  We propose a decision criterion for segmenting the cosmic web into different
structure types (voids, sheets, filaments, and clusters) on the basis of their
respective probabilities and the strength of data constraints. Our approach is
inspired by an analysis of games of chance where the gambler only plays if a
positive expected net gain can be achieved based on some degree of privileged
information. The result is a general solution for classification problems in
the face of uncertainty, including the option of not committing to a class for
a candidate object. As an illustration, we produce high-resolution maps of
web-type constituents in the nearby Universe as probed by the Sloan Digital Sky
Survey main galaxy sample. Other possible applications include the selection
and labelling of objects in catalogues derived from astronomical survey data.
