Risk-Aware Decision Policies for Agents Under Noisy Perception
arXiv:2608.06420v1 Announce Type: new Abstract: Perception in biological systems is inherently noisy, requiring organisms to make decisions under uncertainty where misclassification can be costly or fatal. We present an Artificial Life predator-prey model of foraging under noisy perception, and compare agent performance when using various policies that take into account their noisy predictions. Through controlled experiments under both symmetric and asymmetric perceptual noise, we show that blindly trusting perceptual labels leads to catastrophic failure as noise increases, while uncertainty-aware strategies significantly improve survival and reduce fatal errors. We further observe qualitative regime shifts in behaviour, with agents transitioning from exploratory to conservative strategies as uncertainty increases. Our model links risk-sensitive foraging, ecological information use, and Artificial Life by showing that explicit information gathering can improve robustness when perception is unreliable. These results highlight the importance of uncertainty-aware decision-making and provide an interpretable artificial life analogue to robust learning with noisy labels.
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[Submitted on 5 Aug 2026]
Title:Risk-Aware Decision Policies for Agents Under Noisy Perception
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Abstract:Perception in biological systems is inherently noisy, requiring organisms to make decisions under uncertainty where misclassification can be costly or fatal. We present an Artificial Life predator-prey model of foraging under noisy perception, and compare agent performance when using various policies that take into account their noisy predictions. Through controlled experiments under both symmetric and asymmetric perceptual noise, we show that blindly trusting perceptual labels leads to catastrophic failure as noise increases, while uncertainty-aware strategies significantly improve survival and reduce fatal errors. We further observe qualitative regime shifts in behaviour, with agents transitioning from exploratory to conservative strategies as uncertainty increases. Our model links risk-sensitive foraging, ecological information use, and Artificial Life by showing that explicit information gathering can improve robustness when perception is unreliable. These results highlight the importance of uncertainty-aware decision-making and provide an interpretable artificial life analogue to robust learning with noisy labels.
Comments: 8 pages, 6 figures. Submitted to the 2026 Conference on Artificial Life (ALIFE2026)
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
MSC classes: 68T37
ACM classes: I.2.11
Cite as: arXiv:2608.06420 [cs.LG]
(or arXiv:2608.06420v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.06420
arXiv-issued DOI via DataCite
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From: David Szczecina [view email] [v1] Wed, 5 Aug 2026 18:15:21 UTC (1,171 KB)
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