CoChat
Sign Up
Cited by 185Experimental Behavioral Economics StudiesEvolutionary Game Theory and CooperationClimate Change Policy and Economics

The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.

Communicating sentiment and outlook reverses inaction against collective risks

Open access
Wang, Zhen, Jusup, Marko, Guo, Hao, Shi, Lei, Geček, Sunčana, Anand, Madhur, Perc, Matjaž, Bauch, Chris T., Kurths, Jürgen, Boccaletti, Stefano, Schellnhuber, Hans Joachim·2020·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1922345117PMC PMC7395434
Open this paper in CoChat — free accountOpen PDF

Abstract

Significance: Collective risks trigger social dilemmas that require balancing selfish interests and common good. One important example is mitigating climate change, wherein without sufficient investments, worldwide negative consequences become increasingly likely. To study the social aspects of this problem, we organized a game experiment that reveals how group size, communication, and behavioral type drive prosocial action. We find that communicating sentiment and outlook leads to more positive outcomes, even among culturally heterogeneous groups. Although genuine free riders remain unfazed by communication, prosocial players better endure accumulated investment deficits, and thus fight off inaction as the failure looms. This suggests that climate negotiations may achieve more by leveraging existing goodwill than persuading skeptics to act.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.1922345117

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1. Communication increases the success frequency by improving investment patterns. (A) Compared to control groups, the success frequency of treatment groups is considerably higher, irrespective of group size, although with increasing group size, the overall success frequency decreases. We confirmed the statistical significance of these observations by fitting all nine log-linear models to the data (SI Appendix, Result 1 and Table S2). The best-fitting model is a conditional independence model with interactions group type × success and group size × success included. Furthermore, a considerably smaller success-vs.-failure gap in the average per capita investment in one round posted by treatment groups relative to control groups hints at different investing patterns. (B) Distributions of the average per capita investment in one round differ between control and treatment groups. This is evidenced by a two-way ANOVA test (SI Appendix, Table S3), which highlights group type as a significant factor (F statistic 20.21, P <10⁻⁵). Interestingly, factor group size (F statistic 1.67, P = 0.19) and interaction group type × group size (F statistic 0.50, P = 0.61) are insignificant. A robust version of the two-way ANOVA test (SI Appendix, Table S3) and the corresponding post hoc comparisons (SI Appendix, Table S4) also confirm these results.
Fig. 1. Communication increases the success frequency by improving investment patterns. (A) Compared to control groups, the success frequency of treatment groups is considerably higher, irrespective of group size, although with increasing group size, the overall success frequency decreases. We confirmed the statistical significance of these observations by fitting all nine log-linear models to the data (SI Appendix, Result 1 and Table S2). The best-fitting model is a conditional independence model with interactions group type × success and group size × success included. Furthermore, a considerably smaller success-vs.-failure gap in the average per capita investment in one round posted by treatment groups relative to control groups hints at different investing patterns. (B) Distributions of the average per capita investment in one round differ between control and treatment groups. This is evidenced by a two-way ANOVA test (SI Appendix, Table S3), which highlights group type as a significant factor (F statistic 20.21, P <10⁻⁵). Interestingly, factor group size (F statistic 1.67, P = 0.19) and interaction group type × group size (F statistic 0.50, P = 0.61) are insignificant. A robust version of the two-way ANOVA test (SI Appendix, Table S3) and the corresponding post hoc comparisons (SI Appendix, Table S4) also confirm these results.CC BY · PubMed Central
Fig. 2. Communication prevents giving up. Successful control and treatment groups follow similar paths to success. Group size is an issue because medium- and large-sized groups typically run substantial deficits after the first five rounds; to succeed, these groups need to exert a concerted effort over the last five rounds. More important, however, is that the paths of failed control and treatment groups diverged from one another. The former groups seemed to give up because their negative bias was unlikely to be random. This was evidenced by larger end deficits than those of 95% of unsuccessful random-playing groups (black significance curves). Failed treatment groups, unlike failed control ones, did not give up. They exhibited the same concerted effort as successful groups, and typically ended the game with small, unfortunate deficits. The ensemble-averaged paths are shown here. The paths of individual groups are displayed in SI Appendix, Fig. S7.
Fig. 2. Communication prevents giving up. Successful control and treatment groups follow similar paths to success. Group size is an issue because medium- and large-sized groups typically run substantial deficits after the first five rounds; to succeed, these groups need to exert a concerted effort over the last five rounds. More important, however, is that the paths of failed control and treatment groups diverged from one another. The former groups seemed to give up because their negative bias was unlikely to be random. This was evidenced by larger end deficits than those of 95% of unsuccessful random-playing groups (black significance curves). Failed treatment groups, unlike failed control ones, did not give up. They exhibited the same concerted effort as successful groups, and typically ended the game with small, unfortunate deficits. The ensemble-averaged paths are shown here. The paths of individual groups are displayed in SI Appendix, Fig. S7.CC BY · PubMed Central
Fig. 3. Interplay between cooperators, free riders, and altruists determines a group’s success. (A) We uncovered three behavioral types using a clustering algorithm (SI Appendix, Result 4 and Fig. S10). Cooperators mostly invest two units of capital, thus neither running large deficits nor surpluses. Free riders, by comparison, frequently avoid investing at all, which causes large deficits throughout the game. Finally, altruists create large surpluses by investing mostly four units of capital. (B) The abundance of cooperators and altruists is somewhat lower among control than treatment groups. Free riders, however, dominate in control groups that failed reaching the target. (C) Investment habits depend on the round played, while also being considerably different between behavioral types. An ANCOVA test (SI Appendix, Table S7) confirmed these observations by showing that behavioral type (F statistic 658, P ≈ 0) and round (F statistic 66.4, P <10⁻⁷) are significant factors, as is their interaction behavioral type × round (F statistic 34.5, P <10⁻⁷). Specifically, the population mean investment of 1.96 (95% CI [1.884,2.035]) by cooperators is significantly higher than 0.77 (95% CI [0.698,0.849]) by free riders, but significantly lower than 2.96 (95% CI [2.889,3.040]) by altruists. Furthermore, free riders invest slightly less as the time passes, but this downward trend is insignificant (slope −0.022; 95% CI [−0.048,0.0045]). Cooperators and altruists invest significantly more as the time passes, where the rate of increase for altruists (slope 0.152; 95% CI [0.126,0.178]) is significantly higher than for cooperators (slope 0.080; 95% CI [0.053,0.106]). (D) MCA (SI Appendix, Methods and Table S8) of the between-round questionnaire, combined with the clustering results, helped to understand how the three behavioral types communicate (SI Appendix, Result 5 and Fig. S11). Different from free riders, cooperators and altruists share the optimism of reaching the target (answer “yes” to Q1), are less satisfied with the current situation (answer “no” to Q2 and Q3), and demand more action (answer “yes” to Q4). Principal dimensions in MCA are determined algorithmically and need not have a clear interpretation (SI Appendix, Fig. S12 and Table S9).
Fig. 3. Interplay between cooperators, free riders, and altruists determines a group’s success. (A) We uncovered three behavioral types using a clustering algorithm (SI Appendix, Result 4 and Fig. S10). Cooperators mostly invest two units of capital, thus neither running large deficits nor surpluses. Free riders, by comparison, frequently avoid investing at all, which causes large deficits throughout the game. Finally, altruists create large surpluses by investing mostly four units of capital. (B) The abundance of cooperators and altruists is somewhat lower among control than treatment groups. Free riders, however, dominate in control groups that failed reaching the target. (C) Investment habits depend on the round played, while also being considerably different between behavioral types. An ANCOVA test (SI Appendix, Table S7) confirmed these observations by showing that behavioral type (F statistic 658, P ≈ 0) and round (F statistic 66.4, P <10⁻⁷) are significant factors, as is their interaction behavioral type × round (F statistic 34.5, P <10⁻⁷). Specifically, the population mean investment of 1.96 (95% CI [1.884,2.035]) by cooperators is significantly higher than 0.77 (95% CI [0.698,0.849]) by free riders, but significantly lower than 2.96 (95% CI [2.889,3.040]) by altruists. Furthermore, free riders invest slightly less as the time passes, but this downward trend is insignificant (slope −0.022; 95% CI [−0.048,0.0045]). Cooperators and altruists invest significantly more as the time passes, where the rate of increase for altruists (slope 0.152; 95% CI [0.126,0.178]) is significantly higher than for cooperators (slope 0.080; 95% CI [0.053,0.106]). (D) MCA (SI Appendix, Methods and Table S8) of the between-round questionnaire, combined with the clustering results, helped to understand how the three behavioral types communicate (SI Appendix, Result 5 and Fig. S11). Different from free riders, cooperators and altruists share the optimism of reaching the target (answer “yes” to Q1), are less satisfied with the current situation (answer “no” to Q2 and Q3), and demand more action (answer “yes” to Q4). Principal dimensions in MCA are determined algorithmically and need not have a clear interpretation (SI Appendix, Fig. S12 and Table S9).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 33 references

Cited by

Sorted by citations

See all 185

Related

Metadata

Authors
Wang, Z., Jusup, M., Guo, H., Shi, L., Geček, S., Anand, M., Perc, M., Bauch, C. T., Kurths, J., Boccaletti, S., & Schellnhuber, H. J.
Affiliations
  • Northwestern Polytechnical University (CN)
  • Tokyo Institute of Technology (JP)
  • Statistics and Mathematics College, Yunnan University of Finance and Economics, Kunming 650221, China;
  • Ruđer Bošković Institute (HR)
  • University of Guelph (CA)
  • China Medical University (TW)
  • University of Waterloo (CA)
  • Potsdam Institute for Climate Impact Research (DE)
  • Moscow Institute of Physics and Technology (RU)
Corresponding author
  • Wang, Zhen
  • Jusup, Marko
  • Schellnhuber, Hans Joachim
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2020-07-15
Volume
117
Issue
30
Pages
17650-17655
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1922345117
PMC
PMC7395434
OpenAlex
W3042961264
Cited by
185
References
33
Citations by year
2026: 21 · 2025: 39 · 2024: 66 · 2023: 28 · 2022: 13 · 2021: 17 · 2020: 1
FWCI
20.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • University of Maribor digital library (University of Maribor) (submittedVersion, cc-by)
  • Institutional Repository of the Ruđer Bošković Institute (Ruđer Bošković Institute) (submittedVersion, cc-by)
  • RENATE (submittedVersion, cc-by-nc-nd)
  • Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion, cc-by)
  • RENATE (cc-by)
Funders
  • National Natural Science Foundation of China (31700393, 81961138010, 11931015, 11671348, U1803263)
  • Javna Agencija za Raziskovalno Dejavnost RS (J4-9302, P1-0403, J1-9112)
  • Hrvatska Zaklada za Znanost (#IP-2018-01-3150-AqADAPT, IP-2018, IP-2018-01-3150, -2018-01-3150, IP-2018-01)
  • Fundamental Research Funds for the Central Universities (3102019PJ006, U1803263, 81961138010)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Experimental Behavioral Economics Studies
  • Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
Keywords
collective action, free-riding, climate change mitigation, social dilemmas, investment behavior, group size, behavioral types, cooperation, altruism
MeSH terms
Behavior, Communication, Humans, Models, Theoretical, Climate Change

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review