# Cooperative AI via Decentralized Commitment Devices

arXiv: https://arxiv.org/abs/2311.07815 · HTML: https://mlsanity.com/paper/2311.07815/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 89 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 30 of 46)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/1709.04326/) (2017)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [The Hanabi Challenge: A New Frontier for AI Research](https://mlsanity.com/paper/1902.00506/) (2019)
- [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020)
- [Regulating ChatGPT and other Large Generative AI Models](https://mlsanity.com/paper/2302.02337/) (2023)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- [Off-Belief Learning](https://mlsanity.com/paper/2103.04000/) (2021)
- [Model-Free Opponent Shaping](https://mlsanity.com/paper/2205.01447/) (2022)
- [COLA: Consistent Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/2203.04098/) (2022)
- [A New Formalism, Method and Open Issues for Zero-Shot Coordination](https://mlsanity.com/paper/2106.06613/) (2021)
- [Scaling up Trustless DNN Inference with Zero-Knowledge Proofs](https://mlsanity.com/paper/2210.08674/) (2022)
- [The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2008.02616/) (2020)
- [SoK: Decentralized Finance (DeFi)](https://mlsanity.com/paper/2101.08778/) (2021)
- [Auctions Between Regret-Minimizing Agents](https://mlsanity.com/paper/2110.11855/) (2021)
- [Artificial Intelligence and Auction Design](https://mlsanity.com/paper/2202.05947/) (2022)
- [Influencing Long-Term Behavior in Multiagent Reinforcement Learning](https://mlsanity.com/paper/2203.03535/) (2022)
- [How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents](https://mlsanity.com/paper/2112.07640/) (2021)
- [A Survey on Blockchain Interoperability: Past, Present, and Future Trends](https://mlsanity.com/paper/2005.14282/) (2020)
- [Game Theory with Simulation of Other Players](https://mlsanity.com/paper/2305.11261/) (2023)
- [An analysis of Uniswap markets](https://mlsanity.com/paper/1911.03380/) (2019)
- [Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory](https://mlsanity.com/paper/2208.07006/) (2022)
- [Meta-Learning in Games](https://mlsanity.com/paper/2209.14110/) (2022)
- [Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games](https://mlsanity.com/paper/2003.00799/) (2020)
- [Attacking the DeFi Ecosystem with Flash Loans for Fun and Profit](https://mlsanity.com/paper/2003.03810/) (2020)
- [Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria](https://mlsanity.com/paper/2210.16175/) (2022)
- [Mediated Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.08419/) (2023)

## Built on by (most cited first, 7 of 7)

- [Secret Collusion among AI Agents: Multi-Agent Deception via Steganography](https://mlsanity.com/paper/2402.07510/) (2024)
- [Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents](https://mlsanity.com/paper/2505.02077/) (2025)
- [Infrastructure for AI Agents](https://mlsanity.com/paper/2501.10114/) (2025)
- [IDs for AI Systems](https://mlsanity.com/paper/2406.12137/) (2024)
- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [Collective Alignment in LLM Multi-Agent Systems: Disentangling Bias from Cooperation via Statistical Physics](https://mlsanity.com/paper/2605.10528/) (2026)
- [Policy-Conditioned Policies for Multi-Agent Task Solving](https://mlsanity.com/paper/2512.21024/) (2025)

## Abstract (arXiv)

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.