The Atlas of Social Complexity. Chapter 32: The Unfinished Space

QUICK OVERVIEW AND LINKS TO THE OTHER THEMES AND CHAPTERS IN THE BOOK

 

We come, finally, to the last chapter of the Atlas, and with it, the initial series of blog posts covering the entire book.

 

Here is a summary of those posts and our core themes: 

 

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. 

 

Chapter 27 introduces the final theme of the book, Methods and complex causality. It begins with Chapter 28, Make Love, Not Models and then moves on to Chapter 29, Revisiting Complex Causality. Chapter 30 maps the new methodological terrain. Chapter31 takes on the issue of philosophical complexity and outlining our approach, complex realism.

 

 

The Sixrth and final theme, The Unfinished Space (Chapter 32) ends by not ending.

 

 

THE UNFINISHED SPACE

The final chapter of The Atlas of Social Complexity is our unfinished ending: an open space rather than a final word. We chose not to close the circle but to celebrate its non-closure. The study of social complexity, as we’ve learned from the interviews we conducted for this book, the literature we reviewed, and our own ongoing experimentation across disciplines, thrives when it resists premature conclusions.

We take seriously the generative power of incompleteness, not as a flaw, but as a vital condition for creativity, emergence, and new insight. This chapter is both reflection and invitation: to embrace porousness, to foster discomfort, to wander ‘rhizomatically’ through social systems as through a city without a centre.

 

Rather than resolving complexity, we call for its re-imagination. We draw from the arts, jazz, speculative cartography, and moments of collaborative improvisation. We highlight the danger of retreating into silos, of reducing complexity to normative procedures or narrow metrics. Across conversations and terrains (from symptom networks to actor-networks, urban flows to policy knots) we witness a common thread: the need to stay open, to keep the system leaky.

 

This is not easy. Becoming transdisciplinary means being willing to feel lost. It means resisting the institutional push for closure, while also cultivating the craft, care, and curiosity needed to stay in the work. Education, too, must be rethought, not as instruction in tools but as apprenticeship in thinking, failing, and sensing the edge of the known.

 

We leave this Atlas unfinished on purpose. The chapters do not converge on a grand theory. Instead, they open onto a space of adjacent possibles. Like the megalopolis with its tangled corridors and surprise exits, this final chapter is less a map than a membrane, inviting you, the reader-traveller, to walk, improvise, and reassemble the terrain for yourself.

 

Here, then, are the terrains we reflect on in this final chapter, mapped loosely, nonlinearly, in the same spirit with which we travelled:

 

·      The art of staying unfinished: why incompleteness is not failure but method

·      Unease and comfort: how structure soothes but also constrains the complexity imagination

·      The rhythm of contradiction: our paradoxical urge to both expand and contain the field

·      Rhizomes: the city as metaphor, the map as open architecture

·      Porosity and permeability: how we design membranes, not walls, for transdisciplinary practice

·      The lure of the un-grasped: why complexity attracts those drawn to edge terrains

·      Productive struggle: how our interviewees created generative friction across boundaries

·      Organising for emergence: improvisation, jazz, and institutional design for collective creativity

·      Listening as method: how conversation and rhythm shape collaborative intelligence

·      Becoming transdisciplinary: why this is a journey, not a state

·      Becoming educated: complexity as an apprenticeship in curiosity, not a credential

·      Cartographies of refusal and invitation: the ethics of leaving space unfinished

 

Let this chapter, then, remain porous. A threshold rather than a gate. An atlas, yes! But one with missing pages, unfinished sketches and maps, and journeys waiting for others to take.

 

 

 

The Atlas of Social Complexity. Chapter 31: Getting Philosophically Real . . . a case for complex realism.

QUICK OVERVIEW AND LINKS TO THE OTHER THEMES AND CHAPTERS IN THE BOOK

 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. Chapter 27 introduces the final theme of the book, Methods and complex causality. It begins with Chapter 28, Make Love, Not Models and then moves on to Chapter 29, Revisiting Complex Causality. Chapter 30 maps the new methodological terrain.

The focus of the current blog post is (Chapter 31) is addressing the issue of philosophical complexity and outlining our approach, complex realism.

Getting Philosophically Real

When Lasse and I set out to map the complexity turn in social science, we knew the tour couldn’t end without turning to philosophy. Not the capital-P, armchair sort that polishes metaphysical trinkets. We mean philosophy as groundwork, as excavation, as a toolkit for getting real about complexity. Hence, the final chapter of The Atlas of Social Complexity is our invitation to “get philosophically real.”

It all began with a provocation: I (Brian) was in South Africa, running a workshop on our complexity-friendly methods at Nelson Mandela University. We hit the first condition—“There is no philosophy of complexity”—and the room, filled with philosophers, went still. Then came the hands. “What about Cilliers?” “What about Varela?” “What about Prigogine and Stengers?” The conversation erupted. It was brilliant. And telling.

What we realized in that moment, and what this chapter tries to spell out, is that the philosophy of complexity exists—but only in fragments. Spread across systems theory, postmodern critiques, cybernetics, epistemologies of embodiment, and methodological pluralism, it lacks a clear terrain. It’s like the early days of the social science turn in complexity: present, provocative, but disjointed. Hence our call for a philosophy of complexity—not just a complexity of philosophy. A grounded, ongoing engagement between philosophers and complexity scientists. A framing. A field.

To that end, this chapter sketches a philosophical programme that we and others have come to call complex realism. Rooted in Roy Bhaskar’s critical realism and branching through thinkers like Archer, Sayer, Byrne, Elder-Vass, Harvey, and Williams, complex realism is not yet a fixed canon—it’s a post-disciplinary scaffolding. It’s a start. A field in motion.

In contrast to the narrow empiricism of much computational complexity science, complex realism foregrounds the ontological status of the social: its depth, its historicity, its emergent and stratified nature. It affirms that social systems are real—but not in the deterministic sense of physics. They are open, contingent, probabilistic. In other words, they are complex.

Importantly, complex realism is as much epistemology as it is ontology. It invites us to study social systems using a pluralist, adaptive, model-rich approach. Every model is a window, not a mirror. Causality is plural. Methods must be mixed. And emergence is not just a pattern to be visualised but an ontological feature to be reckoned with.

We are not philosophers, Lasse and I. We are social scientists. But we know that our tour cannot end in methodological pragmatism alone. We need to get clear—ontologically and epistemologically—about what we are doing. And so, we offer the following tenets of complex realism as both provocation and provisional map. Use them. Expand them. Critique them. But most of all, let’s get philosophically real—together.

Complex Realism: Basic Tenets

Ontology

• The question Bhaskar sought to answer was fundamentally ontological. He wanted to know what the social world must be like for social science to take place.
• Complex realism begins with the premise that there is a real world out there beyond what we can observe.
• The social world is just as real as the physical and natural worlds.
• Although the social world is real, it is different from the natural and physical worlds. To begin, it lacks the type of natural necessity one finds in physical and natural systems, which tend to be more deterministic. In social systems, there is also a greater degree of variance and contingency. This stems from human agency and its ability to respond differently in similar situations. It makes social reality probabilistic, with the possibilities of equifinality and multifinality, and limitations to prediction.
• Williams summarises the ontology of social complexity as follows: “social reality at any given time is the produce of the historical realisation of a matrix of contingent outcomes that have the properties of relative invariance, emergence and dynamic change”.
• Byrne and Callaghan add to this, arguing that, in terms of what social reality looks like, the social world is ontologically complex and systemic.
• Defining the bounded nature of any particular social system is a theoretical and empirical act. The systemic nature of the social world, ontologically speaking, is better seen as a complex web that is network-like in its structure and organisation.
• The networked aspects interact and it is through this interaction that environmental influences become internalized. The process by which this happens is emergence: structure is formed through the interaction of the aspects.
• As a result, social complexity is emergent, constituting its own form of reality. Emergence is an ontological necessity of social reality.
• Social existence takes place at different levels of emergent order. ‘Levels’ refers to Archer’s distinction between interactional and local to systemic, not to the conventional distinction between the micro and macro in the sense of small to large.
• Emergence is not a discrete entity or phenomenon that can be investigated as if it is an object out there. Instead, it serves as an ontological vehicle for thinking about the nature of causation.
• As an emergent reality, social complexity is ontologically defined as real, actual and empirical. The experience of social complexity is the empirical; the outcomes across instances are the actual; and the underlying mechanisms of social complexity are the real.
• Given its emergent nature, social complexity is developmentally open, which limits the possibility of prediction.

 

Epistemology

• Given its ontological position, complex realism in the hands of social scientists is focused on the empirical study of the actualized real. The question is under what conditions the real becomes actualized.
• This is the search for (conditional) empirical mechanisms – which is why critical realism and configurational thinking, in our estimate, fit together so well.
• When complex realists use the term ‘social complexity theory’ they mean a framework for understanding social life as emergent from the real to the actual.
• As a framework, social complexity theory can be both conceptual and offer a causal theory.
• Social complexity theory also provides a theory of production: an explanation of e.g., how power is produced, or how social deprivation comes into being.
• Given the complex nature of social reality, epistemologically speaking, we can never fully know social complexity to its full actualized extent. It means that any given empirical model is not entirely correct. They are windows unto the social complexity being studied.
• Still, while bounded, we can know things about social systems and their complex causality.
• Because all models will be under-determined (or, perhaps more appropriate to complexity, over-determined) by the evidence, we need multiple complementary empirical models.
• Given the need to develop multiple models, be it from different angles and theoretical framings, methodologies and methods likewise need to be pluralistic and transdisciplinary.

Next stop? A phase shift. The future of complexity science needs its philosophy—not as ornament but as infrastructure.

Royal College of Physicians’ report, 'A breath of fresh air: responding to the health challenges of modern air pollution'

I am super excited to be part of the writing team for the Royal College of Physicians’ report, A breath of fresh air: responding to the health challenges of modern air pollution. I would like to thank Professor Sir Stephen Holgate, Dr Suzanne Bartington and Dr Gary Fuller and the core RCP team for the opportunity to contribute to this report.

 

MY SECTION (3.2 OF THE REPORT) WAS ON AIR QUALITY POLICY AND BRAIN HEALTH, including mental health and dementia, based on the policy agenda my team and I recently outlined for the journal Environmental Research – see below. For more on our work, in addition to the above article, see InSPIRE, our research and policy consortium for mitigating the impact of air quality and environmental exposures on brain health.

 

OVERVIEW

The RCP’s new report highlights new evidence gained over the last decade showing that there are now links between air pollution and almost every organ in the body and the diseases that affect them.

 

According to the report, around 30,000 deaths per year in the UK are estimated to be attributed to air pollution, with an economic cost of £27 billion in the UK due to healthcare costs, productivity losses and reduced quality of life. When wider impacts such as dementia are accounted for, the economic cost may be as high as £50bn. 

 

The new RCP report is an update to the 2016 joint Every breath we take report from the RCP and Royal College of Paediatrics and Child Health (RCPCH). It sets out new evidence gained over the last decade about the health harms of air pollution even at low concentrations. We now know that air pollution exposure in early life is linked to poor health later in life and that it impacts foetal development, cancer, heart disease, stroke, mental health conditions and dementia. 

 

Indoor air also poses a growing concern, requiring considerably more attention. People spend the majority of their time in buildings, but there are few standards for pollutant concentrations in indoor air. Poor ventilation, damp and mould and emissions from domestic heating, gas cooking and cleaning products can all contribute to poor health.

 

It also highlights the links between air quality and health inequalities, showing that air pollution disproportionately affects those from more deprived or vulnerable backgrounds.

 

Put simply, there is no safe level of air pollution, and increasingly ambitious action needs to be taken to improve air quality across the country to reduce avoidable deaths and improve the health of our population.

 

The report sets out 19 recommendations aimed national, regional and local governments across the UK, industry, regulators, the NHS, clinicians, and individuals in society. 

 

 

3.2 Air quality policy and brain health

3.2.1 The impact of air pollution on brain health

As discussed in Part 1, a major addition to the air quality literature – and a fast-growing topic of study – is the impact that air pollution has on brain health across the life-course. Of particular concern is the effect of exposure to air pollution at critical stages in our lives, such as on early-life cognitive development, as well as the effect of cumulative exposure across time, such as on the development of dementia, cognitive frailty and Parkinson’s disease.1–4 More broadly, air pollution is also associated with general mental health issues such as depression and anxiety and is also an identified risk factor for schizophrenia and personality disorders.5,6 Its impact on people who have existing brain and mental health vulnerability is an additional concern. While most studies focus on the increased mental health risks resulting from air pollution exposure, research has begun to explore the post-diagnostic impact of air quality. For example, there is now strong evidence that air pollution exposure contributes to dementia progression and Alzheimer’s disease deterioration.7–9 Research also suggests that for people with dementia, air pollution exposure can have an impact even at concentrations below the current US EPA annual standard for PM2.5 of 9 μg/m3, and well below the limits of the UK (20 μg/m3) and the European Union (25 μg/m3).10

 

3.2.2 Interactions between air pollution, inequalities and mental health

As outlined in Part 2, pre-existing or ongoing vulnerability is not just biological or psychological, it is also social. The impact of air pollution on brain and mental health appears to be interwoven with the wider determinants of health, including systemic inequalities, pre-existing public health vulnerabilities, the built environment, transport, discrimination and entrenched socio-economic deprivation. Examples include how the causal loop between poverty, living near an industrial air pollution source, and social inequalities across the lifecourse impact upon cognitive decline and neurodegenerative disorders in older, urban populations. Or, how air pollution exposure in early life impacts adolescent global cognition, on account of poor health behaviours, limited access to green space, living in congested housing with poor indoor air quality, and walking to school on busy more polluted roads.11 People living in poverty due to mental health disorders are more likely to have additional breathing issues, making them more susceptible to the impact of air pollution. The places where people are born, live, work and grow old matter very much in terms of the quality of air they breathe and its effects on their brain and mental health.

 

3.2.3 The need for brain health to be built into air quality policy

Current public policies have been developed to mitigate the impact of air pollution on a variety of health outcomes – from asthma and heart disease to chronic obstructive pulmonary disease (COPD) and lung cancer. However, their implications for brain and mental health are only just beginning to be explored.12,13 There is a need for new policies that specifically address mental health, given that the effects of air pollution on the risk factors for vascular dementia and Alzheimer’s disease for example (such as dose response, pollutant mixture, pathways to disease), differ from those for exacerbating lung disorders such as asthma and COPD. In 2022, Castellani et al published the first policy agenda for mitigating the impact of air pollution on brain and mental health, including dementia. They identified three policy domains – research and funding; education and awareness; and policy evaluation – and 14 priority areas, each of which contain a set of immediate to long-term actionable items (See the Figure here and in the report). Some of these priority areas are explored further here.

 

1. Rethinking funding.

 

At present there are few if any research funding calls to explore the impact of air pollution on brain or mental health. To address the priority areas outlined in this domain, air pollution and public health funding needs to finance high-risk/high-reward research. Otherwise developing the evidence necessary to develop cost-effective, scalable, high-impact public policy, particularly in terms of the long-term historical impacts that air pollution has on cognitive decline, dementia and later-life neurodegenerative diseases, will remain intractable.11

 

2. Ensuring that the effects of air quality on brain health are recognised in existing environmental health strategies.

 

Policies directed at environmental health, such as clean air strategies, green urban planning, improving public transport, transitioning to net zero policies, healing ecosystems and promoting better diet and exercise also include benefits for brain and mental health. For example, third sector and governmental organisations focused on dementia or neurodegenerative diseases should emphasise the importance of clean air for brain and mental health. Similarly, school systems focused on cognitive development should consider that the impact of idling cars at drop-off and pickup, for example, is damaging to a young person’s brain.14,15 The sheer volume of cars on the roads not only increases air pollution but also stops parents from allowing their children to travel to school independently.16 Alternatively, clean air strategies could include improved mental health and wellbeing among their benefits, and link these to climate change and urban development strategies.

 

3. Engaging in co-production and participatory research.

Air quality is one of the more politically charged global social problems that affects communities worldwide. The politics of air quality are due, in part, to news and social media misinformation, the lobbying by industry and corporations, voting practices and vote share across different groups, the agenda of different political parties, the challenges of public transport, regressive car taxes on people on lower incomes, the complex ways in which clean air strategies are intertwined with economics and workforce issues, and the inability for citizens to directly see air pollution’s influence, particularly in the case of long-term impact. Given these ‘political’ complexities and associated power imbalances, inequities and inequalities, it is vital that stakeholders at local, regional or national levels are involved in developing this policy area. This will ensure that the conflicting needs of governments, businesses, organisations and citizens are kept at the forefront of policymaking and evaluation, which is crucial to removing barriers and improving levers to change.

 

4. Targeting policy at key points in the life-course.

with the two most important being early life, when brain development and mental health are critical, and later life, when people are more vulnerable due to ageing processes and the cumulative impact of lifetime exposures are evident. Improving indoor air quality in schools, homes and care homes is a good example, or requiring social and private landlords to install heat pumps to reduce both fuel poverty and carbon emissions.

 

5. Understanding the post-diagnosis impact of air quality on dementia and other mental health and brain-based disorders.

While the research on this topic is still in its early stages, if air pollution is found to accelerate certain brain or mental health disorders for specific groups of people, it would offer the potential for major advances in secondary and tertiary prevention, which would not only help to improve the lives of people, but also significantly reduce healthcare expenditure.

 

 

3.2.4 Summary

The link between air pollution and brain health, including early-life cognitive development and later-life neurodegenerative disorders such as dementia, Parkinson’s disease and cognitive frailty should be taken seriously and needs further research. The mental health burden of air pollution is not just an increase in diagnosed psychiatric disorders, but also a worsening of mental health in general. It disproportionally impacts vulnerable populations experiencing poverty, inequality and deprivation. Future air quality policy needs to recognise and aim to mitigate the impact of air pollution on brain and mental health.

 

Social Complexity Retreat based on The Atlas of Social Complexity

On the 12th and 13th of June, the Durham Research Methods Centre (DRMC) held its first Social Complexity Retreat based on The Atlas of Social Complexity. Our hope is that this will be an ongoing event.

For those who've not been on an academic retreat, it is not a workshop and is, most certainly, 180 degrees opposite of a conference. 

 

RETREATS ARE NOT WORKSHOPS OR CONFERENCES 

Unlike a conference or workshop, a retreat offers time for deep focus, personal reflection, and informal exchange, rather than a litany of scheduled talks or formal presentations. This slower pace allows participants to think more creatively, build stronger connections, and explore new ideas without the pressure to present finished work. However, as anyone who has led an academic retreat knows, the more structured and organised the event is, the more relaxed and free-to-think people become.

 QUICK OVERVIEW OF OUR RETREAT

The academic retreat held in Durham offered participants a valuable opportunity to step back from their everyday routines and focus on their own work. It created a space to think, write, and reflect without the usual pressures of email, meetings, or digital interruptions. Attendees were encouraged to treat the time as a break from daily responsibilities and to be as present as possible—keeping phones off, avoiding social media, and setting clear out-of-office messages.

Organised around the themes of the Atlas of Social Complexity, the retreat used the book as a starting point for thinking about the future of the field and how a “social complexity imagination” might help shape new research and methods. Participants brought their own projects, questions, and challenges into the mix, contributing to a rich and open exchange of ideas.

In addition to individual work time, the retreat included several creativity exercises and small group sessions. These activities encouraged participants to approach their research from fresh angles, share work-in-progress, and make use of the collective expertise in the room. Whether writing, talking, or taking time to reflect, everyone was given the freedom to define success on their own terms. The retreat was a chance to slow down, connect with others, and make meaningful progress.

For those potentially interested, we will announce via our social media and communication channels when we will host the next retreat. 

 

Peter Erdi's Two-Part Interview on The Atlas of Social Complexity

We would like to thank Péter Érdi for taking the time to conduct a two-part video interview with us about our new book, The Atlas of Social Complexity.

For those who do not know Péter, he is is a Hungarian-born computational neuroscientist who now lives in Michigan, United States where he is a Henry R. Luce Professor at Kalamazoo College.^[1][2] In his career he wrote several books and published (co-published) many scholarly articles in the fields of chemical kinetics, computational neuroscience and complex systems.  

Péter was also the editor of the Springer Series, Understanding Complex Systems, back in 2009, publishing my first book on complexity, Sociology and Complexity Science: A New Field of Inquiry. The Atlas is our follow-up and what folks should read for our current views.

BOTH VIDEOS ARE ON YOUTUBE

HERE IS THE LINK to Part 1 of the Interview

HERE IS THE LINK to Part 2 of the Interview 

 

 

 

 

The Atlas of Social Complexity. Chapter 30: Mapping the new methodological terrain

QUICK OVERVIEW AND LINKS TO THE OTHER THEMES AND CHAPTERS IN THE BOOK

 

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. Chapter 27 introduces the final theme of the book, Methods and complex causality. It begins with Chapter 28, Make Love, Not Models and then moves on to Chapter 29, Revisiting Complex Causality

 

The focus of the current blog post is (Chapter 30) is Mapping the new methodological terrain

 

 

HERE IS A REVIEW OF THE CHAPTER 

Outside the Methodological Comfort Zone: A Transdisciplinary Tour

Chapter 30 marks the culmination of our methodological tour through The Atlas of Social Complexity, and in many ways, it is the most demanding stop. Here, we abandon disciplinary dogma in favour of methodological adventure. The field of methods has become crowded, yes—but not congested beyond imagination. The problem isn’t the lack of tools; it’s how we think about and use them. If complexity is our landscape, then transdisciplinary, mixed-methods thinking is our means of traversal. And that, we argue, is best achieved by stepping outside one’s comfort zone.

 

We outline three methodological programmes for this next phase of disruptive, transdisciplinary social complexity science: Case-Based Complexity, Systems Mapping, and the emerging world of Approachable Modelling and Smart Methods (AM-Smart). These aren’t isolated tools. They share deep roots in configurational thinking, complexity theory, and the social sciences—and half are qualitative. That’s not a bug. It’s a feature.

 

 

A Quick Recap: Methods We’ve Already Covered

Before diving into the chapter’s new material, let’s briefly name the methods we have already explored throughout the Atlas:

 

• Dynamical Systems Theory (Chapters 13, 15, 25)
• Complex Network Analysis (Theme 2, Chapter 14)
• Complexity in Evaluation (Chapters 23, 24)
• Case-Based Complexity (CBC) – The philosophical and epistemological ground was laid in Chapters 20 and 29.

 

Chapter 30 is where the methodological rubber meets the road.

 

Case-Based Complexity: From Philosophy to Practice

CBC, building on the work of David Byrne and others, views cases not as data points but as complex systems unto themselves—emergent, dynamic, and situated. We categorise CBC into two camps:

 

• Computational approaches: Cluster analysis, case-based modelling, dynamic pattern synthesis
• Qualitative approaches: Process tracing, trajectory-based QCA (TJ-QCA)

 

Let’s take TJ-QCA. Developed by Gerrits and Pagliarin, it goes beyond conventional QCA by focusing on within-case temporal development. Unlike static, cross-sectional approaches, TJ-QCA constructs a case’s history as a trajectory through developmental stages—each a configuration of causal conditions. It embraces equifinality and multifinality, making it indispensable for complex temporal analysis.

 

Dynamic Pattern Synthesis (DPS), by contrast, developed by Philip Haynes, is a more policy-pragmatic tool. Using cluster analysis and cross-sectional comparisons over time, DPS maps dynamic trends without requiring set-theoretic logic. It’s more flexible and accessible for larger datasets.

 

Then there’s Case-Based Modelling, which I developed with colleagues to make computational techniques more available for applied social science. This platform gave rise to tools like:

 

• COMPLEX-IT: An R-based, Shiny-powered, mixed-methods tool that simulates and visualises complex systems.
• The SACS Toolkit: A blueprint for mapping complex systems using vector configurations.
• Case-Based Density Modelling (CBDM): A synergy of case-based analysis and synergetics, capable of forecasting trajectories across high-dimensional phase spaces.

 

 

Systems Mapping: Visualising Complexity for Collective Action

Systems mapping, as Barbrook-Johnson and Penn remind us, is a broad church. Whether qualitative or computational, its goal is shared understanding. From Participatory Systems Mapping (collaborative, cyclic, feedback-rich) to Rich Pictures (hand-drawn stakeholder maps), the emphasis is on meaning-making and group problem-solving.

 

Other tools include:

 

• Causal Loop Diagrams: Mapping reinforcing and balancing feedback systems.
• System Dynamics: A more formalised, equation-based technique for modelling stocks and flows over time.
• Bayesian Belief Networks: Mapping subjective beliefs about conditional causality.
• Fuzzy Cognitive Mapping: Perturbable models to explore how changes cascade across a conceptual system.

 

Each method has strengths and drawbacks, but all push us to visualise complex causality, engage stakeholders, and explore new terrains.

 

AM-Smart: Lowering the Barriers to Entry

The third programme, AM-Smart, is not a method but a movement. It asks: How can we make sophisticated methods more accessible to those outside the complexity club? Platforms like COMPLEX-IT, PRSM, and NetLogo exemplify this movement, lowering cognitive load through intuitive interfaces, visual reasoning, and iterative learning.

 

These platforms embody nine design principles—from scaffolding and feedback to productive failure and gaming environments. They do not replace expertise. Rather, they extend the reach of complexity science to educators, policy workers, and citizen scientists. As Schimpf and I argue, the AM-Smart field needs a proper research agenda—but it is already transforming how we do methods.

 

Final Word: Towards a New Ethos

If there’s one message, we hope readers take from Chapter 30, it’s this: methods are not merely tools. They are epistemological commitments. They are invitations to see the world anew. Social complexity demands a methodological imagination that is flexible, transdisciplinary, and above all, qualitative-friendly. That’s where the revolution lies. Let’s not miss it.