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Faculty & Research


Subjectively Biased Objective Functions

Journal Article
The maximization of an objective function is a cornerstone of OR/MS modeling. How can we integrate subjective values within these models without weakening their scientific objectivity? This paper proposes a methodological answer that maintains the objective function and relaxes the maximization principle. The authors introduce a class of biased models that combine an objective function with a “subjective” factor that biases the maximization of such a function. The authors present the main properties of these models as well as the axiomatic foundations that allow for the rigorous measurement of biasing factors. The authors invite OR/MS scholars to participate in the development of practical applications integrating ethical and sustainability values.

Affiliate Professor of Decision Sciences

Emeritus Professor of Technology and Operations Management