MATS: The Researcher — Predator Equations and Macro Cycles

I spent part of this weekend revisiting the predator equation. I came across a video explaining the mathematical game of survival, specifically the Lotka-Volterra equations that model how two species interact in a cycle. 

It is a beautiful, oscillating balance: as the prey population grows, the predator population follows with a lag, eventually over-consuming the resource and triggering a mutual decline until the cycle resets.

While the video focused on biology, I found myself thinking about global macroeconomics.

In my view, the macro environment follows a similar cyclic nature driven by two forces with a strong negative correlation. I began mapping them in my head. Capitalism, led by growth and inflation, acts as the stronger force. The labor market and employment rates act as the base. The central bank sits in the middle, attempting to act as a smoothing influence or a regulator of the oscillation.

When two instruments have a correlation near –1, the peak of the stronger one can often be inferred from the weaker one approaching its bottom. I wanted to see if my Researcher agent could bridge the gap between this biological math and financial reality.

I asked the agent a specific, tricky question:

"I recently came across the predator–prey equation, which models two forces that move cyclically with strong negative correlation. It made me think about global macro: when two financial instruments have a correlation near –1, the peak of the stronger one can often be inferred from the weaker one approaching its bottom and its velocity slowing. If we map this analogy to macro forces, capitalism as the stronger force, labour as the weaker force, and the central bank as a smoothing influence, how can we derive a mathematical model that captures their cyclical interaction and turning points?"

The response was scarily reasonable. Instead of a generic summary of macroeconomics, the agent pointed me directly toward the Goodwin Model I never know before. Developed in 1967, Richard Goodwin’s "Growth Cycle" is a formal application of the predator-prey equations to the distribution of national income. In this model, the "predator" is the employment rate and the "prey" is the share of labor in national income.

The agent walked through how high employment leads to higher wages, which eventually squeezes profits, reduces investment, and leads to a downturn in employment, restarting the loop. It captured the exact velocity and turning point logic I was looking for.

I asked it to simulate 2026 sp500 peak with python code, and here is its prediction (simplied version - no mont carlo, no parameter sweep, initial settings not accurate):

This interaction validated the current direction of MATS. Its purpose is conceptual synthesis. In a trading context, we are often looking for the "why" behind the "what." By correctly identifying the Goodwin Model, the agent demonstrated it could move from a vague analogy to a grounded, historical framework that provides a mathematical basis for trade timing.

Systems thinking is about finding the universal patterns that govern different domains. The Researcher agent proved it can see the pattern.