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An Analysis of Intent-Based Markets

Tarun Chitra, Kshitij Kulkarni, Mallesh Pai, Theo Diamandis

TL;DR

This work provides two formal models of solvers' strategic behavior: one probabilistic and another deterministic, and introduces an alternative, optimization-based deterministic model which corroborates these results.

Abstract

Mechanisms for decentralized finance on blockchains suffer from various problems, including suboptimal price execution for users, latency, and a worse user experience compared to their centralized counterparts. Recently, off-chain marketplaces, colloquially called `intent markets,' have been proposed as a solution to these problems. In these markets, agents called \emph{solvers} compete to satisfy user orders, which may include complicated user-specified conditions. We provide two formal models of solvers' strategic behavior: one probabilistic and another deterministic. In our first model, solvers initially pay upfront costs to enter a Dutch auction to fill the user's order and then exert congestive, costly effort to search for prices for the user. Our results show that the costs incurred by solvers result in restricted entry in the market. Further, in the presence of costly effort and congestion, our results counter-intuitively show that a planner who aims to maximize user welfare may actually prefer to restrict entry, resulting in limited oligopoly. We then introduce an alternative, optimization-based deterministic model which corroborates these results. We conclude with extensions of our model to other auctions within blockchains and non-cryptocurrency applications, such as the US SEC's Proposal 615.

An Analysis of Intent-Based Markets

TL;DR

This work provides two formal models of solvers' strategic behavior: one probabilistic and another deterministic, and introduces an alternative, optimization-based deterministic model which corroborates these results.

Abstract

Mechanisms for decentralized finance on blockchains suffer from various problems, including suboptimal price execution for users, latency, and a worse user experience compared to their centralized counterparts. Recently, off-chain marketplaces, colloquially called `intent markets,' have been proposed as a solution to these problems. In these markets, agents called \emph{solvers} compete to satisfy user orders, which may include complicated user-specified conditions. We provide two formal models of solvers' strategic behavior: one probabilistic and another deterministic. In our first model, solvers initially pay upfront costs to enter a Dutch auction to fill the user's order and then exert congestive, costly effort to search for prices for the user. Our results show that the costs incurred by solvers result in restricted entry in the market. Further, in the presence of costly effort and congestion, our results counter-intuitively show that a planner who aims to maximize user welfare may actually prefer to restrict entry, resulting in limited oligopoly. We then introduce an alternative, optimization-based deterministic model which corroborates these results. We conclude with extensions of our model to other auctions within blockchains and non-cryptocurrency applications, such as the US SEC's Proposal 615.
Paper Structure (45 sections, 49 equations, 2 figures)

This paper contains 45 sections, 49 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Intent-based markets.
  3. Example intents.
  4. Intents in practice: UniswapX.
  5. Intents in practice: other protocols.
  6. Intents without blockchains.
  7. Competition.
  8. Empirical signatures of reduced competition in UniswapX.
  9. This paper.
  10. An auction-theoretic model
  11. Entry costs, costly effort, and congestion.
  12. Dutch auctions for intents
  13. Interim profits.
  14. Exponential distribution.
  15. Uniform distribution.
  16. ...and 30 more sections

Figures (2)

  • Figure 1: Evolution of competition within UniswapX over time UniswapX-dune. Left: the market share of different participants, in terms of the volume of orders filled. Right: the same data evolving over time. The data set has over 2,000 unique addresses that have participated, despite fewer than 20 addresses participating in January and Feburary 2024.
  • Figure 2: Ratio of the expected revenue to the expected highest price, $\mathop{\bf E{}}[p_{n-1:n}]/\mathop{\bf E{}}[p_{n:n}]$, for the Pareto distribution, versus the number of solvers, $n$. The ratio of the medians of $p_{n-1:n}$ and $p_{n:n}$ is plotted for reference.