---
title: Aleatoric vs Epistemic Uncertainty
type: concept
tags: [uncertainty, ml, abps, agentic-bpm]
sources: ["[[sources/2025-elyasaf-self-modifying-abps]]"]
created: 2026-04-15
updated: 2026-04-15
---

# Aleatoric vs Epistemic Uncertainty

Standard ML distinction (Hüllermeier & Waegeman 2021) emphasised by Elyasaf et al. 2025 as central to safe self-modification in ABPSs:

- **Aleatoric** — inherent randomness (irreducible noise in durations, outcomes, resources). Modeled with probability distributions.
- **Epistemic** — lack-of-knowledge uncertainty (reducible by gathering more data or targeted sensing). Addressed with active learning, Bayesian inference.

Distinguishing the two determines the mitigation strategy: more data (epistemic) vs probabilistic hedging (aleatoric). Uncertainty quantification also gates **learning-to-defer**: when epistemic uncertainty is high an ABPS should escalate to a human decision-maker.

Communicating uncertainty to humans requires pairing quantitative measures with XAI-style explanations (Miller 2019: "probabilities probably don't matter"), e.g. via natural-language generation or Shapley-based explanations.

## Relation to the philosophical probability debate
The aleatoric/epistemic split maps onto the **chance/credence** distinction in philosophy of probability ([[sources/2023-anjum-rocca-phi403-lecture-15-credence]]): aleatoric uncertainty is ontological probability (worldly chance or [[concepts/dispositionalism|propensity]]); epistemic uncertainty is credence (degree of belief under limited knowledge). See [[concepts/probabilistic-causation]] for the philosophical context.

## Related
[[concepts/self-modification]] · [[concepts/explainability-apm]] · [[concepts/predictive-process-monitoring]] · [[concepts/probabilistic-causation]]