| Sumario: | 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.
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