  One of the consequences of persistent technological change is that it force
individuals to make decisions under extreme uncertainty. This means that
traditional decision-making frameworks cannot be applied. To address this issue
we introduce a variant of Case-Based Decision Theory, in which the solution to
a problem obtains in terms of the distance to previous problems. We formalize
this by defining a space based on an orthogonal basis of features of problems.
We show how this framework evolves upon the acquisition of new information,
namely features or values of them arising in new problems. We discuss how this
can be useful to evaluate decisions based on not yet existing data.
