Complexity Insights
A Research Practice Markets aren't in static equilibrium. Our models should not pretend they are.
01Thesis

The standard model assumes a world that doesn't exist. Real economies are networks of adapting agents.

Equilibrium economics answers the wrong question. It asks what price clears the market when everyone is rational, identical, and finished adjusting. That is a useful cartoon for a seminar, and a dangerous one for a trading floor or a supply chain.

The alternative is not to abandon rigor — it is to model behavior that is genuinely adaptive. Traders who learn. Firms that imitate. Shocks that propagate through network topology rather than dissipating smoothly across representative agents. The methods are now mature: agent-based models, calibrated networks, non-linear stochastic dynamics. The bottleneck is practitioners who can translate between the research and a real business problem.

That is the practice.

02Practice

Four problems worth modeling properly.

i.

Market simulation & strategy

Stress-test pricing, launch strategy, or trading rules against synthetic populations of heterogeneous agents before committing capital. Finds the failure modes averages can't see.

Agent-basedMonte CarloCalibration
ii.

Supply-chain & exposure networks

Map the graph — who actually depends on whom, two and three hops deep. Identify nodes whose failure triggers cascades disproportionate to their size or spend.

Network scienceCentralityPercolation
iii.

Systemic & tail-risk indicators

Conventional VaR underestimates regime change. Complexity-based indicators — correlation structure, critical slowing-down — give early warning of phase transitions conventional models miss by construction.

Non-linear dynamicsCorrelation structureEarly warning
iv.

M&A & integration analysis

Apply network theory to deal sourcing, synergy modeling, and post-merger integration risk. Synergies live in the graph of who-talks-to-whom; so do the integration failures.

Graph analysisSynergy modelingIntegration risk
04Writing & research

Working papers, projects, and case studies.

№ 001
Data Pipeline
Data Steerage Project Creating a multi-layer scraping, cleaning and standardization model to power ABMs.
DPL
Q2 2026
№ 002
Agent-based model
Global Agent Based Model Simulating how real world recessions form to predict the next.
GABM
Q3 2026
№ 003
Research note
How to survive a recession Strategies and methods of how the average person can win when the market is losing.
HSR
Q2 2026
№ 004
Applied research
Banking deserts Spatial and network analysis of financial-access gaps.
BNK
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Get in touch

The best problems come in through the front door.

If you have an interesting question — inside a firm, a research group, or a hiring pipeline — the fastest path is a direct note.

© 2026 Complexity Insights · Nicholas Thomas · Washington, D.C. Set in Source Serif 4, Inter & JetBrains Mono