PONE-D-25-04274From self-interest to collective action: The role of defaults in governing common resourcesPLOS ONE

Dear Dr. Montero-Porras,

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Three reviewers provided valuable comments on your manuscript. 

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Please check the reviewer's comments and revise your manuscript according to the comments with point-by-point responses.

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this paper, the authors tested the effects of prosocial and self-serving defaults on extraction levels in a common-pool resource dilemma. The results of an online economic game showed that prosocial defaults caused people to extract less from the common pool and self-serving defaults caused people to extract more. However, these effects were short-lived, disappearing entirely when defaults were removed halfway through the game. The effects were also moderated by Social Value Orientation and risk preferences: for example, the authors found that prosocial defaults were more effective for individualists and risk-averse participants.

The study was described very clearly and the results were easy to follow. I really liked the statistical approach taken in the paper – more studies with repeated games should use GAMs to track changes across rounds! I also assessed the pre-registration, data, and analysis code as part of my review. The pre-registration was comprehensive and clearly laid out the key hypotheses. I was able to reproduce the statistical results of the paper with the data and analysis code (after some changes to file paths in the R file).

That said, I had a few concerns about the paper, mainly related to the discussion of prior literature, the statistical analyses, and the treatment of dropouts.

First, I felt that the discussion of prior literature was scant in places, making it difficult to assess how much of a contribution this paper is making. For example, in the Introduction section, the authors could have discussed how previous psychological work has already studied how the effects of defaults vary depending on individual differences in anxiety and avoidance (Zucchelli et al. 2024), cognitive effort (Ortmann et al. 2023), and mood and personality (Manganari et al. 2022). In the Discussion section, it might also be worth discussing how the results are in line with other studies in behavioural economics, particularly studies showing that the effects of other interventions in repeated games often decay over time (e.g., Chaudhuri & Paichayontvijit, 2017) and go away entirely when removed (e.g., Fehr & Gächter, 2000). Regarding generalising the results to real-world settings, the authors could discuss the use of green defaults in airline carbon offsetting (Berger et al. 2022) and speculate as to whether such interventions might be more effective for more individualist and risk-averse customers. I am aware that the literature on defaults is very large, and this is not a review paper! But I think some extra discussion in places could better couch the current paper and its findings in the existing literature.

Second, I had a few concerns with the analysis approach. While I liked the use of GAMs, the authors should consider including random intercepts for groups in the models in addition to the random intercepts for participants. The nested nature of the experiment means that there will be dependencies within groups. This group-level variation may not be fully captured by the “previous group extraction” fixed effect in the model. Do the results hold when this group-level variation is accounted for with additional random intercepts?

Third, there is the perennial issue of dropouts in online experiments. If I’m understanding correctly, the authors’ pre-registered approach was to exclude any participants who dropped out and any groups containing dropouts. But this is problematic from a causal inference perspective, especially if dropouts are more common in one treatment group compared to others – if this is the case, it becomes difficult to know whether differences between experimental conditions are due to the treatment itself or due to differences in attentiveness and characteristics of participants who drop out. The authors could remedy this in two ways: (1) by analysing whether dropout rates vary systematically across treatment groups, and (2) by running an “intention-to-treat” analysis (McCoy, 2017) that fits the same models but retains observations from groups that contain dropouts. Since there are only a few dropouts, the results will likely not differ all that much, but this analysis would show that the main causal conclusions are unbiased.

Finally, here are some minor additional points to consider:

• Some visual examples of “free-riding” and “cooperation” in the economic game might be helpful for readers, especially those outside of behavioural economics.

• I am assuming that the treatment is at the group-level, such that all participants within a group experience the same treatment. If this is the case, it should be clearly stated in the Methods section.

• Figures 1b and 1c are slightly confusing because the splines go in the opposite direction to the points in Figure 1a. For example, in Figure 1b, the spline goes below the horizontal line, even though the self-serving points are above the control points in Figure 1a. This may confuse readers. A simple fix is to flip the y-axis to capture “Self-serving - Control” instead.

• Four decimal places are used throughout – this is distracting and probably not necessary. Two decimal places would suffice.

In summary, this was a clear and well-described paper that will contribute to the literature on default effects. Please let me know if you have any questions about this review.

Review signed: Scott Claessens (scott.claessens@gmail.com)

References

Berger, S., Kilchenmann, A., Lenz, O., Ockenfels, A., Schlöder, F., & Wyss, A. M. (2022). Large but diminishing effects of climate action nudges under rising costs. Nature Human Behaviour, 6(10), 1381-1385.

Chaudhuri, A., & Paichayontvijit, T. (2017). On the long-run efficacy of punishments and recommendations in a laboratory public goods game. Scientific Reports, 7(1), 12286.

Fehr, E., & Gächter, S. (2000). Cooperation and punishment in public goods experiments. American Economic Review, 90(4), 980-994.

Manganari, E., Mourelatos, E., Michos, N., & Dimara, E. (2022). Harnessing the power of defaults now and forever? The effects of mood and personality. International Journal of Electronic Commerce, 26(4), 472-496.

McCoy, C. E. (2017). Understanding the intention-to-treat principle in randomized controlled trials. Western Journal of Emergency Medicine, 18(6), 1075.

Ortmann, A., Ryvkin, D., Wilkening, T., & Zhang, J. (2023). Defaults and cognitive effort. Journal of Economic Behavior & Organization, 212, 1-19.

Zucchelli, M. M., Gambetti, E., Giusberti, F., & Nori, R. (2024). Use of default option nudge and individual differences in everyday life decisions. Cognitive Processing, 25(1), 75-88.

Reviewer #2: Summary:

The paper reports the results of an experiment conducted on Prolific testing the effects of a pro-social and a self-serving default value for extraction decisions in a Common Pool Resource Dilemma (CPRD) game compared to a control condition without any default. The default was implemented simply as a value pre-selected in a drop-down menu of possible decisions, which participants could change easily if desired. The results indicate significant default effects compared to the control condition, with an interesting asymmetry: the self-serving default led to significantly higher extraction decisions in all five rounds of its implementation, whereas the pro-social default only led to lower extraction decisions in the first three rounds. Both types of defaults did not lead to any significant spillover effects after their removal (i.e., in rounds 6-10). Another interesting finding is that the pro-social default leads participants with individualistic or competitive social preferences (as measured by the SVO measure) to extract less (whereas it has little effect on participants with cooperative and altruistic social preferences). Similarly, the self-serving default leads participants with cooperative or altruistic social preferences to extract more (with smaller effects on participants of individualistic or competitive type). Finally, the paper also finds that risk-averse participants are more strongly influenced by a default.

General Comments:

This is a well-written paper reporting the results of a well-designed and conducted experiment. The results are interesting, especially the analysis of heterogeneity in participants’ reactions to defaults (as a function of social preferences and risk aversion).

I am therefore in general quite positive about the paper; however, I do have some comments and questions that the authors should address in a revision.

Major Comments:

My major comments mainly refer to the reporting of the results. There is not always all relevant information provided to be able to understand and judge your analyses. Here are the specific points that I think should be addressed:

1. Did you use group averages in your analyses, or did you adjust for the non-independence of observations within the groups of four via clustering of standard errors? As participants interacted in groups of four and learned about each other’s extraction decisions, the independent level of observation is the group of four (unless maybe in the very first round of the CPRD game). Please explain how you handled this, especially in your mixed effects model where you analyze the interactions between individual difference variables (SVO and risk preferences) and the experimental treatments.

2. What do the p-values in square brackets ([…]) refer to? Is this the cluster-robust p-value? Or is this the p-value adjusted for multiple hypotheses testing? Please explain clearly how these p-values were derived.

3. In your pre-registration, you said that you would “account for multiple hypothesis testing by doing a correction on the tests such as Bonferroni correction”. You do not seem to have done that in your analyses (at least it is not mentioned in the paper). Please explain whether (and how exactly) you adjusted for multiple hypotheses testing, or, if not, why you chose not to.

Minor Comments:

4. P. 2 you write: “For instance, an electricity provider can offer renewable energy for consumers by default, promoting a course of action supported by policy or a prevailing social norm. Yet, some consumers may prefer other sources of energy and adapt their consumption habits to accommodate their individual preferences [16, 17]. People who are particularly insensitive to the collective issue of energy over-consumption may be more likely to override a default…”. This makes it sound like the possibility to override defaults that do not correspond with personal preferences is a negative outcome. From the standpoint of libertarian paternalism (e.g., Thaler & Sunstein, 2003), however, this is one of the great things about defaults: people with preferences different from the choice architect’s objective are not forced to just succumb to the default, but can opt out according to their own preferences. Indeed, one of the undesirable side-effects of green defaults may be that not enough people opt out when their personal preferences differ (see Ghesla et al., 2020; I believe this is also the paper that should be cited as current reference 38).

5. While I am myself positive about the potential of nudges or choice architecture interventions for behavioral change, when citing the paper by Mertens et al. (2022) on the effectiveness of nudges, I think it’s important to also cite the paper by Maier et al. (2022, PNAS) who, in a direct response to Mertens et al., argue that there is no meta-analytical evidence for the effectiveness of nudging after adjusting for publication bias.

6. You provide a screenshot of the CPRD decision screen in Figure 2, but despite mentioning it in line 210, I couldn’t find further instructions or screenshots for the CPRD in the supplementary material. Please provide these and ensure their location is clearly referenced.

7. I don’t understand the relevance of the following sentence in the context of why the authors elicited participants’ SVO and risk aversion: Line 131: “Moreover, certain decisions can pose greater challenges for specific individuals, such as the relatively slower evidence collection among equality seekers compared to categorical decision-makers [50].” Please elaborate or adapt this sentence for clarity.

8. Why did you measure SVO and risk attitude before the CPRD experiment? In principle, these measures could serve as primes and lead to undesirable spillovers into the CPRD game. You can argue that this is no major problem, because Tasks 1 and 2 were constant across conditions; however, one might also argue that such priming could interact with the treatments (e.g., if measuring SVO primes people to be more cooperative, this could lead them to react more positively to the pro-social default). Personally, I do not think it’s a major issue in the current experiment. However, in future work you might consider measuring such presumably stable individual preferences after the main experiment.

9. What is meant by “in the first rounds” in line 371? I assume you mean the first five rounds? If yes, please make this explicit. It is not completely clear, as you report an analysis for only rounds 3 to 5 just below on line 377.

10. Some typos or suggested language edits:

a. Please proof-read the paragraph on study design (starting line 148) as there are several minor mistakes or language issues.

b. Line 205: I would suggest to formulate this sentence less strongly: “mitigating selective attrition concerns” (instead of “discarding”).

c. Line 489: “a strategic incentive” (not “an strategic incentive”)

d. Line 530: “cost-efffective tool” (not “cost-effect tool”)

e. Line 530: “to ease” seems to be a strange choice of words. Please consider rephrasing this.

Congratulations to the authors on a nice paper and all the best for their future work!

References:

Ghesla, C., Grieder, M., & Schubert, R. (2020). Nudging the poor and the rich – A field study on the distributional effects of green electricity defaults. Energy Economics, 86, 104616.

Maier, M., Bartoš, F., Stanley, T. D., Shanks, D. R., Harris, A. J., & Wagenmakers, E. J. (2022). No evidence for nudging after adjusting for publication bias. Proceedings of the National Academy of Sciences, 119(31), e2200300119.

Thaler, R. H. and Sunstein, C. R. (2003). Libertarian paternalism. American Economic Review, 93(2):175-179.

Reviewer #3: Review of “From self-interest to collective action: The role of defaults in governing common resources"

This paper studies the effect of positive and negative defaults on behavior in a common pool resource lab experiment. It further correlates these behavioral effects with social preferences measured in terms of social value orientation (SVO) and risk attitudes. The study finds that these defaults have corresponding effects on initial behavior, which then converge to the Nash equilibrium over time, and faster for the positive default than the negative default. No spillover effects were detected after defaults were lifted in the second half of the experiment. The study is interesting and can potentially add to the literature on default effects. My comments are as follows.

1) Introduction/motivation: It is unclear how the renewable energy example on p.2 directly matched to the way defaults were experimentally implemented in terms of extraction values. There needs to be a better alignment of what the experiment does with what the paper claims it can tell us about which real-world applications. Clarifying the study’s domain of defaults that it concerns is important, considering that the framing and potentially behavior vary also with the specific defaults studied. Also, how does this study contribute to resolving the mixed results mentioned in p.3? I was puzzled by how this “work expands on this current understanding by testing the presence and nature of spillover effects when default values are lifted.” It was only later that I learned that the defaults were “lifted” in round 6 of 10 in all treatments, and this being a central feature of the experiment requires more emphasis and motivation in the introduction. Generally, I think the first section needs a careful rewrite to elucidate the contribution of the paper.

2) Experiment: While I understand the practicalities of excluding data from dropouts in the sample, and selecting those with high approval ratings, I wonder if this leads to a selection bias whereby certain types are self-excluded from the tests. It would be good to explain if this imposes any limitations on the findings and to explicitly defend the integrity of the experiment in this respect, if possible.

3) Analysis: I was unable to tell if the non-independence from repeated interaction was considered in the reported tests. For example, how was an independent observation defined in the non-parametric tests, and were clusters used in the multivariate tests? Also, the tests for behavior in the first and second halves of the experiment should be analyzed separately and the same applies for the presentation of their results and implications. On the matter of SVO types, are the distributions comparable with those of previous studies (related to my previous point 2) and across treatments? There were only three altruistic subjects so I would be extremely cautious about interpreting their “results”, while the present analysis (text and figures) treats them as prominently as dominant groups of individualists and cooperators. Section 4.3 mentions a hypothesis that was not previously developed.

4) The self-serving default has a noticeable negative effect on cooperation and convergence to the Nash equilibrium is slow, thus reflecting the dangers of such a default. This is also the case in the control, albeit to a lesser extent. In contrast, the pro-social default has the least negative effect on cooperation. This practical implication deserves more emphasis in the discussion.

Minor comments:

In the composite figures 1 and 3, the inconsistency of labels for A-D in caption and a-d in figure is a little confusing.

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Reviewer #1: Yes: Scott Claessens

Reviewer #2: No

Reviewer #3: No

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PONE-D-25-04274R1From self-interest to collective action: The role of defaults in governing common resourcesPLOS ONE

Dear Dr. Montero-Porras,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Please see the comments from the previous reviewers. I also read your revision and thought that major problems were solved. The reviewers pointed out several minor problems.Please address them and provide point-by-point responses.

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Please submit your revised manuscript by Aug 18 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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We look forward to receiving your revised manuscript.

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Yutaka Horita

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you to the authors for dealing with all of the points that I raised in my previous review. I am happy to recommend this paper for publication at PLOS One. I hope that it will make a positive contribution to the literature on default effects.

There is one remaining minor issue that should be addressed before publication. It looks like the logistic regression testing the associations between demographics and drop out rates (reported in Table S2) may not have converged properly, since the standard errors for many of the parameters are huge. This may be because there are only a small number of dropouts in the dataset. The authors should check whether the results of this model are valid.

Reviewer #2: I would like to thank the authors for the revision, the changes they made to the paper, and their responses to my first-round comments. Most of my comments have been addressed. I have the following remaining comments relating to points already raised in the first review.

1. In your response letter, you explain that you’ve accounted for non-independence in your mixed-effects model by including random intercepts for groups, which is appropriate. As a robustness check, however, I encourage you to report cluster-robust standard errors at the group level to show that your key ppp-values remain unchanged even if the random effect is negligible. Additionally, your reply does not address how you handled non-independence in the non-parametric tests and ANOVAs reported in the Results section. Please specify on how many observations each of these tests is based, and describe what adjustments (e.g., aggregation to group means or clustering corrections) you applied to account for within-group dependence.

2. I see no major problems with your deviations from the pre-registration as outlined in S0.1.1 in the Supporting Information. However, I think you should at least put a reference to this section in the main body of the paper (e.g., on line 213, when mentioning the pre-registration).

3. Reference #47 should be changed to the following: Ghesla, C., Grieder, M., & Schubert, R. (2020). Nudging the poor and the rich – A field study on the distributional effects of green electricity defaults. Energy Economics, 86, 104616.

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Reviewer #1: Yes: Scott Claessens

Reviewer #2: No

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From self-interest to collective action: The role of defaults in governing common resources

PONE-D-25-04274R2

Dear Dr. Montero-Porras,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Yutaka Horita

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Scott Claessens

Reviewer #2: No

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