Aligned Agents Biased Swarm Measuring

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name: aligned-agents-biased-swarm-measuring
description: "While Multi-Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties-particularly the accumulation of bias-remain poorly understood. Because real-world MAS are to... Activation: multi-agent systems, agent collaboration, MAS, bias amplification, multi-agent bias."
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# Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems

## Overview
While Multi-Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties-particularly the accumulation of bias-remain poorly understood. Because real-world MAS are too complex to analyze entirely, evaluating their ethical robustness requires first isolating their foundational mechanics. In this work, we conduct a baseline empirical study investigating how basic MAS topologies and feedback loops influence prejudice. Contrary to the assumption that multi-agent collaboration naturally dilutes bias, we hypothesize that structured workflows act as echo chambers, amplifying minor stochastic biases into systemic polarization. To evaluate this, we introduce Discrim-Eval-Open, an open-ended benchmark that bypasses individual model neutrality through forced comparative judgments across demographic groups. Analyzing bias cascades across various structures reveals that architectural sophistication frequently exacerbates bias rather than mitigating it. We observe systemic amplification even when isolated agents operate neutrally, and identify a 'Trigger Vulnerability' where injecting purely objective context drastically accelerates polarization. By stripping away advanced swarm complexity to study foundational dynamics, we establish a crucial baseline: structural complexity does not guarantee ethical robustness. Our code is available at https://github.com/weizhihao1/MAS-Bias.

## Source Paper
- **Title**: Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems
- **Authors**: Keyu Li, Jin Gao, Dequan Wang
- **arXiv**: 2604.08963v1
- **Published**: 2026-04-10
- **Categories**: cs.MA, cs.AI

## Core Concepts

### Key Contributions
1. Novel methodology for addressing To evaluate this, we introduce Discrim-Eval-Open, an open-ended benchmark that b...
2. Theoretical analysis with empirical evaluation
3. Practical applicability in real-world systems

### Technical Framework
This research contributes to systems engineering by providing:
- Advanced control methodologies
- Distributed system optimization techniques
- Practical implementation strategies

## Applications

### Primary Use Cases
- Large-scale distributed systems
- Multi-agent coordination
- Safety-critical control systems
- Resource optimization

### Example Scenarios
1. **Industrial Deployment**: Manufacturing and robotics
2. **Cloud Infrastructure**: Kubernetes and container orchestration
3. **Autonomous Systems**: Multi-robot coordination
4. **Network Optimization**: Wireless and communication systems

## Implementation Considerations

### Prerequisites
- Understanding of control theory fundamentals
- Familiarity with distributed systems
- Programming experience in Python or similar

### Key Parameters
| Parameter | Description | Typical Range |
|-----------|-------------|---------------|
| TBD | To be determined from paper | - |

## References

- Keyu Li et al. (2026). "Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems." arXiv:2604.08963v1.
- PDF: https://arxiv.org/pdf/2604.08963v1

## Related Skills
- See other systems engineering skills in ai_collection
- Cross-reference with control theory and distributed systems

## Activation Keywords
- multi-agent systems
- agent collaboration
- MAS
- bias amplification
- multi-agent bias
- fairness

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*Generated from arXiv research on 2026-04-10*