiguity
increases with higher ambiguity aversion. In the same vein, Attanasi
and Montesano (2012), relying on the Choquet expected utility
model, showed that the reservation price for information about the
probability of an unknown event rises with the degree of ambiguity
aversion. However, what is unexpected is that value-ambiguity
lover subjects are also willing to pay more than value-ambiguity
neutral subjects to eliminate ambiguity. If we look at the mean
WTP by category of ambiguity preferences,27 we notice that value-
ambiguity neutral subjects actually paid very little for information
(5.6 ECUs on average) while the average WTP over the 10 periods
25 The dependent variable takes the value 1 if the subjects’ WTP is strictly
higher than 0, and 0 otherwise.
26 Mean probability for V-ambiguity averse: 0.56 (SD = 0.5); for V-ambiguity
neutral: 0.38 (SD = 0.49); for V-ambiguity lover: 0.73 (SD = 0.45).
27 Mean WTP for V-ambiguity averse: 12.44 (SD = 14.93); for V-ambiguity
neutral: 5.6 (SD = 8.11); for V-ambiguity lover: 14.82 (SD = 14.77).
and the 64 subjects is 10.71 ECUs (SD = 13.61). Also, subjects
who often make zero contribution and those who contribute their
entire climate budget quite often are willing to pay less to obtain
information. Indeed, the coefficients of Nb. of min. contrib. and
Nb. of max. contrib. are negative and significant. Those subjects
value less information because they may not consider the different
mitigation urns if they are used to investing either nothing or their
entire endowment in mitigation. The intensity of the WTP is also
explained by environmental preferences. Subjects with higher NEP
scores are willing to pay less to obtain information while subjects
with higher ENV scores are willing to pay more to know the drawn
urn. It follows from the above the following result.
RESULT 7. Ambiguity preferences explain the probability to buy
information and the intensity of the WTP to obtain information.
4 Discussion
This paper investigates the role of risk and ambiguity
preferences on how to manage probabilistic loss threats in a risky
context, in an ambiguous one, and when there is the possibility
to fully resolve ambiguity by buying information. We propose
an experiment in which each group of four subjects faces a risk
of incurring a climate-related event that can cause a loss for
every group member. In each treatment (i.e., Risk, Ambiguity,
and Information Acquisition), subjects have two strategies to
face the environmental threat: mitigation which reduces the
occurrence probability for everyone in the group and adaptation
which decreases the magnitude of their own damage. They are
asked to decide on the allocation of their tokens between these
two strategies. We also control for the subjects’ risk, ambiguity,
environmental, and social preferences.
We find that the introduction of ambiguity has no effect on
average contributions to mitigation. This result supports the series
of papers which found no effect of uncertainty in public good
games settings (see e.g., Fisher et al., 1995;Boulu-Reshef et al.,
2017;Théroude and Zylbersztejn, 2020). However, when it comes
to the individual decisions to mitigate or to adapt, we show that risk
and ambiguity aversion matters in this trade-off by jeopardizing
cooperation. Indeed, in a risky context, risk and value-ambiguity
aversion negatively impacts the decision to mitigate. When the
probability of a climate-related event occurring is ambiguous,
subjects who dislike ambiguity neglect mitigation policies in favor
of adaptation ones.
We believe that these findings contribute to the understanding
of the effect of ambiguity in public good game settings where
contributions are used to avoid probabilistic losses. The results
show that preferences toward ambiguity play a role in the choice
of one or the other strategy. Future research is thus needed in order
to deepen our knowledge of the effects of individual preferences in
social dilemmas related to disaster prevention.
We acknowledge that we cannot consider our experimental
results as guidelines for climate policies. As shown by Goeschl et al.
(2020), the use of abstract public good game experimental evidence
as guidance for climate policies may be problematic. Indeed, such
configurations (i.e., small group size, relatively high marginal per-
capita return, and payoff symmetry) are unlikely to represent the
Frontiers in Behavioral Economics 14 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
real-life complexities of global climate change. The mitigation of
climate change involve all humanity and each individual effort
has a very low impact. Obviously, such conditions are impossible
to replicate in the lab. That is why these experiments may lack
generalizability. Nevertheless, our research provides interesting
findings on the role of risk and ambiguity in collective action
problems.
Our analysis also shows that subjects are willing to pay to
obtain information in order to eliminate ambiguity. More than
half of the time, subjects wish to have access to information and
they actually use it when they obtain it. This emphasizes the
importance to make information available to individuals, whether
it is through the education system, awareness campaigns, or
science popularization. Alpizar et al. (2011) state that, in some
situations, it could be beneficial for governments to alleviate
ambiguity among individuals by providing information. It is
also in line with the conclusion of Gautier et al. (2019) which
states that raising awareness about the environmental challenges
to come can strengthen the adoption of pro-social and pro-
environmental behaviors.
In future research, it could be worthwhile to introduce
real consequences outside the lab, such as making donations
to environmental associations which actually act against climate
change. In this way, the environmental aspect of the game would
become more salient.
While we have considered mitigation as a means of reducing the
occurrence probability and adaptation as a means of reducing the
size of the damage, an interesting extension of this paper would be
to consider adaptation as a way to reduce individual risks. Indeed,
one can easily imagine individuals choosing to eat healthy or to
exercise in order to reduce their chances of falling ill because of bad
air quality.
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and
accession number(s) can be found at: https://static.data.gouv.fr/
resources/cc-risk-ambiguity/20240605-135931/online-database.
xls.
Ethics statement
Ethical review and approval was not required for the
study on human participants in accordance with the local
legislation and institutional requirements. The studies were
conducted in accordance with the local legislation and institutional
requirements. The participants provided their written informed
consent to participate in this study.
Author contributions
SVD: Writing – original draft, Writing – review & editing.
KBM: Writing – original draft, Writing – review & editing. MB:
Writing – original draft, Writing – review & editing.
Funding
The author(s) declare financial support was received for
the research, authorship, and/or publication of this article. This
research has been conducted with the financial support of the
“Projets collaboratifs INRAE 2021” and the LabEx ARBRE. This
work was supported by a grant overseen by the French National
Research Agency (ANR) as part of the “Investissements d’Avenir”
program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).
This work was also supported by the Laboratory of Experimental
Economics of Strasbourg (LEES).
Acknowledgments
The authors warmly thank Eve-Angéline Lambert, Julien
Jacob, François Cochard, and the participants at the Newcastle
Experimental Economics Workshop 2022 and at the 13th ASFEE
Conference for their useful comments. The authors are grateful for
the support of the LabEx ARBRE. They also thank the two reviewers
for their useful comments.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/frbhe.2024.
1456436/full#supplementary-material
References
Alary, D., Gollier, C., and Treich, N. (2013). The effect of ambiguity aversion
on insurance and self-protection. Econ. J. 123, 1188–1202. doi: 10.1111/ecoj.
12035
Alekseev, A., Charness, G., and Gneezy, U. (2017). Experimental methods: when
and why contextual instructions are important. J. Econ. Behav. Organ. 134, 48–59.
doi: 10.1016/j.jebo.2016.12.005
Frontiers in Behavioral Economics 15 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
Alpizar, F., Carlsson, F., and Naranjo, M. A. (2011). The effect of ambiguous
risk, and coordination on farmers’ adaptation to climate change — a framed field
experiment. Ecol. Econ. 70, 2317–2326. doi: 10.1016/j.ecolecon.2011.07.004
Attanasi, G., Gollier, C., Montesano, A., and Pace, N. (2014). Eliciting ambiguity
aversion in unknown and in compound lotteries: a smooth ambiguity model
experimental study. Theory Decis. 77, 485–530. doi: 10.1007/s11238-013-9406-z
Attanasi, G., and Montesano, A. (2012). The price for information about
probabilities and its relation with risk and ambiguity. Theory Decis. 73, 125–160.
doi: 10.1007/s11238-011-9271-6
Banda, M. (2018). Climate adaptation law: governing multi-level public goods
across borders. Vanderbilt J. Transnatl Law 51, 1027–1074.
Barrett, S., and Dannenberg, A. (2012). Climate negotiations under scientific
uncertainty. Proc. Nat. Acad. Sci. 109, 17372–17376. doi: 10.1073/pnas.1208417109
Barrett, S., and Dannenberg, A. (2014). Sensitivity of collective action to uncertainty
about climate tipping points. Nat. Clim. Change 4, 36–39. doi: 10.1038/nclimate2059
Becker, G., DeGroot, M., and Marschak, J. (1964). Measuring utility by a single-
response sequential method. Behav. Sci. 9, 226–232. doi: 10.1002/bs.3830090304
Berger, L., Emmerling, J., and Tavoni, M. (2017). Managing catastrophic
climate risks under model uncertainty aversion. Manage. Sci. 63, 749–765.
doi: 10.1287/mnsc.2015.2365
Björk, L., Kocher, M., Martinsson,P., and Nam Khanh, P. (2016). Cooperation under
risk and ambiguity. Technical Report 683. Gothenburg: University of Gothenburg,
Department of Economics.
Blanco, E., Dutcher, E. G., and Haller, T. (2020). Social dilemmas with
public and private insurance against losses. J. Econ. Behav. Organ. 180, 924–937.
doi: 10.1016/j.jebo.2019.02.008
Blanco, M., Engelmann, D., Koch, A. K., and Normann, H.-T. (2010). Belief
elicitation in experiments: is there a hedging problem? Exp. Econ. 13, 412–438.
doi: 10.1007/s10683-010-9249-1
Boucher, V., and Bramoullé, Y. (2010). Providing global public goods under
uncertainty. J. Public Econ. 94, 591–603. doi: 10.1016/j.jpubeco.2010.06.008
Boulu-Reshef, B., Brott, S. H., and Zylbersztejn, A. (2017). Does uncertainty deter
provision of public goods? Rev. Écon. 68:785. doi: 10.3917/reco.pr3.0087
Boun My, K., Brunette, M., Couture, S., and Van Driessche, S. (2024). Are
ambiguity preferences aligned with risk preferences? J. Behav. Exp. Econ. 111:102237.
doi: 10.1016/j.socec.2024.102237
Bramoullé, Y., and Treich, N. (2009). Can uncertainty alleviate the commons
problem? J. Eur. Econ. Assoc. 7, 1042–1067. doi: 10.1162/JEEA.2009.7.5.1042
Brunette, M., Coutre, S., and Pannequin, F. (2017a). Is forest insurance a relevant
vector to induce adaptation efforts to climate change? Ann. For. Sci. 74, 41–49.
doi: 10.1007/s13595-017-0639-9
Brunette, M., Foncel, J., and Kéré, E. (2017b). Attitude towards risk and production
decision: an empirical analysis on french private forest owners. Environ. Model. Assess.
22, 563–576. doi: 10.1007/s10666-017-9570-6
Brunette, M., Hanewinkel, M., and Yousefpour, R. (2020). Risk aversion hinders
forestry professionals to adapt to climate change. Clim. Change 162, 2157–2180.
doi: 10.1007/s10584-020-02751-0
Camerer, C., and Weber, M. (1992). Recent developments in modeling preferences:
uncertainty and ambiguity. J. Risk Uncertain. 5, 325–370. doi: 10.1007/BF00122575
d’Albis, H., Attanasi, G., Thibault, E. (2020). An experimental test of the under-
annuitization puzzle with smooth ambiguity and charitable giving. J. Econ. Behav.
Organ. 180, 694–717. doi: 10.1016/j.jebo.2019.09.019
Dannenberg, A., Löschel, A., Paolacci, G., Reif, C., and Tavoni, A. (2015). On
the provision of public goods with probabilistic and ambiguous thresholds. Environ.
Resour. Econ. 61, 365–383. doi: 10.1007/s10640-014-9796-6
De Pinto, A., Robertson, R. D., and Darko Obiri, B. (2013). Adoption of climate
change mitigation practices by risk-averse farmers in the Ashanti region, Ghana. Ecol.
Econ. 86, 47–54. doi: 10.1016/j.ecolecon.2012.11.002
Dickinson, D. L. (1998). The voluntary contributions mechanism with uncertain
group payoffs. J. Econ. Behav. Organ. 35, 517–533. doi: 10.1016/S0167-2681(98)00048-1
Dunlap, R., Liere, K., Mertig, A., and Jones, R. (2000). Measuring endorsement
of the new ecological paradigm: a revised NEP scale. J. Soc. Issues 56, 425–442.
doi: 10.1111/0022-4537.00176
Eddai, N., and Guerdjikova, A. (2023). To mitigate or to adapt: How to deal
with optimism, pessimism and strategic ambiguity? J. Econ. Behav. Organ. 211, 1–30.
doi: 10.1016/j.jebo.2023.04.011
Ehrlich, I., and Becker, G. S. (1972). Market insurance, self-insurance, and self-
protection. J. Polit. Econ. 80, 623–648. doi: 10.1086/259916
Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. Q. J. Econ. 75, 643–669.
doi: 10.2307/1884324
Etner, J., Jeleva, M., and Raffin, N. (2020). Climate policy: how to deal with
ambiguity? Econ. Theory 72, 263–301. doi: 10.1007/s00199-020-01284-y
Fischbacher, U., and Gächter, S. (2010). Social preferences, beliefs, and the
dynamics of free riding in public goods experiments. Am. Econ. Rev. 100, 541–556.
doi: 10.1257/aer.100.1.541
Fischbacher, U., Schudy, S., and Teyssier, S. (2014). Heterogeneous reactions
to heterogeneity in returns from public goods. Soc. Choice Welfare 43, 195–217.
doi: 10.1007/s00355-013-0763-x
Fisher, J., Isaac, R. M., Schatzberg, J. W., and Walker, J. M. (1995). Heterogenous
demand for public goods: behavior in the voluntary contributions mechanism. Public
Choice 85, 249–266. doi: 10.1007/BF01048198
Gächter, S., and Renner, E. (2010). The effects of (incentivized) belief elicitation in
public goods experiments. Exp. Econ. 13, 364–377. doi: 10.1007/s10683-010-9246-4
Gangadharan, L., and Nemes, V. (2009). Experimental analysis of risk and
uncertainty in provisioning private and public goods. Econ. Inq. 47, 146–164.
doi: 10.1111/j.1465-7295.2007.00118.x
Gautier, A., Hoet, B., Jacqmin, J., and Van Driessche, S. (2019). Self-consumption
choice of residential PV owners under net-metering. Energy Policy 128, 648–653.
doi: 10.1016/j.enpol.2019.01.055
Goeschl, T., Kettner, S. E., Lohse, J., and Schwieren, C. (2020). How much can we
learn about voluntary climate action from behavior in public goods games? Ecol. Econ.
171:106591. doi: 10.1016/j.ecolecon.2020.106591
Gollier, C. (2011). Portfolio choices and asset prices: the comparative statics of
ambiguity aversion. Rev. Econ. Stud. 78, 1329–1344. doi: 10.1093/restud/rdr013
Greenhill, B., Dolšak, N., and Prakash, A. (2018). Exploring the adaptation-
mitigation relationship: does information on the costs of adapting to climate
change influence support for mitigation? Environ. Commun. 12, 911–927.
doi: 10.1080/17524032.2018.1508046
Greiner, B. (2015). Subject pool recruitment procedures: organizing experiments
with ORSEE. J. Econ. Sci. Assoc. 1, 114–125. doi: 10.1007/s40881-015-0004-4
Halevy, Y. (2007). Ellsberg revisited: an experimental study. Econometrica 75,
503–536. doi: 10.1111/j.1468-0262.2006.00755.x
Hasson, R., Löfgren, A., and Visser, M. (2010). Climate change in a public goods
game: investment decision in mitigation versus adaptation. Ecol. Econ. 70, 331–338.
doi: 10.1016/j.ecolecon.2010.09.004
Hasson, R., Löfgren, A., and Visser, M. (2012). Treatment effects of climate change
risk on mitigation and adaptation behaviour in an experimental setting. S. Afr. J. Econ.
80, 415–430. doi: 10.1111/j.1813-6982.2011.01278.x
Ingham, A., Ma, J., and Ulph, A. (2007). Climate change, mitigation
and adaptation with uncertainty and learning. Energy Policy 35, 5354–5369.
doi: 10.1016/j.enpol.2006.01.031
Ingham, A., Ma, J., and Ulph, A. M. (2013). Can adaptation and mitigation be
complements? Clim. Change 120, 39–53. doi: 10.1007/s10584-013-0815-3
IPCC (2021). “Summary for Policymakers,” in Climate Change 2021: The Physical
Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, eds. V. Masson-Delmotte, P. Zhai, A.
Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis,
M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O.
Yelekçi, R. Yu, and B. Zhou (Cambridge, UK; New York, NY: Cambridge University
Press), 3–32. doi: 10.1017/9781009157896.001
Jullien, B., Salanié, B., and Salanié, F. (1999). Should more risk-averse agents exert
more effort? Geneva Pap. Risk Insur. Theory 24, 19–28. doi: 10.1023/A:1008729115022
Kane, S., and Shogren, J. F. (2000). Linking adaptation and mitigation in climate
change policy. Clim. Change 45, 75–102. doi: 10.1023/A:1005688900676
Kartha, S., Bhandari, P., van Schaik, L., Cornland, D., and Kjellén, B. (2006).
Adaptation as a strategic issue in climate negotiations. Europ. Clim. Platform Rep. 3,
1–29.
Keser, C., and Montmarquette, C. (2008). Voluntary contributions
to reduce expected public losses. J. Econ. Behav. Organ. 66, 477–491.
doi: 10.1016/j.jebo.2006.06.007
Khan, M. R., and Munira, S. (2021). Climate change adaptation as
a global public good: implications for financing. Clim. Change 167:50.
doi: 10.1007/s10584-021-03195-w
Klibanoff, P., Marinacci, M., and Mukerji, S. (2005). A smooth
model of decision making under ambiguity. Econometrica 73, 1849–1892.
doi: 10.1111/j.1468-0262.2005.00640.x
Knight, F. H. (1921). Risk Uncertainty and Profit. Boston, MA: Houghton Mifflin.
Kuusela, O.-P., and Laiho, T. (2020). The role of research in common
pool problems. J. Environ. Econ. Manage. 100:102287. doi: 10.1016/j.jeem.2019.
102287
Lades, L. K., Laffan, K., and Weber, T. O. (2021). Do economic
preferences predict pro-environmental behaviour? Ecol. Econ. 183:106977.
doi: 10.1016/j.ecolecon.2021.106977
Lefebvre, M., and Van Driessche, S. (2022). Climate Policies Under Income
Inequality. Memphis, TN: Mimeo. doi: 10.2139/ssrn.4624843
Frontiers in Behavioral Economics 16 frontiersin.org
Van Driessche et al. 10.3389/frbhe.2024.1456436
Levati, M. V., and Morone, A. (2013). Voluntary contributions with risky and
uncertain marginal returns: the importance of the parameter values. J. Public Econ.
Theory 15, 736–744. doi: 10.1111/jpet.12043
Levati, M. V., Morone, A., and Fiore, A. (2009). Voluntary contributions
with imperfect information: an experimental study. Public Choice 138, 199–216.
doi: 10.1007/s11127-008-9346-2
McEvoy, D. M., Haller, T., and Blanco, E. (2022). The role of non-binding pledges
in social dilemmas with mitigation and adaptation. Environ. Resour. Econ. 81, 685–710.
doi: 10.1007/s10640-021-00645-y
Morath, F. (2010). Strategic information acquisition and the mitigation of
global warming. J. Environ. Econ. Manage. 59, 206–217. doi: 10.1016/j.jeem.2009.
08.003
Mossin, J. (1968). Aspects of rational insurance purchasing. J. Polit. Econ. 76:
553–568. doi: 10.1086/259427
Murphy, R. O., Ackermann, K. A., and Handgraaf, M. (2011). Measuring
social value orientation. Judgm. Decis. Mak. 6, 771–781. doi: 10.1017/S193029750
0004204
Parry, M. (Ed.) (2007). Climate Change 2007: Impacts, Adaptation and
Vulnerability: Contribution of Working Group II to the Fourth Assessment Report
of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge
University Press.
Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica 32,
122–136. doi: 10.2307/1913738
Raihani, N., and Aitken, D. (2011). Uncertainty, rationality and cooperation in the
context of climate change. Clim. Change 108, 47–55. doi: 10.1007/s10584-010-0014-4
Scheraga, J. D., and Grambsch, A. E. (1998). Risks, opportunities, and adaptation to
climate change. Clim. Res. 11, 85–95. doi: 10.3354/cr011085
Snow, A. (2010). Ambiguity and the value of information. J. Risk Uncertain. 40,
133–145. doi: 10.1007/s11166-010-9088-7
Théroude, V., and Zylbersztejn, A. (2020). Cooperation in a risky world. J. Public
Econ. Theory 22, 388–407. doi: 10.1111/jpet.12366
Tol, R. S. (2005). Adaptation and mitigation: trade-offs in substance and methods.
Environ. Sci. Policy 8, 572–578. doi: 10.1016/j.envsci.2005.06.011
Treich, N. (2010). The value of a statistical life under ambiguity aversion. J. Environ.
Econ. Manage. 59, 15–26. doi: 10.1016/j.jeem.2009.05.001
Truong, C., and Trük, S. (2016). It’s not now or never: implications of investment
timing and risk aversion on climate adaptation to extreme events. Eur. J. Oper. Res. 253,
856–868. doi: 10.1016/j.ejor.2016.01.044
Villeval, M. C. (2012). Contribution aux biens publics et préférences sociales:
apports récents de l’économie comportementale. Rev. Écon. 63, 389–420.
doi: 10.3917/reco.633.0389
Frontiers in Behavioral Economics 17 frontiersin.org
