  This manuscript presents an advanced framework for Bayesian learning by
incorporating action and state-dependent signal variances into decision-making
models. This framework is pivotal in understanding complex data-feedback loops
and decision-making processes in various economic systems. Through a series of
examples, we demonstrate the versatility of this approach in different
contexts, ranging from simple Bayesian updating in stable environments to
complex models involving social learning and state-dependent uncertainties. The
paper uniquely contributes to the understanding of the nuanced interplay
between data, actions, outcomes, and the inherent uncertainty in economic
models.
