Contests: equilibrium analysis, design and learning
Contests are games where agents compete by making costly and irreversible investments to win valuable prizes. They model diverse scenarios ranging from crowdsourcing to competition among Bitcoin miners. Using tools from theoretical computer science, we contribute to the understanding of the agents&#...
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| Format: | Thèse |
| Langue: | English |
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2024
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| _version_ | 1826315174320013312 |
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| author | Ghosh, A |
| author2 | Elkind, E |
| author_facet | Elkind, E Ghosh, A |
| author_sort | Ghosh, A |
| collection | OXFORD |
| description | Contests are games where agents compete by making costly and irreversible investments to win valuable prizes. They model diverse scenarios ranging from crowdsourcing to competition among Bitcoin miners. Using tools from theoretical computer science, we contribute to the understanding of the agents' behavior in contests and make design recommendations to optimize practical objectives. In particular, we (i) analyze learning dynamics in Tullock contests using tools from probabilistic analysis of algorithms and optimization, (ii) design contests that improve diversity in participation, and (iii) study the existence, welfare efficiency, and computational complexity of equilibrium in a class of simultaneous contests. |
| first_indexed | 2024-12-09T03:20:49Z |
| format | Thesis |
| id | oxford-uuid:d75096af-8cc9-4f3f-8411-28faab88c1e1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:20:49Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | oxford-uuid:d75096af-8cc9-4f3f-8411-28faab88c1e12024-11-11T08:50:20ZContests: equilibrium analysis, design and learning Thesishttp://purl.org/coar/resource_type/c_db06uuid:d75096af-8cc9-4f3f-8411-28faab88c1e1Game theoryAlgorithmsComputational complexityOptimisation . . .Learning dynamicsEnglishHyrax Deposit2024Ghosh, AElkind, EGoldberg, PContests are games where agents compete by making costly and irreversible investments to win valuable prizes. They model diverse scenarios ranging from crowdsourcing to competition among Bitcoin miners. Using tools from theoretical computer science, we contribute to the understanding of the agents' behavior in contests and make design recommendations to optimize practical objectives. In particular, we (i) analyze learning dynamics in Tullock contests using tools from probabilistic analysis of algorithms and optimization, (ii) design contests that improve diversity in participation, and (iii) study the existence, welfare efficiency, and computational complexity of equilibrium in a class of simultaneous contests. |
| spellingShingle | Game theory Algorithms Computational complexity Optimisation . . . Learning dynamics Ghosh, A Contests: equilibrium analysis, design and learning |
| title | Contests: equilibrium analysis, design and learning
|
| title_full | Contests: equilibrium analysis, design and learning
|
| title_fullStr | Contests: equilibrium analysis, design and learning
|
| title_full_unstemmed | Contests: equilibrium analysis, design and learning
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| title_short | Contests: equilibrium analysis, design and learning
|
| title_sort | contests equilibrium analysis design and learning |
| topic | Game theory Algorithms Computational complexity Optimisation . . . Learning dynamics |
| work_keys_str_mv | AT ghosha contestsequilibriumanalysisdesignandlearning |