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.