Complexity Insights
Network analysis
Mapping contagion & connectivity.
Correlation graphs, counterparty networks, community detection, centrality-based systemic-risk scoring. Identifying the institutions that are "too connected to fail" — not just too big.
A research house for complex systems.
Complexity Insights is an independent research house applying network science, agent-based modeling, and quantitative data work to economic and financial questions that don't sit still long enough for a textbook answer.
Traditional models assume equilibrium. Real markets are adaptive, networked, and reflexive — small changes cascade, stable periods end abruptly, and the same number means different things across regimes. This research takes that seriously, with code you can run.
Research areas
What the research actually covers.
Agent-based modeling
Simulating emergence in synthetic markets.
Testing strategies, policy shifts, and microstructure changes in risk-free synthetic environments. When closed-form analysis breaks, a well-specified ABM still produces usable answers.
Data science
Where linearity breaks down.
Non-linear dynamics, regime-dependent behavior, feedback loops. Reproducible pipelines in Python; versioned data; everything that makes a finding hold up when someone re-runs it later.
Systemic risk
Quantifying the tails that VaR misses.
Network-aware risk metrics, stress propagation, regime-change early warning. Standard VaR assumes independence. Real crises don't.
M&A & integration
Network theory for deal outcomes.
How network structure shapes synergy realization and post-merger integration risk. What's the topology of the combined entity, and where are the fragile bridges?
Corporate finance
Complexity & valuation.
How non-stationarity changes valuation and capital-allocation conclusions. What happens to traditional DCF under regime-dependent discounting and scenario trees that actually resolve?
Nicholas Thomas
Researcher & FounderI am an economics graduate and researcher focused on the intersection of complexity science and financial markets. My work extends traditional economics with computational methods applied to real-world data.
During my studies, I became frustrated with the gap between textbook models and market reality. The 2008 financial crisis wasn't just a "black swan" — it was a failure of models that assumed independence in a deeply interconnected system.
I founded Complexity Insights to bridge that gap. It is my independent research house: a place to build and publish agent-based and network models of financial and economic systems, in the open.
I am currently available for full-time roles in economic research, data analysis, and financial modeling — anywhere careful quantitative work changes a real decision.
Next
Read the work, or write to me.
The projects page holds work in progress. Questions, critiques, and collaboration are welcome.
All projects →"The map is never finished. Only ever less wrong."