MIT AI Risk Repository · Risk Category · 63.01.00
Miscoordination
Description
"Miscoordination arises when agents, despite a mutual and clear objective, cannot align their behaviours to achieve this objective. Unlike the case of differing objectives, in common-interest settings there is a more easily well-defined notion of ‘optimal’ behaviour and we describe agents as miscoordinating to the extent that they fall short of this optimum. Note that for common-interest settings it is not sufficient for agents’ objectives to be the same in the sense of being symmetric (e.g., when two agents both want the same prize, but only one can win). Rather, agents must have identical pr
From Multi-Agent Risks from Advanced AI (Hammond2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Subdomain
- 7.6 Multi-agent risks
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
Subdomain definition: Risks from multi-agent interactions, due to incentives (which can lead to conflict or collusion) and/or the structure of multi-agent systems, which can create cascading failures, selection pressures, new security vulnerabilities, and a lack of shared information and trust.
How other frameworks describe this risk
- Groups of LLM-Agents May Show Emergent Functionality
- Multi-Agent Safety Is Not Assured by Single-Agent Safety
- Foundationality May Cause Correlated Failures
- Collusion between LLM-Agents
- Financial instability due to model homogeneity
- Multi-agent collusion propensity:
- Impact on Financial Stability
- Multi-agent collaboration capability