Cooperative AI via Decentralized Commitment Devices
Keywords: credible commitment devices, multi-agent security, Multi-Agent Reinforcement Learning (MARL), Maximal Extractable Value (MEV), cooperative AI
TL;DR: We explore the limitations of existing credible commitment devices for multi-agent coordination and highlight the need for research in decentralized cryptographic commitment mechanisms to enhance the security of cooperative AI in the real-world
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. Fortunately, decentralized commitment devices that utilize cryptography have been deployed in the wild, and numerous studies have shown their ability to coordinate algorithmic agents, especially when agents face rational or sometimes adversarial opponents with significant economic incentives, currently in the order of several million to billions of dollars. In this paper, we illustrate potential security issues in cooperative AI via examples in the decentralization literature and, in particular, Maximal Extractable Value (MEV). 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.
Submission Number: 14
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