In system development, epistemic uncertainty is an ever-present possibility when reasoning about the causal factors during hazard analysis. Such uncertainty is common when complicated systems interact with one another, and it is dangerous because it impairs hazard analysis and thus increases the chance of overlooking unsafe situations. Uncertainty around causation thus needs to be managed well....

The notions of uncertainty and closeness have long played an important role in games. Many games such as dart and 20Q involve uncertainty and, furthermore one can describe a notion of closeness in such games. Closeness of the dart to the aim or closeness of the golf ball to the hole can be considered as the instances of the situations that involve uncertainty. In dart, for instance, a player ca...

Risk assessment is an important and significant aid in the decision making process. Risk assessment is performed using ‘model’ and a model is a function of parameters which are usually affected by uncertainty. Some model parameters are affected by aleatory uncertainty and some others are affected by epistemic uncertainty. In this paper we propose a hybrid method to deal with propagation of both...

When using lower probabilities to model uncertainty about the value assumed by a variable, 2-monotonicity is an interesting property to satisfy, as it greatly facilitates further treatments (such as the computation of lower/upper expectation bounds). In this paper, we show that multivariate joint models induced from marginal ones by strong independence, epistemic independence or epistemic irrel...

Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. We show how such a decomposition arises naturally in a Bayesian active learning scenario and develop a new objective for reliable re...

Epistemic uncertainty has been a subject heavily discussed in recent years. Where uncertainty of estimations for long time has been disregarded, it is now viewed as an inherent property of the system model. This work demonstrates how different data types like expert estimations and manufacturer’s data with various degrees of epistemic uncertainty are acquired and aggregated in a coherent framew...

Multi-objective evolutionary algorithms (MOEAs) have proven to be a powerful tool for global optimization purposes of deterministic problem functions. Yet, in many real-world problems, uncertainty about the correctness of the system model and environmental factors does not allow to determine clear objective values. Stochastic sampling as applied in noisy EAs neglects that this so-called epistem...

In modeling and simulation (M&S), we seek to predict the state of a system using a computer-based simulation of a differential equation-based model. In general, the inputs to the model may contain uncertainty due to inherent randomness (aleatory uncertainty), a lack of knowledge (epistemic uncertainty), or a combination of the two. In many practical cases, there is so little knowledge of a mode...

The need to differentiate between epistemic and aleatory uncertainty is now well admitted by the risk analysis community. One way to do so is to model aleatory uncertainty by classical probability distributions and epistemic uncertainty by means of possibility distributions, and then propagate them by their respective calculus. The result of this propagation is a random fuzzy variable. When dea...