Part of the design of many blockchains and cryptocurrencies includes a treasury, which periodically allocates collected funds to various projects that could be beneficial to their ecosystem.
These projects are then voted on and selected by the users of the respective cryptocurrency. To
better inform the users’ choices, the proposals can be reviewed, in distributed fashion. Motivated
by these intricacies, we study the problem of crowdsourcing reviews for different proposals, in
parallel. During the reviewing phase, every reviewer can select the proposals to write reviews
for, as well as the quality of each review. The quality levels follow certain very coarse community guidelines (since the review of the reviews has to be robust enough, even though it is
also crowdsourced) and can have values such as ‘excellent’ or ‘good’. Based on these scores and
the distribution of reviews, every reviewer will receive some reward for their efforts. In this
paper, we consider a simple and intuitive reward scheme and show that it always has pure Nash
equilibria, under two different scenarios. In addition, we show that these equilibria guarantee
constant factor approximations for two natural metrics: the total quality of all reviews, as well
as the fraction of proposals that received at least one review, compared to the optimal outcome
Dettaglio pubblicazione
2022, ACM Advances in Financial Technologies, Pages 268-280
Parallel Contests for Crowdsourcing Reviews: Existence and Quality of Equilibria (04b Atto di convegno in volume)
Birmpas Georgios, Kovalchuk Lyudmila, Lazos Philip, Oliynykov Roman
ISBN: 9781450398619
Gruppo di ricerca: Algorithms and Data Science
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