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Cooperative AI via Decentralized Commitment Devices

Xinyuan Sun, Davide Crapis, Matt Stephenson, Barnabé Monnot, Thomas Thiery, Jonathan Passerat-Palmbach

TL;DR

The paper argues that secure coordination among AI agents relies on credible commitment devices, but real-world constraints—privacy, integrity, and strategic exploitation—undermine traditional approaches. By drawing on congestion games and MEV literature, it highlights how mediators can manipulate outcomes and extract value, underscoring security risks in decentralized commitments. It discusses the trade-offs between cryptographic integrity (e.g., SNARKs) and optimistic enforcement, and exposure to collusion, bribery, and DoS in open environments. The authors call for empirical demonstrations and a shift toward deploying cooperative AI atop decentralized commitment frameworks to achieve robust, real-world-ready coordination.

Abstract

Credible commitment devices have been a popular approach for robust multi-agent coordination. However, existing commitment mechanisms face limitations like privacy, integrity, and susceptibility to mediator or user strategic behavior. It is unclear if the cooperative AI techniques we study are robust to real-world incentives and attack vectors. However, decentralized commitment devices that utilize cryptography have been deployed in the wild, and numerous studies have shown their ability to coordinate algorithmic agents facing adversarial opponents with significant economic incentives, currently in the order of several million to billions of dollars. In this paper, we use examples in the decentralization and, in particular, Maximal Extractable Value (MEV) (arXiv:1904.05234) literature to illustrate the potential security issues in cooperative AI. We call for expanded research into decentralized commitments to advance cooperative AI capabilities for secure coordination in open environments and empirical testing frameworks to evaluate multi-agent coordination ability given real-world commitment constraints.

Cooperative AI via Decentralized Commitment Devices

TL;DR

The paper argues that secure coordination among AI agents relies on credible commitment devices, but real-world constraints—privacy, integrity, and strategic exploitation—undermine traditional approaches. By drawing on congestion games and MEV literature, it highlights how mediators can manipulate outcomes and extract value, underscoring security risks in decentralized commitments. It discusses the trade-offs between cryptographic integrity (e.g., SNARKs) and optimistic enforcement, and exposure to collusion, bribery, and DoS in open environments. The authors call for empirical demonstrations and a shift toward deploying cooperative AI atop decentralized commitment frameworks to achieve robust, real-world-ready coordination.

Abstract

Credible commitment devices have been a popular approach for robust multi-agent coordination. However, existing commitment mechanisms face limitations like privacy, integrity, and susceptibility to mediator or user strategic behavior. It is unclear if the cooperative AI techniques we study are robust to real-world incentives and attack vectors. However, decentralized commitment devices that utilize cryptography have been deployed in the wild, and numerous studies have shown their ability to coordinate algorithmic agents facing adversarial opponents with significant economic incentives, currently in the order of several million to billions of dollars. In this paper, we use examples in the decentralization and, in particular, Maximal Extractable Value (MEV) (arXiv:1904.05234) literature to illustrate the potential security issues in cooperative AI. We call for expanded research into decentralized commitments to advance cooperative AI capabilities for secure coordination in open environments and empirical testing frameworks to evaluate multi-agent coordination ability given real-world commitment constraints.
Paper Structure (9 sections, 1 figure, 1 table)

This paper contains 9 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Congestion Games
  3. Limits of Real-world Commitments
  4. Security of Commitments in Cooperative AI
  5. Call for Action
  6. DeFi network and routing
  7. Where does the Commitment Happen?
  8. CCE in a Stop Light Game
  9. Methods of Commitment

Figures (1)

  • Figure 1: Example DeFi network with 4 assets and routes between them. Full lines are smart contract exchanges and dotted line is a (potential) direct swap.