MIT AI Risk Repository · Risk Sub-Category · 63.01.02
Credit Assignment
Category: Miscoordination
Description
"Credit Assignment. While agents can often learn to jointly solve tasks and thus avoid coordination failures, learning is made more challenging in the multi-agent setting due to the problem of credit assignment (Du et al., 2023; Li et al., 2025, see also Section 3.1 on information asymmetries and Section 3.4, which discusses distributional shift). That is, in the presence of other learning agents, it can be unclear which agents’ actions caused a positive or negative outcome to obtain, especially if the environment is complex. Moreover, in multi-principal settings, agents may not have been trai
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
- 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: