We formalize "degrees of fungibility" by differentiating goods according to both their underlying attributes and the perceived value and/or usefulness of those attributes to a value assessor. This allows us to distinguish between goods that appear to be "exactly the same" from those goods that...

The nature of learning processes as well as evolutionary considerations suggest that aesthetic judgement is of central importance in the formation of custom. Learning and extrapolation rely on evaluations of non-instrumental features like simplicity, analogy, straightforwardness, and clarity....

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual...

In recent years, machine learning research has gained momentum: New developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods...