Complexity in Systemic Cognition: Theoretical Explorations with Agent-Based Modeling

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper deals with an interesting agent-based example of systemic cognition, that could also be termed collective intelligence or self-organization. 

The paper is clear and well written (although a bit verbose for my background, I concentrated mainly on the model). 

I have not found any evident flaw, and the results are sensible. However, I would prefer to see more detailed time plots of data, while only linear fits an error bars are shown.

I think that many possible extensions of the model are possible, mobile agents and variable connections, and multi-dimensional competence. Maybe it is worth citing the most interesting (or adequate) possible extensions in the conclusions.

Author Response

Thank you very much for reviewing our paper and for providing such constructive feedback and interesting points. We have addressed them all, and written a few comments to share our implementation process. Modified parts in the paper appear in blue font.

 

Comment 1: The paper deals with an interesting agent-based example of systemic cognition, that could also be termed collective intelligence or self-organization. 

Response 1: We know this is not a comment that requires action on our part; we just wanted to signal that some of the references we have used in the paper resonate with the idea of the “collective” (e.g., Miller & Page, 2007) and of “self-organizing” (e.g., North & Macal, 2007; Edmonds & Meyer, 2016).

 

Comment 2: The paper is clear and well written (although a bit verbose for my background, I concentrated mainly on the model). 

Response 2: Thank you for your appreciation. We appreciate your comment and realize that writing style depends very much on the discipline. The coauthor team consists of social scientists / humanities scholars, and we acknowledge that our styles are traditionally more prone to verbose texts than those of the natural sciences. We have tried to streamline the text as much as possible, especially throughout the theoretical section (pp.2–4).

 

Comment 3: I have not found any evident flaw, and the results are sensible. However, I would prefer to see more detailed time plots of data, while only linear fits an error bars are shown.

Response 3: We understand and agree. The reason why we have tried to be parsimonious with our exposition of results relates to the length limitations of the submission. However, we agree with you that at least one additional plot can be fitted in. Among the different plots that we could select, those related to time are those that are more difficult to interpret. This is in line with fixed panel regression results, where time resulted not as relevant as the other variables estimated. For this reason, we have opted for a plot that specifies the findings from the regression results. This is one where competence (log) is shown as it relates to the disposition to listen (log; x-axis) and to the disposition to share information (the range of colors). To make results more interesting, we have created a centrality index that is the weighted average of the three centrality coefficients presented in Table 1. We have explained our procedure in the text of the article and motivated on the basis that we did not want to overemphasize one coefficient over the other. Hence, the plot splits between low, mid, and high influence. Findings indicate that, while there is a difference in competence that depends on the structure of the network, the disposition to share seems to also have a key role in explaining higher levels of competence. See lines 405–417 and Figure 4.

 

Comment 4: I think that many possible extensions of the model are possible, mobile agents and variable connections, and multi-dimensional competence. Maybe it is worth citing the most interesting (or adequate) possible extensions in the conclusions.

Response 4: This is another very good point and we now mention a few possible extensions in the concluding section of the paper (lines 521–533).

Reviewer 2 Report

Comments and Suggestions for Authors

 

This is a fairly well-structured piece of work with a philosophical bent. It discusses the concept of human cognition as a systemic process that involves the interaction of various elements, including agents, environment, tools, and social practices. It highlights the importance of the meso domain, which refers to the interactions between individual agents and their environment, and how it shapes cognitive processes. To do this it firstly surmises that cognition is a complex process, but there is sufficient seemingly uncited published material that refers to cognition as being complex. This act of "surmise" really needs to be strengthened a little.

Since the meso domain is of such central importance to this paper, I find it curious that the only reference to it comes from Secchi et al (2024). Recognising that systems is a mutidisciplinary area, it is not problematic to move over to economics to see how the meso domain has been embraced, ad in evolutionary economics it is embraced in the same way as in complexity. Thus, the meso has been discussed by Dopfer et al (2004) in the context of evolutionary economics, later by Dopfer (2012), and applied to complex adaptive/living systems theory by Guo et al (2016). I would recommend you provide a slightly improved historical context for this.

The simulation model seems appropriately formulated, baring in mind that I am not an expert in this area.

Dopfer, K., 2012, The origins of meso economics. Schumpeter’s legacy and beyond. Journal of Evolutionary Economics 2012, 22:133-160.

Guo et al. (2016) The Changing Organisation: Agency Theory in a Cross-cultural Context. Cabridge University Press.

 

Author Response

Thank you very much for reviewing our paper and for suggesting a few directions we can take to strengthen our arguments. We have done our best to consider all of your suggestions and to see them implemented in the paper. Please see our responses to your comments below. Modified parts in the paper appear in blue font.

 

Comment 1: This is a fairly well-structured piece of work with a philosophical bent. It discusses the concept of human cognition as a systemic process that involves the interaction of various elements, including agents, environment, tools, and social practices. It highlights the importance of the meso domain, which refers to the interactions between individual agents and their environment, and how it shapes cognitive processes. To do this it firstly surmises that cognition is a complex process, but there is sufficient seemingly uncited published material that refers to cognition as being complex. This act of "surmise" really needs to be strengthened a little.

Response 1: Thank you very much for this effective summary of our paper; you have caught its most important traits and aims well. At the same time, we agree with you that we could have been a bit more precise and thorough in our theoretical background when it comes at making the complexity stance for cognitive processes (see our more detailed responses below).

 

Comment 2: Since the meso domain is of such central importance to this paper, I find it curious that the only reference to it comes from Secchi et al (2024). Recognising that systems is a mutidisciplinary area, it is not problematic to move over to economics to see how the meso domain has been embraced, and in evolutionary economics it is embraced in the same way as in complexity. Thus, the meso has been discussed by Dopfer et al (2004) in the context of evolutionary economics, later by Dopfer (2012), and applied to complex adaptive/living systems theory by Guo et al (2016). I would recommend you provide a slightly improved historical context for this.

Response 2: This is a very good point and we have taken it on board. The theoretical section now includes a paragraph on Dopfer’s reading of Schumpeter’s micro-meso-macro framework and of the work in change management that you suggested. Actually, we are familiar with the work of Yolles (we have also referred to his work in another recent article we published) and have included it in this revised version of the paper (p. 3).

 

Comment 3: The simulation model seems appropriately formulated, baring in mind that I am not an expert in this area.

Response 3: Thank you very much for your appreciation. In an attempt to clarify results further, we have added a time-dependent plot where we have attempted to improve the presentation of results further (at least, that was our intention!).