Why Shapley Value and Its Generalizations Are Effective in Economics and Finance, Machine Learning, and Systems Engineering

Authored by

Miroslav Svitek, Niklas Winnewisser, Michael Beer, Olga Kosheleva, Vladik Kreinovich

Abstract

In many practical situations, it is necessary to fairly divide the joint gain between the contributors. In the 1950s, the Nobelist Lloyd Shapley showed that under some reasonable conditions, there is only one way to make this division. The resulting Shapley value is now actively used in situations that go beyond economics and finance—and in which Shapley’s conditions are not always satisfied: in machine learning, in systems engineering, etc. In this paper, we explain why Shapley value can be applied to such situations, and how can we generalize Shapley value to make it even more adequate for these new applications.

Details

Organisation(s): Institute for Risk and Reliability
External Organisation(s): Czech Technical University (CTU)
University of Texas at El Paso
Type: Contribution to book/anthology
Pages: 15-26
No. of pages: 12
Publication date: 05.02.2026
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Control and Systems Engineering, Engineering (miscellaneous), Computer Science Applications, Artificial Intelligence
Electronic version(s): https://doi.org/10.1007/978-3-032-06179-9_2 (Access: Closed )