Equilibria Network | Equilibria Network

Agent-Based Simulations

We discover collective behavior laws that no human observation could find.

We can run thousands of controlled experiments on societal simulations - impossible in the real world but essential if you want to make better decisions in a rapidly transforming world.

Mathematical Foundations

We're discovering the mathematical laws that govern all collective intelligence.

We use category theory to import proven theorems from network science directly into social systems, creating a "periodic table" of coordination mechanisms instead of building new math from scratch.

Example

We developed frameworks showing that markets, networks, and democracies aren't cultural accidents - they're optimal solutions to different information processing problems. Markets for compressible information (prices), networks for local structure, democracies for aggregation. This explains why the same structures appear from ant colonies to AI systems, and lets us predict which coordination mechanisms will emerge in new domains like multi-agent AI.

Who This Matters To

Policymakers

We can help you see and quantify the potential impact of your governance proposals on wide populations before implementation

Example

Semiconductor supply chain policies often create hidden vulnerabilities when critical nodes concentrate in adversarial regions. We're building models to reveal which interventions actually increase resilience versus which just look good on paper.

AI Safety Researchers

We can help you evaluate emergent risks and the safety of multi-agent AI systems

Example

We're building mathematical frameworks to predict where multi-agent AI systems break down and identify the control levers needed to prevent those breakdowns. Our approach combines phase transition analysis - mapping the critical points where collective AI behavior shifts from beneficial to harmful - with top-down control theory that reveals which interventions can steer these systems toward pro-social outcomes before problems emerge.

AI Labs

Predict emergent behaviors before expensive deployment

Example

AI systems from many organizations will need to coordinate someday. Should they trade information like a market, vote like democracies, or try some other type of coordination mechanism? We are designing simulations to tell you which approach will work best before you spend millions building systems that end up with coordination failures.