Not all uncertainty is alike: volatility, stochasticity, and exploration
The paper discusses the differences between volatility and stochasticity in the context of adaptive decision-making in artificial intelligence. It highlights how these two types of uncertainty influence exploration strategies in opposite ways. The author introduces a new exploration strategy called Cause-Aware Uncertainty-Sensitive Exploration (CAUSE), which outperforms traditional methods in environments with varying noise structures.
- ▪Volatility enhances exploration while stochasticity suppresses it.
- ▪The paper extends the Gittins index framework to address these differences.
- ▪CAUSE is a new exploration strategy that improves decision-making in uncertain environments.
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Computer Science > Artificial Intelligence arXiv:2605.19215 (cs) [Submitted on 19 May 2026] Title:Not all uncertainty is alike: volatility, stochasticity, and exploration Authors:Payam Piray View a PDF of the paper titled Not all uncertainty is alike: volatility, stochasticity, and exploration, by Payam Piray View PDF HTML (experimental) Abstract:Adaptive decision-making in biological and artificial intelligence requires balancing the exploitation of known outcomes with the exploration of uncertain alternatives. Although prior work suggests that uncertainty generally promotes exploration, it has typically treated distinct sources of environmental uncertainty as equivalent.
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