The Atlas of Social Complexity. Chapter 13: Human psychology as dynamical system

As I stated in my previous post, the second major content theme in The Atlas of Social Complexity is the Dynamics of Human Psychology. In my previous post I provided a quick summary of the theme.

 

The focus of this post is the first chapter in this theme, Human psychology as dynamical system (Chapter 13).

 

 

SUMMARY OF CHAPTER

Our theme on human psychology begins with the basic question: How best should we view the human condition? Is there really a definitive answer to this question? No, there is not. But, for sure, at least for the purposes of scientific inquiry, some answers seem better suited or more applicable, particularly for helping us out of certain path dependencies in thinking and treating people.

 

Dynamical systems theory is just such an answer, as it not only provides a different view of human psychology, but it also overcomes many of the limitations in thinking that current conventional approaches in statistical and qualitative methods cannot, on their own, get past.

 

Still, as we have already indicated, that does not mean it lacks its own problems. For mathematicians and non-mathematicians alike, dynamical systems theory is by no means an easy-going field of study. Some of its areas of research – chaos theory, swarm behaviour, complexity science – have certainly become part of mainstream culture, and the visualisation of these ideas is amazingly intuitive, allowing non-experts access to some of its key ideas. But serious study of human behaviour as an evolving dynamic system is hard work that requiring significant transdisciplinary engagement amongst mathematicians and social scientists working together at the intersection of complexity, methods, and psychology. Otherwise, this field can fall prey to many of the traps created by the thirteen challenges – in fact, in many ways, it is presently struggling with these challenges, as we shall see in a moment. The research we will review here, then, points to an adjacent possible, a potential way out.

 

This chapter first introduces the main the mathematical concepts central to the field, from bifurcation points to continuous dynamical systems; and, second, surveys dynamical psychology’s nine core realities about human psychology. The goal is to point readers toward the most promising research in this transdisciplinary area of social complexity.

 

 

KEY WORDS: Dynamical systems theory, dynamical psychology, differential equations, continuous dynamical system, differential equations, synergetics and psychology.

Improving climate change resilience in healthcare: Japan and the UK Workshop hosted by AMS and JSPS.

I would like to thank the UK Academy of Medical Sciences (AMS) International Policy Arm and the Japan Society for the Promotion of Science (JSPS) London for the opportunity to present and participate in their two-day international policy workshop.

 

The workshop focus was on Improving the resilience of health and public health systems to the impact of climate change: learning between Japan and the UK.

 

OVERVIEW OF MY TALK

My talk focused on two things.

 

1.  Developing interdisciplinary methods that facilitate a more in-depth understanding of healthcare resilience in the face of climate change.

 

2.    Applying these to my particular topic area: EnvironMental Health and the Exposome, with a particular focus on place and living in complex systems.

 

 

For my talk, I introduced COMPLEX-IT, the R shiny platform that my colleagues and I developed, which is free for online use or downloading to run in R Studio.

• 
  CLICK HERE for a PDF of my presentation.

• CLICK HERE for more on the complexity sciences.
  

• For more on the field of social complexity, see our new monograph, The Atlas of Social Complexity.
  

• For more on our work on EnvironMental Health and the Exposome, see our research and policy consortium, InSPIRE.

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I also had the chance to then attend, on the last day, the  AMS & The Lancet-International Health Lecture 2024 on “Climate crisis, cities and health” on 17 October, delivered by Prof Mark J Nieuwenhuijsen, who is Research Professor and Director of the Urban Planning, Environment and Health Initiative, and Director of the Air pollution and Urban Environment Programme at ISGlobal in Barcelona Spain. The manuscript of the lecture has now been published in The Lancet online: Climate crisis, cities, and health - The Lancet. A video of the lecture can be found here.

 

The Atlas of Social Complexity. Content Theme 2: The Complex Dynamics of Human Psychology

As I stated in my previous post, 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 content theme concerns the dynamics of human psychology. Here is a quick summary of the theme.

 

COMPLEX PSYCHOLOGY

There is no defined field of study called complex psychology. There are only somewhat different areas of research that could be connected – although, there is the whisper of a theoretical framework, namely dynamical systems theory. This lack of explicit awareness and synthesis puts the study of psychological complexity at a disadvantage because most psychologists and complexity scientists draw on only parts of the field. Our tour seeks to remedy this problem by taking readers on a journey through this research and synthesising them into a working complex psychology.

 

This theme is comprised of four chapters:

• Human psychology as dynamical system (Ch 13)
• Psychopathology of mental disorders (Ch 14)
• Healing and the therapeutic process (Ch 15)
• Mindfulness, imagination and creativity (Ch 16)

 

These chapters follow, in order, four key questions these scholars ask in their work. Chapter 13 begins with the most fundamental: How best should we view the human condition?  Chapter 14 asks: What is a mental disorder and how best should we measure and assess it? Chapter 15 asks: What does complexity have to say about the healing and therapeutic process? And, finally, Chapter 16 asks: What can the study of social complexity say about the positive aspects of our human psychology, and the power and healing potential of mindfulness, imagination, and creativity?

 

CLICK HERE to purchase the book, or to request it for your library.

 

 

KEY WORDS: dynamical psychology, symptom networks, dynamics of therapy, psychopathology, mindfulness, creativity.

The Atlas of Social Complexity. Chapter 11: Human-Machine

As I stated in my previous post, the first major content theme in The Atlas of Social Complexity is Cognition, Emotion and Consciousness.

• 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, the final one for this section and the focus of the current post, is about human-machine intelligence.

CLICK HERE to purchase the book, or to request it for your library.

 

HUMAN-MACHINE, A quick summary

AI is everywhere today. And it happened quickly. What does it all mean for human consciousness?

As we navigate the Digital Anthropocene, AI and technology provoke critical questions about how our cognition, emotions, and awareness are evolving alongside these new technologies. In particular, they raise quesdtions about the current development and future evolution (as a species) of our cognition, emotion and consciousness in relation to technological systems. Chapter 11 of the Atlas explores these questions. Our guide for this journey is the American literary critic and posthumanist scholar, Katherine Hayles, and her theory of human-technical cognitive assemblages, as outlined in her book, Unthought.

To put Hayles’ framing to work, we do three things in this chapter.

• We define what she means by human-technical cognitive assemblages.
• We rework her definition of machine cognition to better align it with the study of social complexity.
• We set the stage for Chapter 22, in which we spend considerable time exploring our current complex system of systems of digital machines and our posthuman condition.

Here is a glimps at some of our conclusions from this chapter.

The inability of machine cognition to explain itself is why scholars refer to machine learning as a ‘black box’. We know how to programme machine cognition using artificial neural nets, genetic algorithms or computational models; but we often have little insight into how machine cognition arrives at its conclusions because these machine are ignorant of what they do, beyond the output they provide and the data upon which they are trained. Case in point is Cliff Kuang’s New York Times article, Can A.I. be taught to explain itself?[1] In the article, Kuang explains that “as machine learning becomes more powerful, the field’s researchers increasingly find themselves unable to account for what their algorithms know – or how they know it”. 

This gets to a core problem of nonconscious cognition: while it extends our cognitive and emotional life and our consciousness to a near global level, it still requires a significant degree of attendant human intelligence, involvement, management, guidance, or control. This core problem also points to a wider and as yet unaddressed problem in our travels: complexity. Machine cognition is very good at ‘difficult’ and ‘complicated’, processing information and large amounts of data, in brute force, at speeds that are humanly impossible; but human cognition is still better at complexity. Winning at chess is one thing, but winning in diplomacy is another. Human consciousness needs to retain is executive function. The Self evolved and emerged for a reason: complex living systems, even when their embodiment is extended to the mechanical and digital, require guidance, even when that executive function is limited by its own consciousness.

Or at least for now.

What we will become, as posthuman cyborgs, over the course of the several hundred years, given our increasing integration into and cognitive dependency upon a global network of human-machine cognitive assemblages, is difficult to determine. One thing, however, is for sure: any exhaustive study of human cognition, emotion and consciousness needs to contend, at some point, with this newly emerging form of human evolution, as we move through the early stages of the Digital Anthropocene.

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[1] https://www.nytimes.com/2017/11/21/magazine/can-ai-be-taught-to-explain-itself.html

KEY WORDS: Machine cognition, actor network theory, new materialism, posthumanism, transhumanism, human-technical cognitive assemblages.