Question:
You are given the full text of a scientific research paper.

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
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nature climate change
https://doi.org/10.1038/s41558-023-01679-4
Article
Participating in a climate prediction market
increases concern about global warming
Moran Cerf
  1
, Sandra C. Matz
  1
 & Malcolm A. MacIver
  2,3,4,5,6
Modifying attitudes and behaviours related to climate change is difficult.
Attempts to offer information, appeal to values and norms or enact policies
have shown limited success. Here we examine whether participation in
a climate prediction market can shift attitudes by having the market act
as a non-partisan adjudicator and by prompting participants to put their
‘money where their mouth is’. Across two field studies, we show that betting
on climate events alters: (1) participants’ concern about climate change, (2)
support for remedial climate action and (3) knowledge about climate issues.
While the effects were dependent on participants’ betting performance in
Study 1, they were independent of betting outcomes in Study 2. Overall, our
findings suggest that climate prediction markets could offer a promising
path to changing people’s climate-related attitudes and behaviour.
The combined forces of social media and rise of populism have ampli-
fied the politicization of knowledge. What is considered true often
depends on group membership rather than scientific evidence and
facts1–6. This politicization is seen in numerous topics, including climate
change. Overwhelming scientific evidence suggests that climate change
is occurring7, is caused by human activity8 and is likely to result in dire
consequences9,10. Nonetheless, actions of governments around the
world lag behind what climate scientists say is needed. In some cases,
this inaction is related to a lack of concern about climate change. For
example, in the United States, surveys show that over a third of the
population believes that the seriousness of global warming is exag-
gerated11, and more than half the population disagrees with the claim
that climate change is caused by humans12.
Raising concern about climate change and support for remedial
action at the individual and collective level is challenging for numer-
ous reasons. First, it is difficult to attribute a specific climate-related
incident to a single cause. Second, remedial actions taken by one indi-
vidual or collective often do not yield visible outcomes. Third, the
cost of action is immediate whereas the benefits are distributed over
long time horizons13. Specifically, while climate change will adversely
impact future generations, for most people there is no immediate cost
to rejecting its occurrence on ideological grounds14. Compounded by
the brain’s challenges in thinking about temporally or spatially distant
events15–17, these factors make it difficult to change sceptics’ views on
the topic and garner support for corrective action.
However, acknowledging the role of erroneous beliefs that have no
immediate cost offers a potential pathway to shifting people’s climate
change-related attitudes and behaviours: devise a mechanism to make
maintaining false beliefs costly in the near term. Research shows that
people often behave in ways that contradict their stated beliefs when
money is on the line18. For example, climate sceptics publicly deny
global warming but do not invest in geographic regions that will prob-
ably suffer from a rise in sea levels19. Building on this, we suggest using
climate prediction markets to shift attitudes towards the scientific
consensus by increasing the cost of maintaining false beliefs. Simply,
we provide a financial reward (/penalty) for correct (/incorrect) predic-
tions about soon-to-occur events that are impacted by global warming
using a prediction market.
Traditionally, prediction markets have been implemented to
crowd-source estimates about uncertain events in the future20. Those
markets have been shown to accurately predict the outcomes of elec-
tions21, reproducibility of scientific findings22, spread of disease23 or
aggregation of group choices24. In the context of climate change, pre-
diction markets have been suggested as a tool for aggregating views
on policies25 and as a way to provide credible signals about climate
science26–28. However, there is no empirical evidence supporting the
Received: 8 September 2020
Accepted: 21 April 2023
Published online: 8 June 2023
 Check for updates
1Columbia Business School, Columbia University, New York, NY, USA. 2McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
3Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA. 4Department of Biomedical Engineering, Northwestern University,
Evanston, IL, USA. 5Department of Neurobiology, Northwestern University, Evanston, IL, USA. 6Department of Computer Science, Northwestern
University, Evanston, IL, USA.
 e-mail: nature@morancerf.com; sm4409@gsb.columbia.edu; maciver@northwestern.edu

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take a position on topics. Study 1 had 4,737 interactions (stock offers,
trades and so on) with an average of 9.5 contracts per day.
Concern about global warming increases,
conditional on winning
We first tested whether engaging in the climate prediction market had
an impact on how concerned participants were about climate change.
Specifically, we ran linear regressions to predict climate concern in the
post-survey from experimental condition (0 = control, 1 = treatment),
controlling for participants’ concern in the pre-survey. Contrary to our
expectations, participating in the climate prediction market did not
lead to an overall increase in climate concern compared to the control
group (B = −0.005, SE(B) = 0.015, β = −0.001, t = −0.04, p = .976; Fig. 2;
all results hold when using the difference between pre- and post-survey
as outcome). B, Unstandardized regression coefficient. SE, Standard
error. β, Standardized regression coefficient. t, statistical coefficients
of test; p, statistical coefficient of significance.
However, exploratory analyses of the treatment condition
revealed an effect conditional on participants’ performance in the
betting market. Specifically, we used the robust MM-type estimator30
to regress the difference in concern between post- and pre-surveys
(higher values indicate a shift towards more concern about climate
change) onto two indicators of performance: (1) the number of bets
won and (2) total earnings. Here betting outcomes significantly and
consistently predicted the change in concern (number of bets won:
B = 0.007, SE(B) = 0.003, t = 2.44, p = 0.017; total earnings: B = 0.01,
SE(B) = 0.005, t = 2.37, p = 0.021). That is, participants’ concerns about
climate change increased if they were accurate in their predictions.
Finally, we tested whether the impact of the treatment varied
between believers and sceptics. Using the robust MM-type estimator to
regress the difference in concern between post- and pre-surveys on the
binary believer/sceptic variable, we saw a marginally significant effect
(B = 0.14, SE(B) = 0.08, t = 1.71, p = 0.089) suggesting that the treatment
was more effective for believers than sceptics. The moderating effects
of performance on concern were found to be equally strong for both
believers and sceptics (B = 0.001, SE(B) = 0.01, t = 0.11, p = 0.911).
To further explore participants’ engagement with the climate
prediction market, we tested for differences between believers and
sceptics in betting outcomes (bets won and total earnings; Supplemen-
tary Fig. 2) and behaviour (confidence, defined as the distance from
the neutral US$0.50/US$0.50 value). Despite believers being among
the highest earners in our market (top 11% of earners), both groups did
not significantly differ in the number of bets won (B = 0.70, SE(B) = 2.01,
p = 0.728) or the total earnings (B = 1.44, SE(B) = 1.33, p = 0.282). How-
ever, the bets of believers indicated marginally higher levels of confi-
dence (B = 0.48, SE(B) = 0.24, p = 0.053; Supplementary Fig. 6).
Altogether, Study 1 offers suggestive evidence that prediction
markets can increase concern for climate change under certain condi-
tions (that is, successful betting). Despite the promising results, Study
1 also suffers from a number of limitations. First, by virtue of its reliance
on a real-world market resembling the one seen in public exchanges
(‘two-sided’), it was hard to isolate the treatment effects (that is, partici-
pants may have placed bets that did not turn into contracts). Second,
the decision to target only individuals with polarized positions made
obtaining a shift in concern challenging because believers are already
at a climate concern ceiling, while sceptics are hardest to shift. Third,
the size of our participant pool made it impossible to detect small
effects that are common in behaviour-change research. Fourth, the
fact that we opted for a passive control group that did not engage in any
meaningful task during the prediction period prevented us from test-
ing whether the effect of successful betting on concern was uniquely
related to climate predictions or the result of participants experiencing
positive outcomes.
Study 2 overcomes these limitations by testing the effects in a
controlled experimental setting, with an active control group that
notion that betting on climate-related events can shift people’s: (1)
concerns about the consequences of climate change, (2) support for
remedial action at the individual/collective level and (3) knowledge
about climate topics.
Climate prediction markets
We introduce climate prediction markets as a novel intervention and
report experimental findings on how participating in the markets influ-
ences people’s concern about climate change, support for action and
climate knowledge. Our market offers individuals the opportunity
to bet on future outcomes (that is, ‘the average temperature in the
Northern Hemisphere in the coming month will be higher than that in
the equivalent time window over the last decade’) and earn money if
their predictions are proven right.
We implemented two different prediction markets across two field
studies. In both studies participants engaged in a market where they
took positions on future climate events and earned money based on
their prediction accuracy.
Betting topics were set by the experimenters and were released
intermittently (between 1 and 3 days apart in Study 1 and daily in
Study 2). The bets reflected both events that dominated the news (that
is, California wildfires, extreme heat waves) and events that were less
salient to the average participant (that is, Antarctic Sea ice extent,
change in the Air Quality Index). All bets had a settle date/time and an
unambiguous source for determining the outcome. We term a particu-
lar prediction a ‘bet’. For each bet, participants could decide whether
they wanted to make a bet, which position to take (Yes/No) and how
much money to wager.
We surveyed participants before (‘pre-survey’) and after
(‘post-survey’) the period during which they engaged in the predic-
tion market (Fig. 1a). In both studies, we compared participants who
engage in the climate prediction market to a control condition (Study
1: passive control group, Study 2: active control group that participated
in a sports and entertainment prediction market). Comparisons with
the control group allow us to account for changes that might occur
naturally over time (for example, natural variation in the salience of
climate disasters that are known to impact people’s attitudes about
climate change29).
Study 1
Participants (n = 143) were recruited online and screened for climate
beliefs and US nationality. Climate belief was defined as agreement
with the statement ‘Global warming refers to the idea that the world’s
average temperature has been increasing over the past 150 years and
may be increasing more. Do you think that global warming is happen-
ing?’ (Supplementary Table 5 and Supplementary Fig. 4 provide demo-
graphic and climate concern breakdowns for the 70 climate believers
and 73 sceptics included in the study). We screened for individuals
with polarized positions by selecting only individuals who answered
‘Yes’/‘No’, skipping those who said ‘Don’t know’. Participants completed
two surveys, one before the beginning of the prediction period and
one at its conclusion. The surveys captured participants’ concerns
about climate change, support for climate action, climate knowledge
and variables such as demographics, political orientation and more
(Methods and Supplementary Information). Participants within the
groups of believers and sceptics were randomly assigned to either the
control (n = 73) or treatment group (n = 70). Each participant in the
treatment group received US$20 to fund bets in the prediction market.
During the prediction period, participants made bets on future events
(Supplementary Methods show all bets). Because of the double-auction
structure of the market (if one participant bet US$0.55 that an event
would occur, it only becomes a contract if another participant bets
US$0.45 that the event will not occur; Supplementary Methods provide
details on the betting mechanism), not all bets turned into contracts.
We analysed bet offers as reflections of participants’ willingness to

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engaged in a non-climate prediction market, using a much larger
sample size of people who are less extreme in their beliefs about
climate change.
Study 2
Participants (n = 1,005) were recruited online similar to Study 1. Of
the total participants, those (n = 664) who wagered at least US$10 and
placed at least 15 bets were included in the analyses. As in Study 1, pre-
and post-surveys measured participants’ climate concern, support and
knowledge (Methods). Between surveys, participants were randomly
assigned to either a climate prediction market (n = 356, treatment) or a
sports and entertainment prediction market (n = 308, control; Methods
and Supplementary Information provide evidence that the randomi-
zation was successful). Both prediction markets ran for a period of 35
days during which one new bet was posted daily. Upon logging into the
prediction market, participants saw an overview of their betting profile
(amount won thus far, number of bets placed) and were informed about
the outcomes of previous bets. Participants then saw the daily bet
(Fig. 1c). Participants were asked to decide whether to bet, which posi-
tion to take and how much money to wager. Each participant received
US$20 at the beginning of the study. Overall, participants placed 10,384
bets (15.6 bets per person).
Pre-survey
Demographics
Climate concerns
Climate support
Climate knowledge
a
Control
Treatment
Study 1: No predictions
Study 2: Sports/entertainment prediction market
Study 1,2: Climate prediction market
Post-survey
Climate concerns
Climate support
Climate knowledge
b
c
Percent
100
0
Bet won by:*
Average amount bet (US$)
5
10
15
25
20
Study 2
Time (days)
Earnings (US$)
1,000
500
–500
0
–1,000
4
8
12
16
20
24
28
32
36
Study 1
Predicted no
Predicted yes
Abstained
Did not visit site
Average bet
Believers
Sceptics
Time (days)
0
25
50
75
30
35
1
0.25
0.50
0.75
0
*‘Predicted yes’ bets align with expectations of climate science
CO2 level (ppm)
Global carbon dioxide levels
in Earth’s atmosphere
420
410
Year
400
390
380
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
Fig. 1 | Experimental design. a, Participants first answered various questions
about their views on climate issues in a pre-survey (Supplementary Tables 2, 4,
18 and 19 provide survey questions). Afterwards, participants were divided into
treatment (climate prediction market) and control (Study 1: no predictions,
Study 2: sports/entertainment prediction market) groups. b, Participants in the
prediction markets made bets continuously (left: Study 1 earnings breakdown
over time) or daily (right: Study 2; left y axis corresponds to the percent of
participants taking yes/no/abstain positions on each daily bet, and the right
y axis corresponds to the average daily bet wager). We denote above each bet
the prediction that ended up being accurate (note that we attempted to ensure
that ‘Yes’ bets would align with climate science). c, Visualization of the climate
predictions market in Study 1 (on a dedicated website, titled C-Hedge; left) and
2 (right). The wager in Study 1 ranged from US$0.50 to US$0.99 with a position
of less than US$0.50 amounting to switching (that is, US$0.40 ‘Yes’ is US$0.60
‘No’). The wager in Study 2 ranged from US$0.01–US$1. Following the prediction
period, both treatment and control participants completed a post-survey
addressing climate issues and assessments of their overall experience.

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Increase in concern about global warming
We tested whether engaging in the climate prediction market had an
impact on how concerned participants were about climate change,
how supportive they were of remedial action and how much they knew
about climate change. Specifically, we ran a series of linear regressions
to predict climate concern, support and knowledge in the post-survey
from category of experimental condition (0 = control, 1 = treatment)
controlling for the respective concern, support and knowledge in the
pre-survey and including the socio-demographic variables to increase
the precision of the estimates (Supplementary Information ‘Robust-
ness checks’). The treatment group showed significantly higher lev-
els of concern (B = 0.12, SE(B) = 0.045, β = 0.08, t = 2.69, p = 0.007;
Fig. 3a), support for remedial action (B = 0.13, SE(B) = 0.058, β = 0.09,
t = 2.19, p = 0.029; Fig. 3b), and knowledge (B = 1.58, SE(B) = 0.22,
β = 0.52, t = 7.15, p < 0.001; Fig. 4) in the post-survey compared to
the control group.
Given that we asked the same question regarding concern and
support in the pre- and post-survey, we could compare participants’
scores to understand the underlying mechanisms of the effects. Partici-
pants in the treatment condition showed significantly higher levels of
concern in the post-survey than the pre-survey (t(355) = 2.23, p = 0.026;
Fig. 3) while controls did not (t(307) = −0.93, p = 0.353; paired t tests).
Similarly, participants in the treatment condition increased their sup-
port for remedial action (t(355) = 2.89, p = 0.004) while controls did not
(t(307) = 0.37, p = 0.712; paired t tests).
In addition to testing our main hypotheses, we conducted a series
of exploratory analyses. First, we tested whether the treatment effect
was stronger in certain conditions (that is, as in Study 1, when partici-
pants were successful in their bets). While we did not observe signifi-
cant interaction effects between the experimental condition and the
bet winnings for climate concern (B = 0.009, SE(B) = 0.011, β = 0.04,
t = 0.75, p = 0.455) or support (B = 0.007, SE(B) = 0.014, β = 0.04, t = 0.55,
p = 0.583), we found a significant moderation for climate knowledge
(B = 0.182, SE(B) = 0.053, β = 0.41, t = 3.44, p < 0.001). Notably, we
observed a significant interaction between the treatment and politi-
cal ideology. The treatment was more effective at increasing support
for remedial action among more conservative participants (B = 0.077,
SE(B) = 0.036, β = 0.13, t = 2.14, p = 0.033). All treatment effects were
independent of initial climate concerns, suggesting that participants
at all levels of climate concern were equally affected by their involve-
ment in the climate prediction market.
In line with findings on motivated reasoning1,6,31, we observed a
marginally significant relationship between political ideology and the
percentage of bets that superficially align with climate change (r = −0.10,
t = −1.90, p = 0.058). Testing for correlations between political ideol-
ogy and outcomes (number of bets won, r = −0.02, p = 0.526; and total
amount earned, r = −0.004, p = 0.921) or betting behaviours (total bets
placed, r = −0.01, p = 0.828; and total amount spent, r = 0.05, p = 0.192)
did not show any significant correlations (Supplementary Fig. 8). Accord-
ingly, political ideology did not influence participants’ engagement with
the markets, confidence in their bets or prediction accuracy.
Implications of climate prediction markets
In line with existing theoretical arguments about the power of climate
prediction markets32, our findings from two field studies suggest that
participating in such markets can influence people’s attitudes towards
climate change. Specifically, we show that participants who bet on
climate-related events reported higher levels of concern about climate
change, showed higher levels of support for remedial climate action
and had higher levels of knowledge on climate issues. While the positive
impact of our intervention on attitudes was conditional on betting suc-
cess in Study 1, it was unrelated to earnings in Study 2. This discrepancy
might, in part, be explained by the fact that the participants in Study 1
were recruited to be highly polarized in their views on climate change.
The effects of our intervention are small, with our experimen-
tal condition explaining between 1% and 7% of the variance in the
post-survey responses regarding concern, support and knowledge.
However, we argue that our intervention offers a meaningful tool for
behaviour change. Prior work has suggested that when considered at
scale, small effects can turn into highly impactful outcomes33. Further,
our intervention results in positive attitude shifts across the entire
political spectrum. Neither political ideology nor people’s prior views
on climate change moderated the effect. The only exception to this lack
of moderation by political ideology was the shift in climate support in
Study 2, where the intervention was stronger among more conserva-
tive participants. The success of the intervention is promising given
that prior works have reported adverse reactive behaviour among cli-
mate sceptics targeted with attempts to shift their climate views12,34–37.
4
2
6
Climate concern (post)
1
2
4
3
5
7
6
Believer
Sceptic
1
2
3
4
5
6
7
Treatment
1
2
4
3
5
7
6
Control
Climate concern (pre)
Increased climate concern
(relative to pre-survey)
Increased climate concern
(relative to pre-survey)
∆climate concern (post–pre)
Control
Treatment
Condition
−0.10
0
0.10
n = 73
n = 70
Fig. 2 | Distributions of climate beliefs before and after participating in the
climate market. Taking the average of the three climate concern questions
(Supplementary Fig. 4), we see that among the treatment (t(68) = 0.14, p = 0.890,
two-tailed paired t test; left) and control (t(70) = 0.22, p = 0.830, two-tailed paired
t test; centre) groups, there is no significant change in concerns following a
month of waiting/betting (markers above the diagonal). Self-described believers
and sceptics are marked by different symbols. Participants’ group designation
aligns with the reported answer to the survey questions with the majority of
believers scattered at the top-right of the panels (Supplementary Fig. 5). Right
panel depicts the average difference concern score among treatment and control
conditions between the pre- and post-surveys. Error bars depict standard errors
(t(106) = 0.12, p = 0.901, two-tailed independent t test).

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We propose that even if the participation in a climate prediction market
is limited, the media accounts about market valuations, the predic-
tion outcomes and the dissemination of knowledge that is derived
from the markets may yield an increased shift in concerns in the
larger population.
While it is difficult to translate the effects in field studies to
population-level outcomes, there are some metrics that could be
impacted by interventions such as ours. For example, if the predic-
tion market in Study 1 was scaled to 1,000,000 climate believers and
sceptics, and all believers decided to invest their annual earnings
from, say, US$500 in market money (US$25, on average, if applied
to our results) into countering climate change, this would result in
an estimated US$25,000,000 of additional funding for climate solu-
tions. Note that this amount could quickly increase when considering
highly motivated players that might have far greater yields than the
5% earnings observed for the average believer in Study 1. Similarly,
given that participation in climate markets such as the one in Study 2
yields an increase in climate concern, support and knowledge, such an
intervention among a representative subset of the population could
yield a shift in attitudes among millions12 of individuals (Box 1 provides
implementation details).
The majority of previous attempts at getting people to update
their existing position on climate change focused on highlighting sci-
entific consensus37–41, neutralizing partisan conflicts34–37 or appealing
to norms31,42–45. The success of a number of those efforts was driven
primarily by increasing knowledge and providing information, which,
in turn, helped shift perspectives. Some of the challenges in previous
studies have been attributed to: (1) motivated reasoning (that is, rejec-
tion of new information that contradicts standing beliefs6), (2) desire
to signal social identity within a group by clinging to information that
fosters collective homogeneity46, (3) active efforts to foster uncer-
tainty about climate science47. Our intervention offers a solution to all
three of these challenges by: (1) making motivated reasoning costly,
(2) anonymizing people’s decisions (thereby protecting their posi-
tion within a group of climate sceptics, for example) while conveying
aggregated public opinion and (3) creating higher levels of certainty
by having people actively engage with scientific sources. Addition-
ally, because the change in attitudes is intrinsically driven, it has the
potential to be less threatening to one’s identity and hence more sus-
tainable. Together, these features might allow people to engage with
climate-related topics in a way that is less polarizing and less prone to
partisan interpretation4,34–37.
Conservative
Liberal
1
2
3
4
5
6
7
Increased climate concern
(relative to pre-survey)
Treatment
a
b
Control
Treatment
Control
Increased climate concern
(relative to pre-survey)
Increased climate support
(relative to pre-survey)
Increased climate support
(relative to pre-survey)
2
4
6
2
4
6
2
4
6
Climate concern
Climate concern (post)
Climate concern (pre)
∆climate concern (post–pre)
Climate support
2
4
6
2
4
6
Climate support (pre)
2
4
6
Climate support (post)
–0.05
0
0.05
0.10
*
0.15
∆climate support (post–pre)
–0.05
0
0.05
0.10
0.15
Control Treatment
Condition
Control Treatment
Condition
+
n = 356
n = 308
n = 356
n = 308
Fig. 3 | Effects of condition on climate concern and support. a,b, Participants
concern (a) and support (b) before (x axis) and after (y axis) the prediction
market. Right column depicts the shifts in concern across control and treatment
conditions, calculated as the average difference scores across conditions
between the post- and pre-surveys. Error bars depict standard errors (*ΔConcern:
(651) = 2.27, p = 0.024, two-sided independent t test, +ΔSupport: t(661) = 1.82,
p = 0.069, two-sided independent t test).

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Our findings contribute to the existing knowledge on behaviour
change, both inside and outside of the climate domain. First, they align
with existing empirical results on how betting can serve as a tool for
boosting engagement and behaviour change. For example, participat-
ing in sports prediction markets was shown to drive engagement with
athletics48, and trading stocks of companies increases people’s con-
sumption of news related to those companies49. Second, the findings
speak to a growing body of work suggesting that reducing the ‘distance’
(psychologically, temporally or spatially) to a problem can lead to shifts
in attitudes and behaviours37,50,51. For example, people who live closer
to coastlines, where the effect of climate change is more concrete,
express greater concern about climate change and higher support for
regulating carbon emissions52, although unlike our intervention, this
may not hold for sceptics53. While our intervention does not change
the physical setting of participants, continuous engagement with
tangible climate-related events may reduce the psychological distance
to climate change and make its impact appear more imminent. Third,
our findings align with simulations suggesting that participation in
climate prediction markets should foster alignment with scientific
climate consensus32.
Limitations
Our studies had a number of limitations. First, given that these were field
experiments involving real-time responses, the results are impacted by
ongoing events (that is, actions taken by other participants, news cycle
or saliency of climate-related events). In Study 2, for example, climate
events dominated the news during our pre-survey period (including a
50-year record high heatwave in Europe), which probably has impacted
our baseline climate concern and support levels. This might have made
it harder to see bigger increases in concern in the post-survey. By virtue
of their realistic nature, our studies produce findings of high ecologi-
cal validity and the results might vary depending on when the studies
are conducted. Future climate prediction market studies should rep-
licate our findings across multiple time windows to corroborate the
outcomes’ generalizability.
Second, given that the participants were recruited based on
location and climate beliefs, our results reflect the behaviour of US
participants and not necessarily the world population. Indeed, the
polarization of US citizens with respect to climate change is larger
than in other countries54. This polarization might have made it more
difficult to shift concerns with our prediction markets, suggesting that
our results could be a conservative estimate of the effect size elsewhere.
Third, we cannot speak to the exact mechanisms of our effects.
Betting behaviour is the reflection of a complex combination of fac-
tors, including: (1) participants’ view/knowledge on topics, (2) their
confidence, (3) their risk tolerance, (4) their understanding of the
market forces, (5) the amount of time participants have to do research
and place bets, (6) the availability of funds, (7) the likelihood that
others would take the opposite position of a prediction (in Study 1)
and (8) the available information on the outcomes (that is, more data
were available as the settle dates approached, in Study 1) and other
psychological mechanisms. Future research could investigate these
mechanisms individually.
Fourth, our limited study duration imposed a stringent cap on
the temporal horizon of predictions. This cap aligns poorly with the
longer timescale of climate change. We could not, for example, look
into notable changes in Earth’s temperature within the time limit. This
limitation forced us to generate climate predictions with large spatial
domains (that is, multiple cities) or comparison to historical events. The
uncertain relationship between near-term events and outcomes caus-
ally related to global warming inevitably caused some of our markets to
reflect weather events rather than climate events. An implementation
of climate prediction markets on a longer period (that is, years) would
allow for long-term predictions and understanding of the effect of new
information on these predictions, irrespective of the temporal horizon
(that is, predictions about the year 2100 can be updated far ahead of
their settle date if new information in, for example, 2025 suggests a
need for change of bet values). In fact, when Study 2 concluded, we
asked participants to make predictions that span years into the future
(Supplementary Table 19), which could be analysed when they settle
(data available along with our Supplementary Information).
Fifth, our studies were limited to a financial allotment of US$20
per participant, capping motivation and outcomes. Participants were
limited to using their allotted amount and, correspondingly, partici-
pants who lost much of their income early were effectively excluded
from further activity (and presumably less engaged with the study).
The fact that participants did not invest their own money may have
changed their overall motivation compared to prediction markets in
0
25
50
75
100
2.5
5.0
7.5
Climate knowledge (pre)
Count
Control (n = 308)
Treatment (n = 356)
5
10
15
20
Climate knowledge (post)
15.5
20.5
5.43 ± 1.49
5.60 ± 1.53
t(642) = –1.45, p = 0.147, two-sided t test
10.00 ± 3.20
8.41 ± 2.54
t(657) = 7.11, p < 0.001, two-sided t test
10.5
Fig. 4 | Climate knowledge increases after participating in a climate
prediction market. Participants’ knowledge was evaluated by comparing
the treatment and control groups’ knowledge in the pre-survey (left; n.s.) and
post-survey (right). Vertical lines are the mean of the corresponding color
distribution. n.s., not significant.

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public exchanges. However, prior work has suggested that using virtual
money may be as effective as real money55. We argue that this limitation
may indicate that a real-world prediction market could in fact amplify
the outcomes identified.
Taken together, these limitations suggest that while our work pro-
vides an initial feasibility test for climate prediction markets, further
research should examine the markets’ ability to shift attitudes persistently
across a more diverse set of samples. Specifically, future work should
investigate whether changes in concern, support and knowledge are
sustained long term and whether continuous participation in climate
markets solidifies those changes. Additionally, further analyses of the
bets could focus on the positions taken by individuals as dependent
observations to test whether certain outcomes affect future attitudes or
bets (that is, losing multiple bets in sequence leading to less extreme bets).
Finally, we strongly advocate for replication of our results using
large-scale prediction markets, implemented over a longer period in
an open, non-experimental setting56. This would allow market forces to
strengthen the effects and could lead to widespread attitude change.
Conclusion
This study offers empirical evidence for the ability of prediction markets
to change people’s attitudes about climate change. The engagement
with climate prediction markets in a domain that is uniformly quantita-
tive and less polarizing than politics could not only support existing
methods to change climate concerns4,13,51,57 but also act as an ultimate
polling tool to help scientists, activists and politicians aggregate public
opinion about trends, policy preferences and future scientific predic-
tions. It has not escaped our notice that the powerful financial instru-
ment proposed here could be used in other topics of controversy where
an agreed-upon arbiter of truth could allow individuals to reflect their
views through market economics rather than publicly stated opinions.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41558-023-01679-4.
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Article
https://doi.org/10.1038/s41558-023-01679-4
Methods
Study 1
Participants. A total of 160 participants were recruited for the study.
Participants were recruited online using Prolific Academic and through
the Reddit ‘Climate Change sceptics’ group. Participants were screened
on Prolific using two questions: (1) ‘What is your nationality?’ (only
participants who answered ‘United States’ to this question were eligi-
ble to participate) and (2) ‘Do you believe in climate change?’ (people
could answer: ‘Yes’, ‘No’, ‘Don’t know’ or ‘Not applicable/rather not
say’; an equal number of people saying ‘Yes’ or ‘No’ were recruited).
Participants’ location within the United States varied and spanned areas
that are deemed high and low for their support for climate change sci-
ence59 (Supplementary Fig. 1). To ensure that participants beliefs about
climate change were consistent with the earlier Prolific screening, we
added an additional filter question (‘Global warming refers to the idea
that the world’s average temperature has been increasing over the past
150 years and may be increasing more. Do you think that global warm-
ing is happening?’; Answers: ‘Yes’, ‘No’ and ‘Don’t know’). Only people
who responded ‘Yes’ or ‘No’ were included in the study.
Seventeen participants were excluded from the analysis, broken
down as follows: three were excluded because they did not complete
the required surveys, five because they failed an attention check ques-
tion in either the pre-/post-survey and nine because they did not fulfil
the requirement to use the entirety of the allotted US$20. Of the 143
remaining participants, 73 were used as controls (35 believers, 38
sceptics) and the remaining 70 were used as the treatment group (35
believers, 35 sceptics). Supplementary Table 5 provides a breakdown
of all participants’ demographics.
Participants in the control group received US$5 for completing
the pre-survey and an additional US$5 for completing the post-survey.
Participants in the treatment group received the same renumeration
for these surveys, along with additional US$20 to spend in the climate
prediction market (Supplementary Fig. 3 provides bet topics distribu-
tion). Participants were instructed to use the full amount for climate
predictions. At the end of the study, participants received their earnings
in the climate prediction market. Participants who lost all their US$20
allotment during the betting period received only a US$10 participation
fee. In total, participants in the treatment group could earn anything
from US$10 (participation fee) to a maximum of US$650 (participation
fee and their earning from bet wins).
Experimental procedure. On the day of the study initiation, partici-
pants received a message instructing them to complete a pre-survey. At
the end of the survey, they were given a personalized link to a web-based
online climate betting site. Upon logging in to the site, they were pre-
sented with a number of climate betting markets and could take a posi-
tion on any number of them (Fig. 1). In addition, participants could
choose to trade a position with other participants. The number of avail-
able markets changed daily based on old markets closing and new mar-
kets opening. Participants could place multiple bets on the same market
and could trade continuously before the bet’s settle date and time.
During the betting period, participants could log in to the pre-
diction market site whenever they wished, monitor their currently
available funds, view the available markets, make bets or trade posi-
tions. The market mechanism was ‘double auction’ (Supplementary
Information), which required two participants to take opposite bets
such that the sum of two bets was US$1 (that is, if one participant chose
to wager US$0.60 that a ‘Yes’ bet will occur, only when another partici-
pant wagered US$0.40 that a ‘No’ on the outcome would a contract be
initiated). If no participant was willing to take the opposite wager, the
offer remained pending until the participant making the offer chose
to revoke it. The manifested value of each market at any given moment
was that of the last ‘Yes’ transaction to occur. That is, if a participant
made a bet for US$0.82 that the average methane level in October 2018
will be the highest on record and another participant took the opposite
position at US$0.12, then all participants saw the current market value
as US$0.82. Accordingly, the values of markets represented the aggre-
gated stable amount of money people were agreeing to wager on each
topic. Naturally, as the settlement date of markets approached, the
bets were likely to converge to the probability (0…100) of the correct
outcome (that is, if the market asked whether the number of disasters
in a certain location be more than 10 by a certain date, and a few days
before the closing time, a number of disasters already reached 9, the
likelihood of a ‘Yes’ bet was higher). The betting period was initiated
on 9 September 9 2018, and lasted until 11 November 11 2018. When
the betting period was complete, participants were instructed to com-
plete a post-survey. Once participants completed the post-survey,
they were paid for their participation in the entire study. The pre- and
post-surveys included a variety of questions (Supplementary Infor-
mation provide all questions), but the main focus of the study was the
subset of questions pertaining to the concern about climate change.
To ensure the site’s robustness to large-scale use and to reduce
the risk of technical issues jeopardizing the real-time experiment, we
ran a pilot test of the site for two months before the experiment on a
smaller group of participants.
Study 2 was similar to Study 1 in its design, with the following
deviations: (1) the criteria for exclusion in Study 1 was stricter (that is,
participants were asked to use the full amount of money allotted to
them), (2) the betting period in Study 1 was longer and bets were not
released daily but rather intermittently, (3) the participant population
for Study 1 was selected such that the pool was more polarized, (4) the
control group for Study 1 did not participate in an alternative predic-
tion market, (5) the treatment group’s bets in Study 1 occurred in a
double auction, which pitted the believers and sceptics against each
other with predictions occurring only when two participants claimed
opposite sides of a bet such that the sum of the positions was US$1 (Sup-
plementary Information provides an explanation of the double-auction
fulfilment method), (6) participants in Study 1 could trade their bets
in the market as the settle date was approaching based on the value of
the trade at the time, (7) participants in Study 1 did not have to take a
position on a bet as soon as it appeared on the portal but could choose
to make a decision to enter as more information became available (the
option price presumably reflected the information availability and
outcome certainty), (8) participants in Study 1 could take contrary
positions on the same bet or hedge their bets with a variety of positions.
Study 2
Participants. Participants were recruited through an online panel,
Prolific Academic. Our target sample for the start of the study was 1,000
participants with anticipated attrition rates of approximately 30–40%
over the course of the entire assessment period. To obtain this initial
sample, we recruited 1,754 participants whose native language was
English and who currently resided within the United States. We used
Prolific’s representative sample criteria to ensure that the sample was
generalisable. We excluded participants who took less than two minutes
to complete the survey and who failed an attention check embedded
in the survey (n = 134).
All participants were asked a series of questions about their con-
cerns pertaining to climate change, their support for climate action
and their knowledge on basic climate-related topics (Measures). We
further excluded participants whose answers were at ceiling (by calcu-
lating the sum across four ‘Concern’ questions with a score of 1–7 each
and excluding participants with a score of 27 or 28, out of 28; n = 615;
Supplementary Information).
A total of 1,005 participants met all inclusion criteria. Participants
were randomly assigned to a treatment (n = 524) or control condition
(n = 481; Supplementary Table 6). Participants in the treatment condi-
tion were told that they would participate in a four-week-long climate
prediction market, while participants in the control condition were
told about their participation in a sports and entertainment prediction

Nature Climate Change
Article
https://doi.org/10.1038/s41558-023-01679-4
market (Supplementary Table 6 provides evidence that the treatment
and control groups randomization assignments did not significantly
differ from one another).
Each participant was allotted US$20 to use for bets throughout
the study. We considered participants’ study records complete if they:
(1) placed at least 15 bets and spent at least US$10 from their allocated
wages during the prediction period, (2) completed the post-survey at
the end of the study, which included the same climate-related meas-
ures (that is, concern, support and knowledge) as the pre-survey. After
excluding participants who did not meet these criteria, we were left with
an analysis sample consisting of 664 participants (34% overall attri-
tion rate; 32% in the treatment group, 36% in the control; x2(2) = 1.71,
p = 0.191, n.s.). Participants were compensated with a fixed sum of US$11
for completing the pre- and post-surveys and a variable additional
amount depending on their earnings in the prediction period.
Experimental procedure. The study consisted of three main building
blocks: (1) the pre-survey that measured participants’ concern about
climate change (climate concern), their support for possible solu-
tions (climate support) and their knowledge on climate issues (climate
knowledge) before the intervention, (2) a five-week-long prediction
market and (3) a post-survey that captured climate concern, support
and knowledge following the intervention. Participants were recruited
between 17 July and 21 July 2022 and completed the pre-survey as part
of the initial screening procedure. After exclusion, participants were
randomly assigned to the five-week climate (treatment) or sports/
entertainment (control) prediction markets. The betting period started
on 1 August 2022 and concluded on 4 September 2022. The final bet was
settled on 6 September 2022. The post-survey was sent to participants
on 8 September 2022 and was closed on 14 September 2022, at which
point all participants were paid (Fig. 1).
Each participant received a unique login identifier that allowed
them to use a personalized version of study surveys. Every day at 10:00
all participants received an email through the study messaging system
indicating that a new bet was available on the prediction portal. The
message included a link to the prediction portal.
Upon receipt of the daily reminder, participants had 20 hours to
enter the portal (Fig. 1), look at that day’s bet and decide whether to make
a prediction. Once participants logged in to the portal, they were greeted
with their personal identification and a summary of their personal study
metrics. The metrics were: how many bets they had already placed, how
much money they currently held in their wallet, how much money they
had in bets escrow (that is, bets awaiting resolution) and their total earn-
ings up to that point. Below the metrics, participants saw a summary of the
bets that already materialized and their outcome. Below this information,
participants saw the day’s new bet (that is, ‘Will the number of wildfires
in California exceed 5,500 by August 8, 2022?’) alongside the bet’s settle
date/time (that is, ‘August 8, 2022, 23:59 EDT’), the source for deter-
mining the outcome (that is, ‘https://www.fire.ca.gov/incidents/2022’)
and (where possible) a graph of the history of the variable being bet on,
showing the settle date of the current bet with respect to that graph.
Participants were then asked to indicate whether they wanted to
abstain from betting, predict ‘Yes’ or predict ‘No’. If participants elected
to make a Yes/No prediction, they advanced to the next screen where
they were asked to select the bet amount. Depending on their level of
confidence, participants could bet any amount between US$0.01 and
US$1. After participants determined their position and bet wager, they
were asked to confirm their decision or restart their decision. Once the
participants confirmed their decision, the bet was locked for the day
and they were not able to alter their bet.
Measures
Climate concern. We measured people’s concern about climate change
in both the Pre- and Post-survey using the following four items: 1) ‘Do
you think that global warming/climate change is happening?’ (Definitely
not—Definitely yes, 2) ‘Do you think global warming/climate change
is the result of human activities?’ (Definitely not—Definitely yes), 3)
‘How much risk do you believe global warming/climate change poses
to humanity’s health, safety and prosperity?’ (None at all—Extremely
high), and 4) ‘Some people say that global warming/climate change
is simply a scam. What do you think about this?’ (Strongly disagree—
strongly agree; reverse coded). The measure was adopted from work
by Weber and colleagues54. Responses were recorded on a 7-point scale.
With a Cronbach’s alpha of 0.93 in both the Pre- and Post-surveys, the
internal consistency of our measure was found to be excellent.
Support for climate change solutions. We measured people’s support
for climate change solutions in both the pre- and post-survey using the
following three items: (1) ‘Addressing global warming/climate change
should be a priority of the government’ (strongly disagree—strongly
agree), (2) ‘I feel personally responsible to help slow down global warm-
ing/climate change’ (for example, by making changes to my lifestyle or
paying higher taxes) (strongly disagree—strongly agree) and (3) ‘Some
people say that climate change is real, but that the cost of fixing it today
might not be worth the investment (that is, that the cost of fixing it today
is higher than the cost of the damages caused by it)’ (strongly disa-
gree—strongly agree). Responses were recorded on a seven-point scale.
With a Cronbach’s alpha of 0.84 in the pre- and 0.87 in the post-survey,
the internal consistency of our measure was found to be good.
Climate knowledge. The questions for climate knowledge differed
between pre- and post-survey. The pre-survey asked ten relatively
generic multiple-choice questions (that is, ‘How many major layers does
Earth’s atmosphere have?’ Or ‘What is the primary effect of greenhouse
gasses?’. The post-survey, on the other hand, asked a more compre-
hensive set of questions that were directly related to knowledge about
climate change (that is, ‘When does a tropical disturbance become a
tropical storm and gains a name?’ or ‘What percentage of heat from
global warming has the ocean absorbed in the past 40 years?’; Sup-
plementary Tables 2, 4, 18 and 19 provide all questions).
Socio-demographic control variables. We collected information
about a wide range of participants’ socio-demographic characteristics.
These included: age, gender, ethnicity, education, employment status,
income, religious beliefs, political ideology and number of children
(Supplementary Tables 18 and 19).
Ethics statement
All participants in Study 1 and Study 2 signed an online consent form
upon initial engagement with the pre-survey. Study 1 protocols were
approved by Northwestern University’s Institutional Review Board
(STU00206273). Study 2 protocols were approved by Columbia Uni-
versity’s Institutional Review Board (AAAU2501).
Reporting summary
Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.
Data availability
The data generated during the work are available at https://doi.org/
10.17605/OSF.IO/PH72Y.
Code availability
The codes used for the analyses are available at https://doi.org/
10.17605/OSF.IO/PH72Y.
References
59.	 Marlon, J., Howe, P., Mildenberger, M., Leiserowitz, A. & Wang,
X. Yale Climate Opinion Maps 2019 (Yale Program on Climate
Change Communication, 2019).

Nature Climate Change
Article
https://doi.org/10.1038/s41558-023-01679-4
Acknowledgements
We thank M. Kim and A. Yu for assistance with managing the prediction
market and maintaining the study throughout the betting periods;
A. Mishra and I. Katz for designing and coding an initial version of
the online prediction markets; J. McCoy for discussion on prediction
market utility; B. B. McShane, S. Franconeri, J. N. Druckman and
A. Coughlan for comments on the manuscript; E. Weber for support
with climate concern assessment; G. Schmidt, K. Hayhoe, D. Horton,
E. Berlow and K. Marvel for help with identifying climate markets; and
R. Behnam and members of the US Commodities and Futures Trading
Commission for discussions on federal implementation of climate
prediction markets. This work was funded by the Columbia University
Tamer Center for Social Enterprise (M.C. and S.C.M.) and by the
Northwestern Institute on Complex Systems (M.C. and M.A.M.).
Author contributions
All authors equally designed the research, performed the research,
analysed the data and wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41558-023-01679-4.
Correspondence and requests for materials should be addressed to
Moran Cerf, Sandra C. Matz or Malcolm A. MacIver.
Peer review information Nature Climate Change thanks
Paul Stern, Michael Vandenbergh and the other, anonymous,
reviewer(s) for their contribution to the peer review
of this work.
Reprints and permissions information is available at
www.nature.com/reprints.

    Policy Brief:

Answer:
**Participating in a climate futures market increases support for costly climate policies**

**Policy Problem**Over a third of the US population believes that the seriousness of global warming is exaggerated, suggesting that politicians proposing to enact costly policies to address it may be at risk of losing their next election. While concern varies elsewhere, there is a large gap, globally, between the level of concern exhibited and the level of support needed to meet climate change mitigation goals. A market-based mechanism, or climate prediction market, could help close this gap. At scale, such a mechanism could also generate a new branch of the financial information sector dedicated to predicting the consequences of climate change, which could have substantial economic value for steering mitigation measures. Further, provided that each bet has a pre-determined arbiter for the outcome settlement, this market-based resolution mechanism could be useful in other topics of potential controversy outside of climate action.

**Scientific Research Findings**Participation in a climate prediction market, where individuals make predictions about climate futures and earn/lose money based on their forecasting accuracy, increases concern about global warming, support to mitigate the risks of climate change and knowledge about climate issues. This is true across levels of initial belief in climate change and political ideology. In one study (with a polarized group of climate believers and sceptics), the shift in perspective was contingent on winning (people who made money in their predictions also shifted their beliefs), whereas in a study including participants with more moderate views, the changes occurred independent of winning. This research provides a practical way to increase people’s concern about climate change, as well as a powerful tool for policymakers to poll public opinion about climate issues, test potential future policies, inject private money to a new financial instrument and even use potential market gains towards climate solutions.

**Scientific Research Study Methods**Participants completed surveys before and after engaging in a prediction market. The surveys measured their climate concerns, support for climate action and climate knowledge. Between the surveys, participants were randomly assigned to either engage in a climate prediction market (treatment) or a control group. In study 1 (n = 143), the control group sat idle, while in study 2 (n = 664), the control group engaged in a sports and entertainment prediction market. During the prediction period, participants in the climate prediction market made bets about future climate outcomes (that is, “Will the number of wildfires in California exceed 5,500 by August 8, 2022?”).

**Policy Implications**• Participation in a climate prediction market, where individuals make bets on outcomes and earn money when they are correct, results in more climate concern, support and knowledge.
• Climate prediction markets can be used by policymakers as a highly accurate polling mechanism.
• The market, at scale, can infuse private money into climate efforts. It can act as an exchange where those who hold inaccurate beliefs sponsor policies with the money they lose.
• The market allows policymakers to leverage the wisdom of the crowd to estimate future outcomes based on current bet values. Thus, it ameliorates the brain’s challenge in valuing distant consequences.
• The market acts as an agreed-upon referee and, through its anonymity, protects social standing in groups where alignment with science on politicized issues results in exclusion.