Cooperative AI via Decentralized Commitment Devices
It addresses the problem of secure coordination for AI agents in open environments, calling for expanded research and empirical testing, but is incremental as it builds on existing commitment device and MEV studies.
The paper identifies security issues in cooperative AI by analyzing decentralized commitment devices, using examples from Maximal Extractable Value (MEV) literature to highlight vulnerabilities in multi-agent coordination.
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.