How can complexity science reshape investment thinking?
In this section, I introduce Complexity Arbitrage — the idea that asset managers can achieve better outcomes by applying concepts from complexity science such as non-ergodicity, systemic risk, price emergence, and adaptive strategy.
Traditional financial models often rest on simplifying assumptions:
In practice, markets are far messier. They are driven by shifting narratives, diverse participants, and constant feedback between expectations and outcomes. The gap between model and market can foster misplaced confidence and poor decisions.
As W. Brian Arthur and colleagues observe, in their Santa Fe Institute Artificial Stock Market paper, “academic theorists and market traders tend to view financial markets in strikingly different ways… The market, in [the] standard theoretical view, is rational, mechanistic, and efficient… From the traders’ viewpoint, the standard academic theory is unrealistic and not borne out by their own perceptions.”
Complexity science helps bridge this gap — recognising patterns, adapting to change, and improving decision-making under uncertainty. The following sections explore the five themes of my Complexity Arbitrage framework.
Better investment outcomes come from recognising and adapting to the realities of financial systems — shaped by interaction, feedback, and continual change — rather than relying on static, simplifying assumptions. Complexity science highlights the patterns and processes that capture this dynamism more accurately.
At its core, Complexity Arbitrage brings together five interlinked themes:
These five themes form the foundation of my Complexity Arbitrage framework, which applies complexity science to improve investment thinking and practice.
Markets in process.
Prices are shaped by countless interactions, feedback loops, and shifting conditions. Seeing prices as emergent — not fixed reflections of “true value” — reveals the underlying processes driving market movements
The stories we think in. Stories, mental models, and beliefs shape expectations and decisions. Tracking them helps identify sentiment shifts and the potential for self-fulfilling or self-defeating outcomes.
Guides, not crystal balls.
Models are crude maps of a patterned reality rather than fixed predictors. Using them adaptively — updating them as conditions change — keeps strategies adaptive and assumptions fresh.
Beyond risk.
Not all unknowns can be measured. Building flexibility into strategies, preparing for varied scenarios, and avoiding overconfidence in forecasts greatly strengthens long-term resilience and strategic adaptability.
The journey matters.
In non-ergodic systems, the sequence of returns — not just the average — shapes results. Interim losses, volatility, and compounding can leave lasting marks on performance that conventional thinking often overlooks.
The documents below provide deeper insights into the ideas introduced on this page, grouped into two categories:
Complexity Arbitrage Project
Other Resources
Together, these resources offer both a conceptual grounding in complexity science and a practical framework for applying it to finance, helping asset managers navigate markets with greater insight and adaptability.