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).