An Axiomatic Characterization of CFMMs and Equivalence to Prediction Markets

TL;DR

This paper establishes a formal link between constant-function market makers (CFMMs) like Uniswap and cost-function prediction markets, showing they are equivalent under certain axioms and can be transformed into each other, enhancing understanding and design of market mechanisms.

Contribution

It introduces axioms characterizing good market makers and proves their equivalence to prediction markets, enabling cross-application of tools and strategies between the two frameworks.

Findings

CFMMs with concave potential functions satisfy key axioms.

Every CFMM on n assets is equivalent to a prediction market with n outcomes.

Liquidity strategies can be transferred between CFMMs and prediction markets.

Abstract

Constant-function market makers (CFMMs), such as Uniswap, are automated exchanges offering trades among a set of assets. We study their technical relationship to another class of automated market makers, cost-function prediction markets. We first introduce axioms for market makers and show that CFMMs with concave potential functions characterize "good" market makers according to these axioms. We then show that every such CFMM on $n$ assets is equivalent to a cost-function prediction market for events with $n$ outcomes. Our construction directly converts a CFMM into a prediction market and vice versa. Conceptually, our results show that desirable market-making axioms are equivalent to desirable information-elicitation axioms, i.e., markets are good at facilitating trade if and only if they are good at revealing beliefs. For example, we show that every CFMM implicitly defines a proper scoring rule for eliciting beliefs; the scoring rule for Uniswap is unusual, but known. From a technical standpoint, our results show how tools for prediction markets and CFMMs can interoperate. We illustrate this interoperability by showing how liquidity strategies from both literatures transfer to the other, yielding new market designs.