Shapley Values and their Application Inside and Outside of Economics
Autor: | Anonymous |
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EAN: | 9783346796172 |
eBook Format: | |
Sprache: | Englisch |
Produktart: | eBook |
Veröffentlichungsdatum: | 18.01.2023 |
Kategorie: | |
Schlagworte: | Applied Economics Fairness Fairness Axioms Literature Review Marketing Channel attribution Mathmatical Fairness Microeconomics Network Theory Politics Portfolio Theory SHAP Shapley Values Social Network Theory economics |
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Bachelor Thesis from the year 2022 in the subject Business economics - Business Management, Corporate Governance, grade: 1.15, Technical University of Ilmenau, language: English, abstract: The Shapley Value is a famous solution concept of cooperative Game Theory which aims at allocating a jointly generated payoff to the contributing members under fair circumstances. It represents a unique solution to a set of predefined fairness axioms which favoured its employment in many applications and scientific literature over the years. The resulting vast amount of literature is mainly focused on applying the Shapley Value in various areas, which created a discrepancy between the advancements in practical solutions and review sources which capture a holistic overview of possible application areas. The objectives of this literature review were therefore to firstly identify application areas, then highlight the respective employment and current advancements by integrating literature sources in a second step in order to discuss the usefulness in regard to popular non-game theoretic methods at the end. Methodically, a literature review was accomplished which utilized multiple databases and search-engines to find Shapley Value-related literature, which was used to categorize application areas. From there on, keyword- and forward/backward searches were executed for every identified application area to further enrich the literature basis. In total, 168 unique studies contributed to this review which revealed that Shapley Values are employed in Profit and Cost allocation, Marketing, Machine Learning, Politics, Portfolio and Social Network Theory and in Statistics. As this review also assessed the current and future relevance of individual application areas, it was projected that a majority of the here presented applications will be of value in the future.