AMMs and their predecessors, such as market scoring rules, were originally invented as a way to provide liquidity for prediction markets.  They now do

pm-AMM: A Uniform AMM for Prediction Markets

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2024-11-05 23:00:03

AMMs and their predecessors, such as market scoring rules, were originally invented as a way to provide liquidity for prediction markets. They now dominate most decentralized exchange volume in crypto.

One possible reason is that existing automated market makers are a poor fit for outcome tokens (tokens that resolve to $1 if an event occurs and $0 if it does not occur). The volatility of outcome tokens is dependent on the current probability of the event and the time until the prediction market expires, meaning that the pool provides inconsistent liquidity. Liquidity providers (LPs) are also essentially guaranteed to lose all of their value once the prediction market expires.

We present a new AMM optimized around these considerations. This required addressing a longstanding question in AMM research: what does it mean for an AMM to be optimized for a particular type of asset? In other words, given a model for some asset (such as an option, a bond, a stablecoin, or an outcome token), how should that affect what AMM we use for it? We present a possible answer to this question, based on the concept of loss-vs-rebalancing (LVR).

We develop a model for the price processes of some outcome tokens, which we call Gaussian score dynamics. This model is a potential fit for prediction markets on whether some underlying random walk (such as the score difference of a basketball game, the vote margin in an election, or the price of some asset) will be above some value at a particular future expiration time.

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