Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Studies in Big Data |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 15-26 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 978-3-032-06179-9 |
| ISBN (Print) | 978-3-032-06178-2 |
| Publikationsstatus | Veröffentlicht - 5 Feb. 2026 |
Publikationsreihe
| Name | Studies in Big Data |
|---|---|
| Band | 181 |
| ISSN (Print) | 2197-6503 |
| ISSN (elektronisch) | 2197-6511 |
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.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Ingenieurwesen (sonstige)
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Artificial intelligence
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- BibTex
- RIS
Studies in Big Data. Springer Science and Business Media Deutschland GmbH, 2026. S. 15-26 (Studies in Big Data; Band 181).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Why Shapley Value and Its Generalizations Are Effective in Economics and Finance, Machine Learning, and Systems Engineering
AU - Svitek, Miroslav
AU - Winnewisser, Niklas
AU - Beer, Michael
AU - Kosheleva, Olga
AU - Kreinovich, Vladik
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026/2/5
Y1 - 2026/2/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=105029825562&partnerID=8YFLogxK
U2 - 10.1007/978-3-032-06179-9_2
DO - 10.1007/978-3-032-06179-9_2
M3 - Contribution to book/anthology
AN - SCOPUS:105029825562
SN - 978-3-032-06178-2
T3 - Studies in Big Data
SP - 15
EP - 26
BT - Studies in Big Data
PB - Springer Science and Business Media Deutschland GmbH
ER -