MIT AI Risk Repository · Risk Sub-Category · 63.06.01

Undesirable Dispositions from Competition

Category: Selection Pressures

Open the MIT Risk Navigator All entries from Hammond2025

Description

"Undesirable Dispositions from Competition. It is plausible that evolution selected for certain conflict-prone dispostions in humans, such as vengefulness, aggression, risk-seeking, selfishness, dishon- esty, deception, and spitefulness towards out-groups (Grafen, 1990; Han, 2022; Konrad & Morath, 2012; McNally & Jackson, 2013; Nowak, 2006; Rusch, 2014). Such traits could also be selected for in ML systems that are trained in more competitive multi-agent settings. For example, this might happen if systems are selected based on their performance relative to other agents (and so one agent’s loss

From Multi-Agent Risks from Advanced AI (Hammond2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Domain: 7. AI system safety, failures, & limitations
Subdomain: 7.6 Multi-agent risks
Causal entity: Other
Intent: Unintentional
Timing: Other

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
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Collusion between LLM-Agents
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Multi-Agent Safety Is Not Assured by Single-Agent Safety
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Foundationality May Cause Correlated Failures
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Financial instability due to model homogeneity
Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)
Impact on Financial Stability
Frontier AI Risk Management Framework (v1.0) (Tse2025)
Multi-agent collaboration capability
Frontier AI Risk Management Framework (v1.0) (Tse2025)
Multi-agent collusion propensity:
Frontier AI Risk Management Framework (v1.0) (Tse2025)

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