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

 

 

 

 

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.

 

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.

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.
  

 

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[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’.

The Atlas of Social Complexity. Chapter 23: Governance, Politics and Technocracy

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 SocialScience (Chapter 20). From there we move to the Complexities of Place (Chapter 21); followed by Socio-technical Life (Chapter 22). 

 

The focus of the current post is CHAPTER 23: GOVERNANCE, POLITICS AND TECHNOCRACY

 

OVERVIEW OF CHAPTER

We are occasionally mystified that complexity scholars who understand their subject very well can at the same time come up with policy recommendations that are so wide off the mark that one should not be surprised that no one listens.

The term ‘wicked problems’ is never far away once the discussion turns to how the complexity sciences can inform government and public policy. Rittel and Webber’s[1] seminal 1973 paper[2] observed how scientists are good at developing recommendations for those instances where there is agreement about the goals to be reached, and that have a known problem structure. In such cases, science can identify what must happen in order to close the gap between the current situation and the desired system state. These are ’tame problems’. Somewhat unfortunately, most complex societal issues lack both aspects. Not only is the structure underneath the problem poorly understood, but there is also little agreement about what desirable future(s) should be pursued. These are the type of ‘wicked problems’ that challenge complexity scientists. They cannot be solved by throwing more (computational) science at it because the lack of consensus about the desired future means that the space of possibilities increases a manifold.

The reason why, some 50 years later, we draw this paper back into focus when it comes to the conjunction of societal problems, public policy and complexity is that there is a tendency for complexity scientists to do exactly what Rittel and Webber warned us for half a century ago: to throw more science at it.

 

Wicked problems are real, but their causal complexity is only one aspect of it. Normative ideas about what is good[3] cannot be settled through science.

 

Our comment in Chapter 3 that the complexity sciences can appear tone-deaf to the real-world stems from exactly this point. The complexity sciences have a veneer of technocracy, which is not very useful as a solution direction. It is one thing to get a better understanding of a seemingly intractable problem, it is quite another to believe that science can be the arbiter about what constitutes the best solution.

 

This does not imply that science has no role to play in societal issues, beyond the science itself. Most certainly, we are convinced that science must speak truth to power.

 

Our main argument for wicked problems is that politics are central to the equation in ways they are not in tame problems. In terms of the study of social complex, this means that any analysis of a real-world societal problem must include the complexities of politics and policy.[4] There is no use in analysing such problems and devising solutions for them if one treats government as a black-box-implementation-machine that will just carry out whatever scientists think is the best solution. Frustrating? Perhaps. But this is the reality, and the complexity sciences must deal with that reality. Hence our journey for this part of the tour. In Chapter 23, we discuss how governance and politics have become interwoven in societal networks and need to be understood as such. Ample attention goes to that one big blind spot in the study of social complexity: power. We discuss how power may be understood from a complexity perspective. Last but definitely not least, we demonstrate the need for dialogue instead of technocracy if one wishes to achieve real impact in today’s complex societies.

 

While certainly not everything in the chapter, we thought it useful to provide a list of things that need to be given focus or attention for those keen on using social complexity to study governance and policy. Here are the key takeaways from the chapter:

• Empirical Engagement Over Abstraction: Complexity science should prioritize empirical grounding by engaging directly with governance and policy challenges rather than relying solely on abstract modelling.
• Integrate Complexity with Political Science & Policy Studies: Bridging insights from complexity science with political science, public administration, and policy studies can improve real-world applicability.
• Recognize the Role of Power in Complexity: Complexity-informed governance studies must incorporate power dynamics, including issues of dominance, inequality, and structural constraints on decision-making.
• Use Complexity Science to Navigate Policy Uncertainty: Policy-making occurs in a dynamic and uncertain environment; complexity tools can help identify emergent risks, feedback loops, and unintended consequences.
• Develop Configurational Approaches to Policy Analysis: Recognizing that governance outcomes depend on specific contextual configurations, case-based complexity and conjunctural causation should be integrated into policy studies.
• Adopt Fitness Landscape Models for Governance Networks: Policy actors navigate governance networks like fitness landscapes, where positioning, negotiation, and adaptability shape policy effectiveness.
• Enhance Policy Adaptability & Responsiveness: Policies should be designed to be flexible enough to respond to local conditions while maintaining coherence at broader levels.
• Engage in Cross-Disciplinary Dialogue: Scholars from complexity science and governance studies should collaborate to refine methodologies, ensuring complexity models reflect real-world governance processes.
• Leverage Complexity for Policy Implementation Insights: Complexity science can help explain why policies succeed or fail based on local contingencies, feedback loops, and adaptive learning mechanisms.
• Use Complexity as a Sense-Making Tool: Instead of focusing solely on predictive modelling, complexity science should be applied as a framework for sense-making and scenario analysis in governance.
• Incorporate Metaphors & Narrative Framing: Complexity concepts should be communicated using accessible metaphors and narratives to bridge the gap between academia and policymakers.
• Move Beyond Technocratic Approaches: Complexity science should not reinforce technocracy but engage meaningfully with political realities, democratic processes, and stakeholder participation.
• Prioritize Real-World Engagement Over Theoretical Purity: Complexity scholars should engage directly with policymakers, governance networks, and stakeholders to refine their models based on practical governance challenges.
• Improve Policy Resilience through Complexity-Informed Strategies: Recognizing that policies function within dynamic, interconnected systems, complexity science can help policymakers design more resilient and adaptive strategies.
• Encourage Translational Research & Knowledge Brokerage: Complexity scholars should work with policy practitioners to translate complex concepts into actionable governance insights.
• Reconceptualize Power as Emergent & Relational: Instead of viewing power as an individual attribute, complexity science should analyse how power emerges from networked interactions and structural conditions.
• Balance Between Modelling & Empirical Research: While formal models are useful, they should be informed by and tested against real-world governance cases to ensure relevance and impact.

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[1] Unsurprisingly, Rittel was a professor in the science of design, and Webber a planner and planning scholar. As we noted in Chapter 24, planners are among the people acutely confronted with real-world complexity.

[2] Horst W. J. Rittel and Melvin M. Webber, ‘Dilemmas in a General Theory of Planning’, Policy Sciences 4, no. 2 (1 June 1973): 155–69.

[3] A central tenet in political science, public administration, and other related strands, is that there is no such thing as the ‘common good’. The term is often used to justify a certain goal, but such is society that the goal will always be contested.

[4] David Colander and Roland Kupers, Complexity and the Art of Public Policy: Solving Society’s Problems from the Bottom Up (Princeton University Press, 2014).