Complexity Insights
VOL. I · APR 2026
Complexity Insights is a one-person practice 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 don't stand still — they are adaptive, networked, and reflexive. Small changes cascade; stable periods end abruptly; the same number can mean wildly different things across regimes. The practice is built around taking that seriously, with code you can run.
Four threads. They overlap more than they diverge; most engagements use two or three at once.
Correlation graphs, counterparty networks, community detection, centrality-based systemic-risk scoring. Identifying the institutions that are "too connected to fail" — not just too big.
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.
Non-linear dynamics, regime-dependent behavior, feedback loops. Reproducible pipelines in Python; versioned data; everything that makes a finding hold up under re-running it next quarter.
Network-aware risk metrics, stress propagation, regime-change early warning. Standard VaR assumes independence. Real crises don't.
Deal sourcing, synergy modeling, post-merger integration risk. What's the topology of the combined entity, and where are the fragile bridges?
Capital allocation and FP&A that takes non-stationarity seriously. Traditional DCF meets regime-dependent discounting and scenario trees that actually resolve.
I am an economics graduate and researcher focused on the intersection of complexity science and financial markets. My work challenges the static assumptions of traditional economics by applying computational methods 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. The firm serves as both a consultancy and a research laboratory, applying Agent-Based Modeling (ABM) and network theory to solve strategic problems in finance and risk management.
I am currently available for full-time roles in economic research, data analysis, and financial modeling, where I can apply these rigorous quantitative methods to drive business value.
One page. What you actually want to know, what a useful answer looks like, what data exists. Most engagements end here if the question is wrong.
Ingest, clean, version. Reproducibility first; plots second. The pipeline is the deliverable just as much as the chart on top.
Specify, fit, break. Every model gets run in at least one regime it wasn't fit on. A fragile result should fall over in the lab, not in production.
Prose, code, notebook. Findings stated in plain language with the caveats in the open. Nothing goes out that I couldn't defend under cross-examination.
Open to full-time roles, contract engagements, research collaboration, and the occasional "can you look at this?" from someone with a thorny network question. I read every message.