Decoding Ponzi Schemes with Math and AI

Author(s): Shenggang Li

Originally published on Towards AI.

Experiments in Action: Unveiling Hidden Trends in Stocks and Markets Using Mamba Neural Networks

This member-only story is on us. Upgrade to access all of Medium.

Imagine this: Someone promises you guaranteed returns way higher than the market average. Early investors make money, the buzz grows, and more people join in — until one day, it all falls apart. That’s how a classic Ponzi scheme works, built on trust, deception, and a constant need for new money to keep it going.

Now think about a system where the total money stays the same, and every win comes at someone else’s loss — like in lotteries or speculative stock trades. There’s no one pulling the strings, but the setup still feels similar to a Ponzi scheme: one person’s gain means another’s loss. Add in a central operator, like a casino or a big market player, who takes a cut of the money, and it becomes a negative-sum game. Here, everyone loses a bit more because the operator skims off the top.

This paper explores these three cases — traditional Ponzi schemes, zero-sum games, and negative-sum games with a central operator. While the latter two differ from Ponzi schemes, they reveal striking structural and mathematical parallels. By examining their shared dynamics, we uncover insights… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI