Next-Gen Economic Strategy

Navigate Uncertainty with
Complexity Economics

Traditional models assume equilibrium. We assume reality. Leverage network science and agent-based simulations to turn market volatility into a strategic advantage.

Powered by Advanced Methodologies
Network Theory
Agent-Based Modeling
Non-Linear Dynamics
Systemic Risk Analysis

Strategic Solutions

We help organizations look beyond simple averages to understand the emergent behaviors that drive risk and opportunity.

Market Simulation & Strategy

Test new pricing models, product launches, or trading strategies in risk-free synthetic environments before deploying real capital.

View Simulations

Supply Chain Resilience

Map hidden dependencies in your supply network to identify "too connected to fail" nodes and prevent cascading failures.

See Network Analysis

Systemic Risk Detection

Traditional risk models fail during crises. Our complexity-based indicators provide early warning signals for phase transitions.

Explore Risk Frameworks

Finance and M&A Analysis

Applying network theory to deal sourcing, synergy modeling, and post-merger integration risks.

View Our Models

Recent Intelligence

Applied research and technical analysis.

Network Analysis

S&P 500 Correlation Breakdown

Mapping the erosion of diversification benefits during the 2022 volatility spikes.

Read Report
Agent-Based Model

Liquidity Spirals

Simulating how algorithmic trading strategies amplify flash crash dynamics.

Coming Soon
Research Note

The End of Equilibrium

Why static economic models are costing businesses millions in unhedged tail risk.

Read Article

Bridging Theory & Practice

Complexity economics has long been trapped in academia. Complexity Insights demonstrates the practical application of these powerful tools.

Founded by Nicholas Thomas, we combine rigorous economic theory with advanced computational modeling and data science. We tackle problems standard econometrics cannot solve—specifically those involving feedback loops, behavioral adaption, and network effects.

100%

Data-Driven

Python

Native Analysis

Meet the Team
def simulate_market_shock(agents, shock_val): # Initialize contagion parameters network = build_exposure_graph(agents) failed_nodes = [] for node in network.nodes: if node.exposure > shock_val: failed_nodes.append(node) trigger_cascade(node, network) return calculate_systemic_loss(failed_nodes)

Let's Start a Conversation

Interested in seeing how complexity economics can apply to your specific challenges? We are currently accepting new projects and collaborations.

Send a Message