Decoding Ponzi Schemes with Math and AI

Author(s): Shenggang Li

Originally published on Towards AI.

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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.

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