MIT AI Risk Repository · Risk Category · 63.03.00
Collusion
Description
"Collusion has long been a topic of intense study in economics, law, and politics, among other disciplines. While there is no universal definition of collusion, it generally refers to secretive cooperation between two or more parties at the expense of one or more other parties. Most classic examples of collusion – such as firms working together to set supra-competitive prices at the expense of consumers – also tend to be not only secretive but in violation of some law, rule, or ethical standard. Distinctions are also commonly made between explicit and tacit collusion (Rees, 1993), depending on
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
- Intentional
- 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
- Collusion between LLM-Agents
- Multi-Agent Safety Is Not Assured by Single-Agent Safety
- Foundationality May Cause Correlated Failures
- Financial instability due to model homogeneity
- Impact on Financial Stability
- Multi-agent collaboration capability
- Multi-agent collusion propensity: