philosophy

Complexity, Brains, and Machines: Reflections on the AI-ABC Workshop

What connects the brain, the immune system, a city, an ant colony, and a large language model? The answer, explored intensively at the recent AI-ABC Workshop on Complexity Science, Complex Adaptive Systems, Neural Networks and AI, is this: all of them are complex adaptive systems — networks of interacting components that give rise to organised behaviour no single part could produce alone.

The workshop was a rich, hands-on exploration of ideas that sit at the intersection of science, technology, and philosophy of mind. It was, by any measure, a very successful one — the kind of gathering that generates more questions than it answers, and does so productively.

What Is a Complex Adaptive System?

A Complex Adaptive System (CAS) is characterised by several defining properties. It is composed of many interacting agents or elements — neurons, cells, people, software modules — each following local rules. Yet the system as a whole displays emergent properties: patterns, behaviours, and capacities that cannot be predicted simply by examining the parts in isolation.

CAS are also adaptive: they learn, reorganise, and evolve in response to their environment. They are neither fully ordered nor fully random — they operate at what complexity theorists call the edge of chaos, a regime of maximal flexibility and computational power.

Examples span every scale of nature and society: ecosystems, economies, immune systems, cultures, brains, and — increasingly — artificial intelligence architectures.

Neural Networks Through a Complexity Lens

One of the workshop’s central threads was the relationship between biological neural networks and their artificial counterparts. Modern deep learning systems — transformers, recurrent networks, convolutional architectures — are, in a technical sense, artificial neural networks. But how closely do they mirror the complexity of the brain?

The brain is not merely a network of weighted connections. It is a living dynamical system operating across multiple timescales simultaneously, self-organised through development, experience, and ongoing metabolic processes. Its computational power arises not from any single module but from the coordinated dynamics of billions of neurons operating in concert — and in tension.

Artificial neural networks, by contrast, are trained rather than developed, optimised rather than adaptive in the biological sense. They can perform remarkable feats — image recognition, language generation, strategic game-playing — but they remain brittle in ways biological systems are not. They fail catastrophically on inputs slightly outside their training distribution. They do not generalise the way humans do.

The workshop invited us to ask: what would it mean to build AI systems that were genuinely complex and adaptive — not just large and powerful, but dynamically organised in ways that resemble living systems?

AI as a Complex Adaptive System

Current AI systems are often discussed as tools — instruments that humans use to accomplish tasks. But from a complexity science perspective, they are increasingly becoming participants in complex adaptive systems: embedded in social, economic, and informational ecosystems, shaping and being shaped by the environments in which they operate.

This framing has profound implications. In a CAS, there is no central controller. Behaviour emerges from the interactions of many agents following local rules. When AI systems enter social ecosystems — recommending content, mediating communication, influencing decisions — they become agents in a system whose emergent properties no one has fully designed or can fully predict.

Understanding AI through the lens of complexity science is therefore not merely a theoretical exercise. It is a prerequisite for responsible deployment. We need to ask not just “what does this model do?” but “what kind of system does it help bring into being?”

Coordination and Synchrony

A particularly generative theme was coordination: how do elements in a complex system synchronise, differentiate, and maintain coherent organisation over time? In biological systems, coordination dynamics — the science of how coupled oscillators and distributed networks achieve flexible, context-sensitive entrainment — offers a powerful framework for understanding everything from motor control to social interaction.

In artificial systems, coordination is typically engineered: attention mechanisms, synchronisation protocols, shared memory architectures. But the workshop raised the question of whether richer, more biologically-inspired coordination mechanisms might yield more robust and adaptable AI — systems capable of the fluid, context-sensitive integration that characterises human cognition.

A Productive Day

The AI-ABC workshop brought together researchers, clinicians, engineers, and thinkers from different disciplines, united by a shared conviction: that understanding complex systems — and building AI responsibly — requires frameworks that go beyond linear causality and simple input-output models.

The conversations were lively, the ideas were genuinely challenging, and the sense of intellectual community was palpable. (Photos from the day are coming — watch this space.)

If the goal of such a workshop is to expand the conceptual vocabulary of everyone in the room, this one succeeded. Complexity science is not a niche specialty. It is a way of seeing — and in a world increasingly shaped by adaptive systems both biological and artificial, it is a way of seeing we urgently need.

Reimagining Psychotherapy: When Symptoms Are Attractors, Not Causes

What if the symptoms we try so hard to eliminate are not the problem, but the solution — at least from the perspective of the nervous system? This is one of the most provocative and generative questions animating current discussions in psychotherapy research, and it was at the heart of a structured discussion I recently participated in at the Society for Psychotherapy Research (SPR) Annual Conference in Osaka, 2026.

The Attractor Metaphor

In dynamical systems theory, an attractor is a state — or set of states — toward which a system tends to evolve over time, regardless of where it starts. Think of a ball rolling into a valley: no matter where you release it on the hillside, it will settle at the bottom. The valley is the attractor.

Now imagine that the valley is depression, or chronic anxiety, or a pattern of emotional dysregulation. The person keeps returning to this state — not because they want to, not because of moral weakness or poor coping — but because the system has organised itself around this pattern. It has become stable. It has become, in a very real sense, the path of least resistance.

This is what we mean when we say that symptoms can be attractors. They are not random misfirings or mere symptoms of some deeper hidden cause. They are organised states — self-reinforcing, coherent, and remarkably resistant to simple intervention.

Why This Reframing Matters

The conventional medical model treats symptoms as signs pointing to an underlying pathology. Remove the pathology, the symptoms disappear. This logic works well for many physical illnesses. But in mental health, it has consistently underdelivered. Decades of research into the causes of depression, anxiety, psychosis, and personality disorder have not translated into dramatically better outcomes. Symptom reduction rates remain stubbornly modest. Relapse is the norm, not the exception.

Part of the problem may be ontological: we have been looking for the wrong kind of thing. If a symptom is an attractor — a stable dynamic pattern — then eliminating it is not simply a matter of removing a cause. You cannot drain a valley by attacking the ball. You need to reshape the landscape.

This is a profound shift in therapeutic logic. Instead of asking “what is causing this symptom?”, we begin asking: “What conditions are maintaining this attractor? What would it take to destabilise it? And what new attractors — new stable patterns — could emerge in its place?”

Implications for Psychotherapy Practice

If symptoms are attractors, then psychotherapy is — in dynamic terms — a process of attractor landscape modification. The therapist and client together are not trying to eliminate a defect; they are working to shift the topology of the person’s psychological terrain.

Several implications follow from this:

Stability before change. Attractor states are, by definition, stable. Attempting to force change from outside — through behavioural prescriptions or cognitive challenges alone — often meets strong resistance, not because the person is unwilling, but because the system is organised to return to its stable state. Effective intervention may require first understanding the attractor before attempting to move it.

Phase transitions matter. In dynamical systems, change rarely happens gradually. Systems often remain in one attractor for long periods before shifting — sometimes rapidly — to another state. Therapy may work by creating the conditions for such transitions: increasing variability, building new repertoires, expanding the range of possible states before a new stable pattern emerges.

Small interventions, large effects — sometimes. Near a tipping point, even small perturbations can push a system into a new attractor basin. This may explain why certain therapeutic moments feel disproportionately impactful — they arrive at just the right point in the person’s dynamic landscape.

The therapeutic relationship as context. From this view, the therapeutic relationship is not merely a vehicle for technique delivery. It is part of the dynamic environment that shapes the attractor landscape. Safety, co-regulation, and relational repair may themselves contribute to destabilising pathological attractors and enabling new ones to form.

Osaka and the Broader Conversation

The SPR Osaka structured discussion was an opportunity to bring these ideas into dialogue with a broader community of psychotherapy researchers — clinicians, process researchers, outcome specialists, and theorists. The conversation was generative precisely because it was multidisciplinary: different framings of the same clinical realities, held in productive tension.

What emerged from these discussions was not a unified theory — that would be premature — but a shared sense that the field needs richer metaphors. The language of symptoms-as-pathology, of diagnosis-as-essence, of treatment-as-elimination, may have reached the limits of its usefulness. The language of dynamics, of patterns, of attractors and bifurcations, opens new conceptual territory.

This does not mean abandoning what we know. Evidence-based treatments remain our best tools. But it does mean holding them differently — as perturbations to a dynamic system, rather than corrections of a broken one.

Looking Forward

The reimagining of psychotherapy through the lens of complexity science is still in its early stages. There are enormous theoretical and empirical challenges ahead: How do we measure attractors clinically? How do we identify tipping points? How do we design interventions that work with dynamic landscapes rather than against them?

These are hard questions. But they are the right questions. And they are beginning to receive the serious scientific attention they deserve.

The conversation continues — in Osaka, and beyond.

IDEAS: A New Treatment Programme for Young People with Complex Emotional Needs

How do we treat young people whose emotional lives are characterised by intense instability, overwhelming feelings, and difficulty maintaining relationships — and who do not fit neatly into any single diagnostic category? This is one of the most pressing challenges in adolescent mental health. The IDEAS Programme — Interventive Dynamic Emotion Assessment and Skills — is a new approach that takes this challenge seriously, starting from a fundamentally different understanding of what is happening.

What Is IDEAS?

IDEAS is an 8-week, group-based treatment programme designed for young people aged 16–25 presenting with Complex Emotional Needs (CEN) — a transdiagnostic category that includes presentations characterised by severe emotional dysregulation, unstable attachment, identity disturbance, and self-harm. It is delivered within NHS services and is designed to be accessible within existing healthcare delivery structures.

What makes IDEAS different is its theoretical foundation. Rather than targeting a specific diagnostic category with a fixed protocol, IDEAS is built on the framework of dynamical disorders — the idea that CEN presentations are best understood as disorders of coordination flexibility: disruptions in the capacity of the mind-body system to synchronise, adapt, and transition between states appropriately.

Phenotype-Matched Treatment

A central feature of IDEAS is that it does not treat all young people the same way. Before beginning the programme, each participant is assessed for their coordination phenotype — their characteristic pattern of synchronisation and coordination failure. Four phenotypes are distinguished:

  • Hypervigilant — locked in rigid threat-monitoring and over-synchronisation
  • Collapsed — withdrawn, affectively numbed, under-activated
  • Chaotic — rapidly fluctuating, unstable, with low recovery capacity
  • Balanced — the target state: flexible, context-sensitive, with adaptive regulation

Each phenotype has a distinct physiological signature and responds differently to different kinds of intervention. The IDEAS programme uses this profile to weight and sequence the four intervention modules adaptively across the eight weeks.

The Four Modules

The programme is structured around four modules, each targeting a specific synchronisation domain:

  • Connect, Communicate, Coordinate (CCC) — interpersonal coordination: restoring flexible coupling and decoupling in social interaction
  • Distress Tolerance — autonomic regulation: expanding the window of tolerance and building capacity to remain in moderate arousal
  • Emotion Regulation — affective coordination: building the attractor landscape flexibility needed for adaptive emotional response
  • Behavioural Flexibility — action-environment coupling: developing context-sensitive action selection and generalization capacity

Crucially, modules are not delivered in fixed sequence. Their weighting is adapted based on initial phenotype assessment and updated session-by-session based on clinical monitoring — making IDEAS a genuinely personalised treatment rather than a standardised protocol.

Pilot Results

A pilot study of 48 young people with CEN showed encouraging results. Large effect sizes were found across primary outcome measures, with gains sustained at 3-month follow-up. Importantly, the coordination-based measures — including heart rate variability complexity and measures of interpersonal coordination flexibility — corresponded to the clinical improvements, suggesting that the programme is achieving its intended mechanism of change. Phenotype-stratified analyses confirmed differential response trajectories: each phenotype showed its own pattern of change, consistent with the theoretical model.

A New Class of Treatment

IDEAS represents something genuinely new in mental health treatment: a Dynamical Systems Treatment (DST) — an intervention designed explicitly to restore coordination flexibility, rather than to reduce symptoms defined by a diagnostic category. The target is the attractor landscape of the individual, not a checklist of behaviours or feelings.

This matters for young people with complex presentations who have often experienced repeated treatment failures. It matters for the clinicians working with them, who need tools that match the dynamic, multi-level nature of the conditions they are treating. And it matters for the field, which needs a new generation of outcome measures and mechanisms of change that reflect the complexity of human psychological functioning. IDEAS is an early step in that direction.

At SPR Osaka 2026: Reimagining Psychotherapy Through Dynamical Systems

In June 2026, I had the privilege of presenting at the Society for Psychotherapy Research (SPR) Annual Conference in Osaka, Japan — one of the most stimulating gatherings in the field of psychotherapy research. This year’s conference brought together researchers, clinicians, and theorists from around the world to explore the frontiers of psychological treatment and its mechanisms of change.

I contributed two sessions, each approaching a shared question from different angles: What if the way we think about mental health and psychotherapy has been fundamentally incomplete — and what would change if we adopted a dynamical systems perspective?

Panel Presentation: Restoring Flexible Functional Synchrony

The first session was a panel presentation titled “Restoring Flexible Functional Synchrony: A Dynamical Systems Approach to Complex Emotional Needs.” The central argument: mental health conditions are not best understood as discrete symptom clusters, but as disorders of coordination flexibility — the capacity of the human system to synchronise, desynchronise, and transition between states in a context-appropriate way.

The focus was on Complex Emotional Needs (CEN) — a transdiagnostic presentation characterised by severe emotional dysregulation, unstable attachment, and interpersonal difficulties — as a compelling test case for this framework. These conditions show coordination dysregulation across multiple timescales: from autonomic nervous system rigidity at the millisecond scale, through affective instability across minutes, to interpersonal coordination failures across sessions and relationships.

We presented four dynamical phenotypes — distinct attractor landscape profiles that characterise different patterns of coordination failure in CEN:

  • Hypervigilant — rigid over-synchronisation, deep narrow attractor basin, constant threat-monitoring
  • Collapsed — reduced synchronisation, flat attractor landscape, affective numbing and withdrawal
  • Chaotic — unstable coordination, multiple fragmented attractors, rapid dysregulation with low recovery
  • Balanced — flexible synchrony, moderate-depth wide basin, context-sensitive regulation

These phenotypes correspond to measurable physiological signatures and guide matched clinical intervention through the IDEAS Programme (Interventive Dynamic Emotion Assessment and Skills): an 8-week, group-based intervention for young people aged 16–25 with Complex Emotional Needs. A pilot study of 48 participants showed large effect sizes across clinical outcomes, maintained at 3-month follow-up, with differential response trajectories by phenotype.

Structured Discussion: Reimagining Psychotherapy as Coordination Restoration

The second session was a structured discussion: “Reimagining Psychotherapy as a Dynamical Treatment of Synchronization and Coordination.” This broader conceptual exploration opened a conversation about what it would mean to fundamentally reconceptualise psychotherapy — not as a technique that reduces symptoms, but as a process that restores flexible functional synchronisation across neural, physiological, and interpersonal domains.

The discussion centred on three questions: What distinguishes functional from dysfunctional synchrony across clinical presentations? How can coordination dynamics be measured as mechanisms of therapeutic change? And what are the implications for training and treatment development when psychotherapy is understood as coordination restoration rather than symptom management?

A key theme was the reconceptualisation of rupture and repair in the therapeutic relationship — not merely as relational events, but as dynamical phase transitions with measurable physiological signatures. Successful repair does not simply restore the previous state, but expands the attractor landscape, building resilience and flexibility.

Looking Ahead

Both sessions generated rich discussion and a genuine sense that the field is ready for this shift. The convergence of complexity science, affective neuroscience, and psychotherapy research is opening new possibilities for how we understand, measure, and treat psychological distress. I look forward to continuing this work and to the collaborations that emerged from Osaka.