Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty o...

Bayesian Deep Learning | Uncertainty in Deep Learning

Uncertainty and Uncertainty Management in EFL Translators

This study tried to examine EFL translators’ uncertainty and uncertainty management strategies through employing think aloud procedures. The participants of this study were some MA andBA translators selected from several universities in Iran. To this aim, a proficiency test was firstly administered among the volunteers. Then, think aloud protocol and retrospective interview were used to collect...

A Bayesian Model of Risk and Uncertainty

We show that the distinction between risk and uncertainty can be modeled within a Bayesian framework if (1) an agent faces repeated draws from an unknown distribution and (2) his utility is not separable across these draws. The resulting uncertainty premium can help explain a number of asset-pricing anomalies. To better model uncertainty, we define a new distribution called the categorical Diri...

Bayesian effect estimation accounting for adjustment uncertainty.

Model-based estimation of the effect of an exposure on an outcome is generally sensitive to the choice of which confounding factors are included in the model. We propose a new approach, which we call Bayesian adjustment for confounding (BAC), to estimate the effect of an exposure of interest on the outcome, while accounting for the uncertainty in the choice of confounders. Our approach is based...