arXiv:2603.20750v2 Announce Type: replace Abstract: Social actors do not observe a common social world: each individual forms judgments from a partial and potentially distorted view of the surrounding network. We study whether graph-local evidence and credibility-weighted communication can generate persistent distortions in perceived academic standing, even when agents repeatedly receive objective performance signals. We introduce a data-constrained multi-agent framework in which LLM agents operate through individualized subjective graphs that determine peer visibility, evidence access, and in
The rapid advancement in large language models and multi-agent systems enables the simulation of complex social dynamics with increasing fidelity.
This research explores fundamental limitations and potential distortions in social perception within AI agent systems, crucial for robust and ethical AI deployment.
Our understanding of how 'objective' information can be distorted by 'subjective' networked interactions within AI agent groups is enhanced, impacting future trust models.
- · AI ethicists
- · Multi-agent system developers
- · Social scientists
- · Organizations deploying AI for social simulation
- · Developers unprepared for embedded biases
- · Systems relying on perfect information flow
More sophisticated and robust multi-agent AI systems, capable of simulating complex social phenomena, will emerge.
This could lead to new avenues for designing AI agents that proactively mitigate 'echo chamber' effects or misinformation within their networks.
These insights may inform the design of human-AI interfaces and social platforms, creating more resilient information ecosystems by understanding agent-to-agent distortion.
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Read at arXiv cs.AI