Most histories of complexity are heroic adventures, focusing entirely on advance; ours is critical, exploring the challenges holding back the study of social complexity. The complexity sciences are not the disruptive, transdisciplinary science they promised to be.
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| Map, Special Collections, University of Amsterdam |
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CHAPTER 2 SUMMARY:
Chapter 2 is all about setting the context for the tour the Atlas takes. You must set sail from somewhere. Our point of departure is the challenging and problematic place the complexity sciences find themselves in, presently, in terms of the study of social complexity.
Critically mapping that problematic space is the focus of Chapter 3. As outlined in that chapter, we have identified a set of thirteen situations that are holding the study of social complexity back from being a truly transdisciplinary disruptive science.
Chapter 2 focuses on understanding the historical pathways by which the study of social complexity ended up in a challenging space in the first place. We start by defining complexity. From there we present a critical history of the study of social complexity, focusing on four major historical shifts:
(1) the emergence of the complexity sciences.
(2) the invasion of natural scientists into everything social.
(3) the 1990s complexity turn in the social sciences.
(4) the social science turn in complexity, circa 2020s, which is the shift that holds our attention for the rest of the book.
Map of the early years of the complexity sciences, with a focus on the Santa Fe Institute
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| Johanna Bergmann, John Casti, 2002 |
Map of the complexity sciences, circa 2022. The field has massively expanded, but also, in the process, become watered down, evolving into ‘normal’ science in the Kuhnian sense of the word.
No, Not Everything is Scale-Free!!!!
Of the various examples we provide in Chapter 2 of how, historically speaking, the study of social complexity has evolved into the current predicament it faces, one of the most straightforward is the study of scale-free networks.
There is significant controversy around whether scale-free networks exist, given that the definition requires that the distribution fit a power-law, which most networks with long-tails do not meet. The problem is that, in the real-world, scale-free networks that fit a power law are not so easily identified and mapped. After the initial excitement resided, then, in the 1990s,researchers started running into all sorts of problems fitting the tails of these distributions.What they found was that, while lots of scale-free networks have the classic heavy tail distribution, they do not regularly fit the power law criteria.
Here is the problem: instead of evolving the concept in response to the empirical data, divisions emerged, with many of those involved in its initial conception holding on to the concept as sacred. In fact, these are literally the words used by Voitalov and colleagues’ in their 2019 article, 'Scale-Free Networks Well Done’, which is a defence of scale-free networks. They state, “Scale-free and power-law are sacral words in network science, a mature field that studies complex systems in nature and society by representing these systems as networks of interacting elements” (p. 1).
(For more on this controversy, see Erica Klarreich’s article, CLICK HERE. For Barabasi’s response, amongst, others, CLICK HERE.)
What is our point? We are not saying complexity science cannot be without controversies. We are saying the opposite. It should be controversial! For us, most of science is a dialogue, a critical argument, in search of useful insights into how the world works. When concepts in the complexity sciences are considered sacred, one is venturing into troublesome territory, with boundaries being built around ideas, including who is allowed to say what and when, as in the case of natural science over social science. If the complexity sciences are to be continually transdisciplinary and disruptive for the purposes of studying social complexity, the field needs a bit of a shake. It needs what we are calling a social science turn – a critical reflection on complexity from a different perspective, it needs a rigorous and critical engagement with the social sciences it sought to advance.
REFERENCES
1. Anna D. Broido and Aaron Clauset, ‘Scale-Free Networks Are Rare’, Nature Communications 10, no. 1 (December 2019): 1017.
3. Brian Castellani and Rajeev Rajaram, ‘Past the Power Law: Complex Systems and the Limiting Law of Restricted Diversity’, Complexity 21, no. S2 (2016): 99–112.
4. Voitalov et al., ‘Scale-Free Networks Well Done’, Physical Review Research 1, no. 3 (18 October 2019): 033034, https://doi.org/10.1103/PhysRevResearch.1.033034.
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