The first major content theme in The Atlas of Social Complexity is Cognition, Emotion and Consciousness. This first theme includes six chapters, which I have so far blogged on. Chapter 6 addresses autopoiesis. Chapter 7 turns to the role of bacteria in human consciousness. Chapter 8 explores how the immune system, just like bacteria and cells, is cognitive – and the implications this has for our wider brain-based consciousness. Chapter 9 explores a complexity framing of brain-based cognition, emotion and consciousness. Chapter 10 explores the complex multilevel dynamics of the Self. Chapter 11 is about human-machine intelligence.
The second major content theme in The Atlas of Social Complexity is The Dynamics of Human Psychology. So far for this theme, I’ve given a basic overview, found here. I then moved on to the first theme, Human psychology as dynamical system (Chapter 13). From there I reviewed Chapter 14: Psychopathology of mental disorders ; Chapter 15: Healing and the therapeutic process; and Chapter 16: Mindfulness, imagination, and creativity.
The third major theme is living
in social systems (Chapter 17). The first chapter in this section is Complex
social psychology (Chapter 18). From there we move on to Collective
behaviour, social movements and mass psychology (Chapter 19). Next is Configurational Social Science (Chapter 20). From there we
move to the Complexities of Place (Chapter 21); followed by Socio-technical Life (Chapter 22). Chapter 23 turned to the
theme of Governance, Politics and Technocracy. Chapter 24 focused on
The Challenges of Applying Complexity. Chapter 25 focused
on Economics in an unstable world. And, finally, Chapter 26
focused on
resilience and all that jazz.
The focus of the current post is CHAPTER 27: METHODS
QUICK OVERVIEW OF THEME 5
With Chapter 27 we move to the final theme of our book: methods. Theme 5 seeks to balance a rigorous critique of the methods of computational and complexity science, while also providing a clear horizon along which various advances are being made in answer to the thirteen situations. The chapters in this theme include Make love, not models (Chapter 28), Revisiting complex causality (Chapter 29), Mapping the new methods terrain (Chapter 30) and Getting philosophically real (Chapter 31). Chapter 29, in particular, includes a survey of the qualitative and interdisciplinary methods we see as most promising, which includes various case-based methods and systems mapping. Chapter 31 end the theme and the tour by grounding it all in the philosophical framing we find most useful, complex realism.
A bit more in-depth explanation:
While we have been hypercritical of the methods turn in the complexity sciences, we do acknowledge that it does mean that a lot has been happening in the field of methods, particularly in the areas of application, mixed-methods, decision making support and policy.[1] The question is, “To what extent have these advances embraced a social complexity imagination sufficient to overcome this area’s particular configuration of the thirteen situations to truly become transdisciplinary?”
The short answer is, “We still have a very long way to go”.
As a reminder, on the natural, mathematical and computational sciences side, in addition to a failure to engage the wider social sciences (Situation 2), the current methodological limitations includes: technique in the absence of theory (Situation 7), the lean toward predictive machines over learning tools (Situation 8), the minor if not entirely absent role of qualitative methods (Situation 9), and the dire sound of technicalities. On the social sciences side, it has to do with the methodological closing of the social scientific mind (Situation 10), which, while somewhat improving, still faces, particularly at the undergraduate level, the continued need for a better methods curriculum; and, in terms of staffing, the need to hire more transdisciplinary methods experts in social science and computer science departments. Despite this inexcusable lapse, many academic disciplines remain largely nonplussed.
Still, there are a handful of rather productive trajectories of methodological advance that we, as tour guides, strongly recommend pursuing for those with interested in advancing transdisciplinary methods. So far on our tour, we have highlighted (albeit somewhat indirectly) four: dynamical systems theory, complex network analysis, case-based configurational methods and complexity in evaluation.
There are other methodological avenues that we have yet to formally review, which we believe also have significant potential. They fall into three major categories – case-based complexity, systems mapping, and approachable modeling and smart methods, or AM-Smart for short – and with half of these methods being qualitative. Examples across the three include trajectory-based QCA, causal loop diagrams, participatory systems mapping, and system dynamics. Our purpose in this theme is, in part, to review these new methods – which constitutes the focus of Chapter 30 (Mapping the new methodological terrain). Our focus in Chapter 30 is primarily to set up a cafeteria approach to methods. We want to encourage readers to really explore the possibility of using, combining, or developing new and different methodological suites or interdisciplinary methodological repertoires.
Our other goal, which forms the majority of chapters in Theme 5, is more philosophical. Since the beginning of the tour, we have rallied against a certain way of doing social complexity, which for lack of a better phrase, is grounded in a ‘naturalising’ or physical-computational science stance. While we have variously explained our concerns with this approach, we have not formally addressed the underlying epistemologies driving it. Here is a point to remind readers: we are not opposed to any method per say, as much as we are opposed to how they are used. Our concerns are one of approach. Our philosophical objective in Theme 5 is to have an in-depth conversation with the methodologies we find the most problematic. We do this in two ways. We will riff (in the spirit of jazz improvisation) on a variety of epistemological issues and concerns across chapters 28 through 30, comparing and contrasting different ways of methodologically thinking about causal complexity. We end by outlining our own position, Chapter 31, which is grounded in a complex, critical realism.
[1] See, for example, Barbrook-Johnson, P., & Carrick, J. Combining complexity-framed research methods for social research. International Journal of Social Research Methodology. 2021: 1-14; Gilbert, N., et al. Computational modelling of public policy: Reflections on practice. Journal of Artificial Societies and Social Simulation. 2018: 21(1).
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