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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Shapley-Based Core-Selecting Payment Rules |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2022 |
| Abstract Text | Recent research has found Shapley-based core-selecting payment rules to perform well in combinatorial auctions, but we still lack a satisfactory explanation for their good performance. We investigate the hypothesis that low local manipulability can explain the good performance of Shapley-nearest, one Shapley-based payment rule. To this end, we define a local manipulability metric that measures the expected influence bidders can exert on their own payments. In order to measure the local manipulability, we develop a method for finding the derivatives of core-selecting payment rules in any domain. We provide analytical and experimental results for the well-known LLG domain and for the L3G domain, a slightly more complex domain we introduce. We show that Shapley-nearest does not have a particularly low local manipulability compared to other core-selecting payment rules. Further, we show that local manipulability generally is not a good predictor for the performance of payment rules. Lastly, we introduce a novel core- selecting payment rule that we call SVCG-nearest. It is a hybrid between Shapley-nearest and Quadratic, the payment rule used most commonly in practice. We find SVCG-nearest to have good performance and show that it has very low local manipulability. |
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