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09/04/2025

The Atlas of Social Complexity. Chapter 26: Resilience and all that Jazz

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.


The focus of the current post is CHAPTER 26: RESILIENCE AND ALL THAT JAZZ


QUICK OVERVIEW OF CHAPTER

Environmental sciences in a book about social complexity? Certainly. The study of ecologies and natural systems has generated a wealth of insights about complexity that cannot be ignored by social scientists. Importantly, the distinction between social and natural or physical systems is analytical only. In reality, social life is deeply embedded in all sorts of ecological processes. It is common to think of this embedding or coupledness in terms of socio-ecological systems. This part of the tour surveys the complexity of that coupledness, e.g., in terms of coevolution, and discusses its analytical implications for the study of social complexity. We will spend ample attention to the concept of resilience because it originates from the study of coupled socio-ecological systems, and in many ways has gained more traction than other concepts from the complexity sciences.

 


The idea that the natural state of the earth is being put under pressure as a consequence of humankind’s exploitation has a long history. While there may have been times when smoking chimneys were regarded as signs of improvement during the Industrial Revolution,[1] such romanticised images of societal progress have long since disappeared. The environmental crisis that has risen since then, as exemplified in e.g., the break-down of the ozone layer, acid rain, and, more recently, rising worldwide average temperatures, is clear for all but the most sceptical. The study of the environment and its reciprocal relationship with human activities continues to attract considerable scholarly attention. Importantly, this scholarly work has contributed significantly to the complexity canon. Consider, for example, how system dynamics modelling aids to understanding the feedback loops driving environmental change;[2] how the concept of coevolution articulates the reciprocity in coupled socio-ecological systems;[3] or how hysteresis helps understanding why it is incredibly difficult to restore ecological systems to a previous stable state once toppled into a new state.[4]

 

The inclusion of this intellectual strand in the Atlas is threefold. Firstly, it is one of the fields that was quick to recognize that the earth, the environment, the atmosphere, the ecosystems and their niches, etc. are best understood as complex systems.[5] In terms of a human example, fertilizers and climate change can impact the vegetation of a grassland (biotic) and the quality of the minerals and soil (abiotic) upon which the ecosystem depends, which leads to changes in biodiversity. This is why an ecological community is systemic and complex.

 

This insight takes us to the second reason why this theme should be explored in the Atlas: it is useless to think of ecosystems as existing independently from complex social systems. Humans and their environmental are fully intertwined.

 

The third reason follows from the previous two: of all concepts and methodologies gathered in the complexity canon, it is those from environmental sciences that – on the whole – appear to resonate strongly with a wider audience in their application to pressing societal problems.

 

One of those rich aspects is resilience.

 

Resilience, it must be stressed again, is a concept used just as much outside of social complexity or the study of SES, as it is within these fields.[6] Somewhat predictably, there are multiple and competing definitions of resilience.[7] This is not only because different applications ask for different conceptualizations, but it is also because the concept has been around long enough to be refined (but not discarded).

 

Arguably the most well-known version is the one introduced by Crawford Stanley Holling in 1973. In this seminal paper[8], he argues that resilience is present when systems bounce back after having been put under strain.

 

Clearly, there is more to it than this simple working definition. What, exactly, is a socio-ecosystem’s internal structure? How can one know a system’s limits, and, knowing that coupled systems are multifaceted instead of monoliths, how many limits does a system have? What are the timescales for short-term and long-term recovery? And, importantly, under what conditions can a system be said to have been restored to its original state? These are tough questions, but the last one is especially difficult to answer when it comes to social systems.

 

The main point is this: definitions and operationalizations of resilience are much harder than the general idea of resilience. It is very difficult to move from that general idea (that works as long as it is being kept simplistic and abstract) to concrete assessments of such resilience (that quickly can become something in the eye of the beholder).

 

The power of the concept is that it can be understood intuitively and that it offers an overarching concept that can host several other important aspects of complex systems, including regime shifts, punctuated equilibrium and hysteresis.[9] A such, it captures many aspects that we surveyed in this Theme 4 of the Atlas – and so, we suggest all readers engage this topic as it is not standalone. Instead, it is deeply tied to everything else we have surveyed on our tour.



[1] Jürgen Osterhammel, Die Verwandlung der Welt: Eine Geschichte des 19. Jahrhunderts, 4. Aktual. edition (München: C.H.Beck, 2009).

[2] D. H. Meadows, ‘Whole Earth Models and Systems’, CoEvolution Quarterly, 1982, 98–108.

[3] M. A. Gual and R. B. Norgaard, ‘Bridging Ecological and Social Systems Coevolution: A Review and Proposal’, Ecological Economics 69, no. 4 (2010): 707–17.

[4] Marten Scheffer and Stephen R. Carpenter, ‘Catastrophic Regime Shifts in Ecosystems: Linking Theory to Observation’, Trends in Ecology & Evolution 18, no. 12 (December 2003): 648–56.

[5] S. A. Levin, ‘Ecosystems and the Biosphere as Complex Adaptive Systems’, Ecosystems 1, no. 5 (1998): 431–36; Gregg Hartvigsen, Ann Kinzig, and Garry Peterson, ‘Complex Adaptive Systems: Use and Analysis of Complex Adaptive Systems in Ecosystem Science: Overview of Special Section’, Ecosystems 1, no. 5 (1 September 1998): 427–30; C. S. Holling, ‘Understanding the Complexity of Economic, Ecological, and Social Systems’, Ecosystems 4, no. 5 (1 August 2001): 390–405.

[6] Tuna Taşan-Kok, Dominic Stead, and Peiwen Lu, ‘Conceptual Overview of Resilience: History and Context’, in Resilience Thinking in Urban Planning, ed. Ayda Eraydin and Tuna Taşan-Kok, GeoJournal Library (Dordrecht: Springer Netherlands, 2013), 39–51; Ran Bhamra, Samir Dani, and Kevin Burnard, ‘Resilience: The Concept, a Literature Review and Future Directions’, International Journal of Production Research 49, no. 18 (15 September 2011): 5375–93.

[7] Patrick Martin-Breen and J. Marty Anderies, ‘Resilience: A Literature Review’, 2011.

[8] C. S. Holling, ‘Resilience and Stability of Ecological Systems’, Annual Review of Ecology and Systematics 4 (1973): 1–23.

[9] Joana Figueiredo and Henrique M. Pereira, ‘Regime Shifts in a Socio-Ecological Model of Farmland Abandonment’, Landscape Ecology 26, no. 5 (1 May 2011): 737–49; Marjolein Sterk, Ingrid A van de Leemput, and Edwin THM Peeters, ‘How to Conceptualize and Operationalize Resilience in Socio-Ecological Systems?’, Current Opinion in Environmental Sustainability, Sustainability governance, 28 (1 October 2017): 108–13.


08/04/2025

Advancing a social complexity imagination to disrupt place-based health complexities -- IRSPM Conference Presentation

It was great to present with my colleague Jonathan Wistow at the International Research Society for Public Management (IRSPM) Conference, hosted by the University of Bologna, Italy.

For those who attended our presentation or are just interested in what we discussed, here is an overview with links to a PDF of our presentation and our methods platform, COMPLEX-IT. See, also, this blog post by my co-author, Jonathan Wistow, as background for our argument. 



ABSTRACT

Advancing a social complexity imagination to disrupt place-based social complexities: Health inequalities and multiple conjunctural causation 


The complexity and persistence of health inequalities pose significant challenges to public management and service delivery, affecting both their understanding and response strategies (Wistow et al., 2015). For Salway and Green (2017), part of the problem is the miss-specification of system boundaries in public policy that fails to go far enough up the causal chain. Policy and research have neglected the complex causal factors that produce the socio-economic inequalities in which health inequalities are (re)produced, (Doyal with Pennell, 1979, Navarro, 2009, and Schrecker 2017), which leads us to extend a theoretical exploration of the issue, meeting a key aim of this panel. One potential solution is to employ a social complexity framework and corresponding methods that embrace rather than simplify these persistent complexities (Wistow et al., 2015).


 

We develop a ‘social complexity imagination’ as a form of creative destruction designed to drive science and its relationship with practice (Castellani and Gerrits, 2024). We do so by framing multi-level and multi-actor governance system responses to the deeply embedded and ‘wicked challenge’ of health inequalities within debates about how the social contract and political economy influences place-based social complexities (i.e., trajectories of unequal social and economic outcomes) and the scope and capacity for policy systems to respond to these (Wistow, 2022). Health inequalities are a key measure of the unequal and deeply embedded trajectory of places in a post-industrial society like England and require disruption to the social contract in order to enable policy and public management to be more effective.

As demonstration, we analyse a longitudinal (pre and post) place-based dataset of all English local authority administrative areas. This was designed around a social determinants of health (SDH) perspective (see, Marmot et al., 2020) and Public Health Outcomes Framework (Office for Health Improvement & Disparities, n.d.) for England alongside some additional health service and place-based contextual data. The dataset covers a wide-range of factors relating to social complexities and trajectories of place.

In terms of methodological innovation, we will use the COMPLEX-IT platform to explore place-based complex conjunctural causation around two significant health inequalities outcomes – life expectancy and healthy life expectancy, which represent systems outcomes at the level of local authorities (as places and policy systems). COMPLEX-IT is a case-based, multi-methods platform employing the tools of computational social science to facilitate exploring and analysing complex data and support applied social inquiry (Schimpf and Castellani 2022). COMPLEX-IT provides a bespoke suite of techniques: cluster analysis, machine learning, data visualisation, data forecasting, case-based scenario simulation and, case-based systems mapping.

Through the findings and analysis, we identify elements of health inequalities that are amenable to local complexity-informed public management policies and service delivery and those that are less tractable to interventions at the local level alone and, therefore, require intervention higher up the causal chain via national place-sensitive policy and interventions. Through the methodological exploration of the issue, we contribute to the second aim of the panel and use this to extend our theoretical exploration, which contributes to the first aim.

 

CLICK HERE for the PDF of our Presentation

CLICK HERE to explore our methods platform, COMPLEX-IT

 

 

 

 


26/03/2025

Air Pollution, Brain Health and Dementia. Presentation at the Scottish Air Quality Annual Seminar 2025

Much thanks for David Hector (Air Quality Consultant at Ricardo) and team for the opportunity to present at the Scottish Air Quality Annual Seminar 2025.

My presentation focused on the impact of air quality on brain health and dementia. 

I am Director of the InSPIRE, a research and policy consortium for mitigating the impact of air quality on brain health, mental health and dementia. 


AIR POLLUTION IMPACTS BRAIN HEALTH AND DEMENTIA?

YES IS DOES. . . . 



For those new to this link, the relationship between air pollution and brain health is no longer speculative. It is systemic, cumulative, and deeply concerning. Fine particulate matter (PM2.5), nitrogen dioxide, and ultrafine particles don’t simply irritate the lungs; they infiltrate biological systems, cross the blood–brain barrier, and trigger neuro-inflammatory cascades. Over time, these processes contribute to accelerated cognitive decline, structural brain changes, and an increased risk of dementia. 

It can also accelerate the progression of brain diseases, including neuro-degenerative disorders and dementia spectrum. 

But the impact is not linear. It is complex, shaped by a web of social, environmental, and biological interactions. Socioeconomic disadvantage amplifies vulnerability. Early-life exposure compounds later-life effects. And urban infrastructure, shaped by policy and planning decisions, becomes a silent architect of neurological health disparities.

This is not just a public health issue. It is a complexities of place (i.e., complex systems) problem. The brain does not sit in isolation; it is nested within a bio-social ecology. Complexity science invites us to move beyond single-cause models and toward dynamic, multi-level analyses that capture the interplay of genes, pollutants, social stressors, and institutional failures. 

The implication is clear: mitigating air pollution is not only about cleaner air; it is a long-term investment in cognitive equity, ageing resilience, and the mental health of future generations.

 

CLICK HERE to download the PowerPoint for my presentation

CLICK HERE to visit the InSPIRE consortium website on brain health and air quality. 

CLICK HERE to visit the Scottish Air Quality website and mapping of Scotland air quality.



The Atlas of Social Complexity. Chapter 25: Economics in an unstable world

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. And Chapter 24 focused on The Challenges of Applying Complexity.   


The focus of the current post is CHAPTER 25: ECONOMICS IN AN UNSTABLE WORLD


QUICK OVERVIEW OF CHAPTER

The premise of complexity economics is as simple as it is powerful: while neoclassical economics essentially assumes equilibrium as the natural state of an economy, complexity economics assumes nonequilibrium states because economies are essentially open systems and therefore prone to continuous interaction with their environment. As simple as this premise is, it slashes away one of the main pillars underneath conventional economics. Given the importance of conventional economics, and its impact on society, we survey what scholars in complexity economics claim in response to conventional economics. This is what the chapter is about. We review the arguments in favour of a complexity approach to economics, with special focus on the economy as a complex adaptive system, the role of agency, and the networked nature of economic systems. We also review the progress that has been made in measuring and indexing the levels of complexity in economies, i.e., economic complexity.

 

A BIT MORE DETAIL

Despite decades of computational advancement, economic models still falter when it comes to forecasting systemic shocks—precisely because they remain grounded in assumptions of equilibrium and linearity. Yet the economic world, as Brian Arthur argued, thrives in a state of nonequilibrium. The economy is an open, evolving system, rife with endogenous feedback, punctuated changes, and adaptive agents. Complexity economics, emerging from this epistemological shift, takes such disequilibrium as foundational. It offers a conceptual toolkit more attuned to recursive feedback, historical contingency, and emergent macrostructures. Arthur’s theory of increasing returns, for instance, upends conventional ideas of diminishing returns by showing how nonlinearities and path-dependencies destabilize systems. Complexity, here, is not metaphor but mechanism.

 

This reorientation is not without lineage. Institutional and evolutionary economists—Dopfer, Foster, Hölzl, Norgaard—have long treated variation, selection, and retention as endogenous features of economic systems. These frameworks converge in their recognition of economic systems as entangled, co-evolving with social, ecological, and technological networks. The complexity sciences, then, do not offer a singular theory but a pluralistic, even contradictory, assemblage of methods and insights that together animate a new economic imagination.

 

Agency in economics

One of the central provocations of complexity economics is its rejection of the rational actor model. Agents, contrary to neo-classical dogma, are myopic, adaptive, and embedded. They operate under bounded rationality, responding not to complete information but to contextual cues and semiotic frames. Human behavior does not follow transitive preferences or utility maximization; it evolves, shifts, and resists aggregation. As such, the micro does not scale simply to the macro—a point emphasized by Haken and mirrored in the meso-level theories of Dopfer and colleagues. The rules governing economic life—whether formal, institutional, or informal—are not static but emergent, often self-organizing through networks of meaning, habit, and symbolic exchange. Agent-based modelling (ABM) enters here as a vital methodological intervention, revealing how simple rule sets give rise to complex social and economic patterns. But more than technical prowess, ABM affirms a deeper epistemic shift: from prediction to generativity, from equilibrium to exploration.

 

Economic networks

If agency is fluid and rules are emergent, then the architecture of economic life must be understood as networked. Networks—technological, transactional, institutional—structure the pathways of innovation, adaptation, and collapse. These are not simply nodes and links, but dynamic assemblages of information, value, and power. Importantly, they defy the territorial logic of traditional economic systems. Brexit, for example, reveals the folly of trying to draw stable boundaries around open systems. Instead, what complexity economics emphasizes is the entanglement of economic, social, and environmental systems—each evolving through feedback, punctuated shifts, and tipping points. The benefit here is methodological: economic data, unlike most social indicators, offer traceable, empirical patterns of complexity—if, and only if, one adopts the right lens.

 

Complex indices of economic diversity

Perhaps the most tangible effort to operationalize economic complexity comes through indices that map diversity and specialization across systems. These indices, built on network science and configurational logics, attempt to measure the structural complexity of economies. While promising, such metrics risk reductionism if disconnected from the causal logics of emergence, feedback, and agency. Still, their empirical heft is undeniable, especially when linked with evolutionary dynamics. They signal the beginnings of a quantified complexity science in economics—one capable of capturing proximity, path-dependence, and structural lock-in.

 

Future trajectories

Looking forward, complexity economics sits alongside its neoclassical kin not as a replacement but as a parallel paradigm. Yet, for its full potential to be realized, it must move beyond critique. Its most fertile terrains lie in configurational, realist ontologies and richer theories of agency. Not simply as correctives, but as generative tools for modelling an economic world that is irreducibly complex, radically open, and perpetually becoming.

 


24/03/2025

Much thanks to Orion Maxted and team at the Imaginary Institute, The Centre Leo Apostel, University of Brussels, for the chance to present on our book, The Atlas of Social Complexity.

 

CLICKHERE to read more about the Institute

CLICK HERE for the PDF of our PowerPoint.

 

Here is a quick summary of our session, which focused on the last chapter of the Atlas of Social Complexity, CH32 THE UNFINISHED SPACE:

 

The Imaginary Institute calls for a radical rethinking of how we create the conditions for new ideas – how we build spaces that invite the unknown, where knowledge is not a monument but an ever-expanding terrain. In this session, we turn to the final chapter of Brian Castellani and Lasse Gerrits' The Atlas of Social Complexity, The Unfinished Space, which emerged from 41 interviews with scientists, artists, and practitioners across politics, law, physics, sociology, and beyond. Their collective concern? How do we ensure that the study of social complexity remains disruptive, refusing ossification, always on the edge of new discoveries? The answers align with the ethos of the Imaginary Institute: unfinished spaces, where knowledge remains open-ended; the art of incompleteness, embracing uncertainty; unease and discomfort, resisting closure; rhizomes, networks without hierarchy; permeability and pores, keeping disciplines porous; the terrain not yet grasped, always pushing beyond; organizing emergence, cultivating generative collaborations; becoming transdisciplinary, transcending intellectual silos; and becoming educated, shaping how we learn complexity. Our goal of this seminar is to explore these themes with the group to share our various experiences around creating the conditions for the manifestation of urgent new ideas, to envision beyond the present and bring forth what does not yet exist.



15/03/2025

The Atlas of Social Complexity. Chapter 24: The Challenges of Applying Complexity

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.

 

The focus of the current post is CHAPTER 24: THE CHALLENGE OF APPLYING COMPLEXITY

 

OVERVIEW OF CHAPTER

One development clearly visible on the map of thecomplexity sciences is that most of the recent advances in the study social complexity can be found in applications to real world issues, ranging from urban planning in derelict neighbourhoods to social work with disadvantaged groups. 

 

Why? 

 

It is in practical, every-day situations where complexity is felt most pressingly. There are no straightforward answers to complex issues such as poverty, inequality, climate change or conflict resolution. It is also the space where ideas are put to true tests. Things may work in the highly stylized conceptual environments of the complexity sciences, but the real proof is in the confrontation with real social life. Survival in the face of those complex situations is a good indicator for the robustness of an idea.

 

Enter the study of social complexity.

 

Any survey of living in social systems would be incomplete without talking about the challenges of applying the complexity sciences and the study of social complexity to issues of policy and practice. Note our phrasing here. The current policy and practice literature in the complexity sciences is rather clear: there is an urgent need to apply a complex systems approach to public policy planning, implementation and evaluation.[1] What is less clear, as we saw in chapters 21 through 23 of our tour, is how to do this effectively.[2]  Research and practice have shown mixed results, due to a series of challenges. A short list includes: a strong tendency to model or describe public policy issues in complex systems terms instead of interrogating the development, implementation and evaluation of systems-level interventions; policy makers and practitioners and funding organisations being biased toward simple, individual-level, short-term solutions (sometimes based on clinical trials); academics being tone deaf about the roadblocks to applying complexity to public policy and practice; the need to focus more on stakeholder engagement; an overemphasis on computational models; and a confusion about or obfuscation of complexity terminology.

 

 

Fortunately, there are hard-won practical solutions to these challenges that researchers and practitioners have identified: some focus on what appears effective (system-level interventions grounded in co-production); others on what is needed next (e.g., switching from complex interventions to interventions in complex systems). What we found fascinating is that many of the solutions found in the literature were echoed by the practitioners and policy experts we interviewed for this book. All of which gave the focus for the current chapter: we sought to combine the current literature and interviews to have a very practical discussions about the challenges of applying complexity.

 

Here are some of our key points:

  • Conventional approaches fail to grasp the interdependent, uncertain nature of these challenges, making complexity a necessary alternative. However, translating complexity theory into practice requires more than applying models—it demands a cognitive shift.
  •  Practitioners experience complexity as a conceptual liberation, breaking from hierarchical, reductionist thinking. Yet, theory-to-practice translation is fraught with obstacles: bureaucratic resistance, political agendas, and time constraints.
  •  Complexity’s greatest strength lies in its heuristic power—providing metaphors, analogies, and guiding principles (e.g., self-organization, emergence) that reframe problems rather than dictate solutions.
  •  Some concepts, like resilience and coevolution, thrive in practice, while others, like attractor basins, remain too abstract. Yet, the field struggles with key omissions—power, agency, and accountability. Without engaging these forces, complexity risks irrelevance.
  •  Ultimately, the study of social complexity does not provide solutions but opens new epistemological spaces. It shifts perception, helping practitioners unlearn rigid assumptions and reimagine possibilities. The challenge is not just modeling complexity but embedding it into lived realities, ensuring its insights resonate beyond academia.

 




[1] Pete Barbrook-Johnson et al., ‘Policy Evaluation for a Complex World: Practical Methods and Reflections from the UK Centre for the Evaluation of Complexity across the Nexus’, Evaluation (SAGE Publications Sage UK: London, England, 2021). Junus M. van der Wal et al., ‘Advancing Urban Mental Health Research: From Complexity Science to Actionable Targets for Intervention’, The Lancet Psychiatry 8, no. 11 (2021): 991–1000.

[2] Barbrook-Johnson et al., ‘Policy Evaluation for a Complex World’.