PLOS Complex Systems

Dear Dr. Roos,

Thank you for submitting your manuscript to PLOS Complex Systems. After careful consideration, we feel that it has merit but does not fully meet PLOS Complex Systems'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 submit your revised manuscript within 60 days Jan 25 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 complexsystems@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcsy/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to any formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Marcos Oliveira

Academic Editor

PLOS Complex Systems

Hocine Cherifi

Editor-in-Chief

PLOS Complex Systems

Additional Editor Comments (if provided):

Thank you for submitting your manuscript to PLOS Complex Systems. Your submission was reviewed by four reviewers, who found the work innovative, insightful, and useful, particularly appreciating the creative use of two simple models to study social complexity. However, they identified areas for improvement, including (1) the need for greater coherence throughout the manuscript, (2) more evident connections to real-world social complexity, (3) and a more detailed discussion of the assumptions underlying the models. Their suggestions and comments should be carefully considered and incorporated into a revised version.

[Note: HTML markup is below. Please do not edit.]

Reviewers' Comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Complex Systems’s publication criteria?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

Reviewer #1: The article offers an interesting view of social complexity by differentiating between 'hierarchical' and 'dynamic' types of complexity that may affect the ability of an organisation to respond to problems or change. The author has approached this idea by constructing two agent-based models to explore the effects of the different types of complexity on the efficiency and ability of groups to solve problems. The models are intentionally simple to show the extremes of these two types of complexity, and thereby offer an interesting avenue to assess their potential effects.

When describing the different types of complexity, I appreciated the background research described by the author, situating the distinction in existing philosophical literature. However, I would liked to have seen more detailed examples of what the types of complexity could look like in real-world situations. I struggled to fully understand how a dynamic system could work at a scale beyond friendship or acquaintance networks, without an element of structured organisation (or hierarchy). The author does describe how the complexity types overlap substantially in real human organisations. Given this substantial overlap ('trans-complex'), I wonder whether a single agent-based model with parameters affecting the level of hierarchical or dynamic complexity would allow for a more thorough exploration of the parameter space between the two types and how they may interact. A model that explicitly explored how increasing or decreasing hierarchical and dynamic complexity in tandem or opposition may be more relatable to the real-world scenarios that the author discusses.

The author described the models and chosen measures of model output well and thoroughly. They may consider including a supplementary document describing the models following the 'ODD protocol' (see Grimm, et al. 2020. The ODD protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism. Journal of Artificial Societies and Social Simulation, 23 (2), 7), to aid replicability and understanding of the details of the model. A longer explanation of the process of problem solving in the model could be included there, as this is only briefly touched on in the main text. Other parameter combinations and their effects could also be explored and justified. For example, why was the parameter 'max-work' limited to 3 in the N system? This parameter could have a substantial effect on the model outputs, if allowed to run for longer.

In the results section, the author clearly shows the difference between the two models by their chosen metrics. However, the comparison of the histograms in Figure 3 seems to suggest that the problems are solved in two steps by a greater proportion of model runs in the N system than the H system, even though the averages suggest the H system is more efficient. Could additional metrics be included, such as the median and interquartile range, to show how different the outputs are? While the model code is available online, the data from the 1000 iterations run by the author are not available to re-run the analyses. Could this data be made available, either online or in supplementary materials?

I found the discussion at the end of the paper to be measured and informative. I particularly appreciated the discussion of the limitations of the model in combination with avenues for future research.

A small additional note: there were some minor typo errors in the main text that should be corrected. The written English was otherwise very clear.

Reviewer #2: This is in many ways a quite nice paper, but I think it needs some streamlining. It does not entirely keep it eyes on the ball through the paper. The introduction makes for a great start, and I like the idea with two simple models. I also like the use of the measures, which seem quite useful.

However, when it comes to the models, the setup as such is quite clever. But there is an aspect of simple that I don't like, namely the proliferation of arbitrary numerical constants. They multiply and become fractional numbers, which is always confusing because it's hard to keep track of what is what.

1 is an ok constant. There is one manager, fine. But 6 specialists? I immediately wonder what happens if one scales the system - how do the measures vary? On page 11 there is for example exercises with numbers that aren't overly aesthetic. The quality measure equation on page 8 is QS = 5 - i's, where 5 is an artifact of the problem size. It's a lot better to plot instead, and symbolical variables, so that one gets an idea for how the quantities are related. How does the complexity measures increase with problem size, number of specialists, cost breakoff, and so on? For instance, it feels like Table 1 would be much more instructive if the model results were expressed in terms of the variables of the model.

Certainly, I see that specific examples can be useful, but perhaps you can have, say, three scenarios with numerical values, or you can plot how the measures vary with problem size, number of agents, etc.

Also, if examples with numerical constants are required, consider an appendix?

Some brief suggestions:

* Dynamic complexity: What about Lyapunov exponents?

* Dynamic and structural complexity. My immediate reaction what the Peter Érdi had introduced those in his 2008 book Complexity Explained, but then I looked and he hadn't really done that. The terms are quite self-explanatory so I suppose they have been floating around. But the book is worth having a look at in any case.

* Andersson and Törnberg (2014) or (2018) suggest that complexity really is a cognitive phenomenon and thereby it may be essentially hard to capture except for in classes, since it doesn't have any single root cause or essence. That it is really a certain type of mental overflow, where the brain is presented with too many degrees of freedom - which can be generalized to other kinds of processing.

* When it comes to dynamical complexity, my first image is bird flocks and stuff like that, which is not really dealt with in the paper.

The paper, in summary delivers things along the way, but it doesn't really come together as well as it could. The contents are in there really, but a new round of making it sleeker. Make sure the questions posed initially really are central. Now the measures take over for a while, and they're useful, but how do they really contribute to differentiating between structural and dynamical complexity? If this is done well, then the conclusions are easier to write succinctly and to the point. Now the conclusion is a bit meandering and a selection of interesting points to make.

So in summary, the paper needs streamlining and better finish, but I certainly think there is hope for it.

Reviewer #3: The paper presents a novel framework for the study of social complexity, delineating between structural and dynamic complexity. Subsequently, two models were devised: the hierarchical system (HS) and the non-hierarchical system (NS). A set of metrics was proposed to quantify both types of complexity. This distinction and these metrics are innovative and provide researchers with a new avenue for understanding and assessing the complexity of human social systems. These concepts are illustrated through the presentation of two agent models. But I have some other opinions about the article as follows,

1. It is recommended that the authors concentrate on the coherence of the paper's structure and content in order to facilitate a more effective reading and comprehension process for the readers.

2. The introduction is not very good well-designed, and what the author wants to express is not clear. The introduction proposes an analysis of both forms of structural and dynamic complexity, yet only offers a general statement regarding the importance and value of understanding a system's complexity. It also makes a passing reference to the necessity of distinguishing between the two forms of complexity, yet it fails to provide a detailed account of the rationale behind the selection of these two specific forms and the unique research value each of them offers. In the final paragraph of the introduction, it is recommended that the specific aims and research questions of the study are clearly stated in order to provide the reader with a clear understanding of the research direction of the article.

3. The authors proposed some index for structural complexity and dynamic complexity. What is the relationship between these indicators and why they are chosen is not well explained by the authors.

4. Could you mention some examples of the real world to better combine theory with practice?

5. In the discussion and conclusions, it is recommended that the practical value of the research be emphasized, particularly in relation to its potential impact on policy-making and organizational management. A discussion could be held on the ways in which the research findings could be applied in practice to assist policy makers in the design of more effective social systems.

In conclusion, it is recommended that authors undertake miner revisions to their papers.

Reviewer #4: This paper addresses important and intriguing questions, offering a foundation for further exploration. However, while the simplicity of the approach allows for initial insights, it also highlights the need for more sophisticated and well-adapted mathematical models and tools to conduct a deeper and more meaningful analysis.

One key concern is that one of the models employed is overly conservative, relying entirely on randomness. This assumption is both unrealistic and unfair, as it inherently limits the performance of the model. The paper essentially compares two strategies to solve a problem: one structured and informed by prior knowledge, and the other completely random. The analogy is akin to deciding whether to reach a destination using a GPS or by randomly turning at intersections—there is no meaningful debate about which approach would be better. As a result, the comparison feels not only unfair but also somewhat trivial, as the random strategy lacks practical relevance and the comparison itself risks being misleading. Additionally, the paper does not delve into the details of "randomness," which is crucial, as different distributions (e.g., Gaussian vs. power law) can lead to vastly different outcomes. Addressing this omission would add much-needed depth to the discussion.

In my view, the second model (system N) would greatly benefit from refinement to make it more meaningful and insightful. For instance, a Markov chain model might be more appropriate, offering a structured stochastic framework that could better represent the problem while retaining an element of randomness. Another issue with the paper lies in its treatment of dynamic complexity. By definition, dynamic complexity pertains to changes and interactions over time, yet all the models presented are static. This contradiction weakens the analysis and suggests that dynamical systems—such as those based on Markov chains or other time-evolving models—would be more suitable for capturing the essence of dynamic complexity.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: Yes:  Corentin Briat

**********

Figure resubmission:Reproducibility:--> -->-->To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols-->?>

Attachments

Attachment

Submitted filename: Response letter.pdf

Attachment

Submitted filename: Response letter_R2.pdf

PCSY-D-24-00134R1

The Complexity of Problem-Solving Human Social Systems: Structural vs Dynamic Complexity

PLOS Complex Systems

Dear Dr. Roos,

Thank you for submitting your manuscript to PLOS Complex Systems. After careful consideration, we find that your manuscript has substantial merit, and we appreciate the significant effort you have made in addressing the reviewers' comments.

It is now nearly ready for publication, pending a minor revision to address the issue raised by Reviewer 3 regarding the description of Figure 3. Additionally, Reviewer 2 suggested some reframing; these are not mandatory, and you may consider them only if you feel they would improve your manuscript. We invite you to submit a revised version incorporating this final revision.

Please submit your revised manuscript within 30 days May 27 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 complexsystems@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcsy/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to any formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Marcos Oliveira

Academic Editor

PLOS Complex Systems

Marcos Oliveira

Academic Editor

PLOS Complex Systems

Hocine Cherifi

Editor-in-Chief

PLOS Complex Systems

Journal Requirements:

Additional Editor Comments (if provided):

[Note: HTML markup is below. Please do not edit.]

Reviewers' Comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

publication criteria?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

Reviewer #1: I would like to thank the author for addressing all of my comments clearly. The inclusion of the new HN-model is particularly appreciated and I think adds substantial clarity to the message of the paper. I also appreciate that the author has included links to full model descriptions available to view online. Overall, I think the paper is much improved and I recommend it for publication.

Reviewer #2: Overall I’m pleased with the responses given by the author to my comments. I’m pleased to find sensitivity analyses since this add quite a bit of value.

One thing that I would like to suggest to the author, however, is the possibility of slightly re-framing the findings, which are stated along the lines that dynamic complexity is more robust and structural complexity effective on known problems.

I think this confirms a widespread intuition, which weakens them as findings. However, calling this “the findings” also seems to me to undersell the paper and not correspond to what the models really do.

The models' primary contribution lies not in confirming this intuitive result but in allowing us to explore and understand precisely why dynamic and structural complexities lead to different outcomes. The metrics developed in this paper are crucial because they quantify these complexities in ways that reveal their underlying roles and interactions.

If the author agrees on this, I think the paper would gain impact by adding that precision to the framing – which may require some adjustments of framing further down in the text too – although not very big adjustments since I think this is already what the paper does.

Another reflection is that the collection of measures actually makes the paper valuable also as a review. It's always valuable to have such things collected, commented and compared in one place.

Reviewer #3: Although the structural configurations of the networks used by the authors in their manuscript are relatively simplistic, the research objective of highlighting differences between structural complexity and dynamic complexity remains meaningful. The authors have conscientiously and open-mindedly revised their work according to the previous reviewers' comments, and I have no further comments at this stage.

There is only one minor issue I would like the authors to confirm. In section 2.3�authors saied, "The team leaders are represented by green circles, the normal team members are blue, and the manager is red. " but I find the blues much more like team learders from Fig 3.

Reviewer #4: No further comments.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: Yes:  Claes Andersson

Reviewer #3: No

Reviewer #4: Yes:  Corentin Briat

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

Figure resubmission:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. If there are other versions of figure files still present in your submission file inventory at resubmission, please replace them with the PACE-processed versions.

Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

The Complexity of Problem-Solving Human Social Systems: Structural vs Dynamic Complexity

PCSY-D-24-00134R2

Dear Dr. Roos,

We are pleased to inform you that your manuscript 'The Complexity of Problem-Solving Human Social Systems: Structural vs Dynamic Complexity' has been provisionally accepted for publication in PLOS Complex Systems.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team. 

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they'll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact complexsystems@plos.org.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Complex Systems.

Best regards,

Marcos Oliveira

Academic Editor

PLOS Complex Systems

Hocine Cherifi

Editor-in-Chief

PLOS Complex Systems

***********************************************************

Thank you for your valuable contribution and the thoughtful revisions made in response to the reviewers' comments.