  Decision making in modern stochastic systems, including e-commerce platforms,
financial markets, and healthcare systems, has evolved into a multifaceted
process that involves information acquisition and adaptive information sources.
This paper initiates a study on this integrated process, where these elements
are not only fundamental but also interact in a complex and dynamically
intertwined manner.
  We introduce a relatively simple model, which, however, captures the novel
elements we consider. Specifically, a decision maker (DM) can choose between an
established product $A$ with a known value and a new product $B$ with an
unknown value. The DM can observe signals about the unknown value of product
$B$ and can also opt to exchange it for product $A$ if $B$ is initially chosen.
Mathematically, the model gives rise to a sequential optimal stopping problem
with two different informational regimes (before and after buying product $B$),
differentiated by the initial, coarser signal and the subsequent, finer one. We
analyze the underlying problems using predominantly viscosity solution
techniques, differing from the existing literature on information acquisition
which is based on traditional optimal stopping techniques. Additionally, our
modeling approach offers a novel framework for developing more complex
interactions among decisions, information sources, and information costs
through a sequence of nested obstacles.
