The Atlas of Social Complexity. Chapter 28: Make Love, Not Models

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.

 

 

The focus of the current chapter (Chapter 28) is Make Love, Not Models and explores the challenges of modelling methods today. 

 

 

OVERVIEW OF CHAPTER

This chapter revisits a recurring tension in the modelling of social complexity: the allure of elegant abstraction versus the demands of real-world complexity. It begins with a seminar—an impressive model of crowd behaviour, mathematically refined, visually striking. Yet beneath its surface lies a telling absence: no data, no context, no engagement with the psychological or social dynamics of human crowds. This moment crystallises a larger issue. The chapter explores how certain strands of complexity science, particularly those rooted in the physical-computational tradition, risk prioritising form over substance. These approaches, while technically sophisticated, often assume that methods developed for natural systems can be seamlessly transferred to the social world. But social systems are not just complex—they are contextual, reflexive, and shaped by meaning, memory, and power.

Midway through, the chapter reflects on the limits of abstraction. Models such as Kauffman’s NK framework or power-law-based network theories offer valuable insights but struggle to translate into domains where variability, interpretation, and lived experience are central. Rather than dismiss these tools, the chapter calls for more grounded modelling—work that stays close to the systems it seeks to understand, blending formal methods with qualitative inquiry and participatory design. Social systems require models that support learning, not just prediction. They call for a shift from control to collaboration, from mechanical metaphors to ecological ones.

 

Ultimately, the chapter advocates for a modelling practice that is humble, pluralistic, and open to complexity in all its forms. It is not a rejection of mathematics but a rebalancing of method and meaning.