Abstract
Motivated by governance models adopted in blockchain applications, we study the problem of selecting appropriate system updates in a decentralized way. Contrary to most existing voting approaches, we use the input of a set of motivated experts of varying levels of expertise. In particular, we develop an approval voting inspired selection mechanism through which the experts approve or disapprove the different updates according to their perception of the quality of each alternative. Given their opinions, and weighted by their expertise level, a single update is then implemented and evaluated, and the experts receive rewards based on their choices. We show that this mechanism always has approximate pure Nash equilibria and that these achieve a constant factor approximation with respect to the quality benchmark of the optimal alternative. Finally, we study the repeated version of the problem, where the weights of the experts are adjusted after each update, according to their performance. Under mild assumptions about the weights, the extension of our mechanism still has approximate pure Nash equilibria in this setting.
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Notes
- 1.
For instance, an update in a DeFi protocol could alter the exchange rate calculation on assets the user might want to invest in. However, the user will only reap the benefits \(g_{ij}\) if this update is ‘good’, since otherwise the protocol will be rolled back to its prior state.
- 2.
This is completely precise in the case where the external rewards \(g_{ij}\) are all 0, but it is still largely true whenever the rewards of the mechanism are large compared to external rewards.
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Acknowledgements
We would like to thank Nikos Karagiannidis for many enlightening meetings, helping us formulate the model in early versions of this work. This work was supported by the ERC Advanced Grant 788893 AMDROMA “Algorithmic and Mechanism Design Research in Online Markets”, the MIUR PRIN project ALGADIMAR “Algorithms, Games, and Digital Markets”, and the NWO Veni project No. VI.Veni.192.153.
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Amanatidis, G., Birmpas, G., Lazos, P., Marmolejo-Cossío, F. (2022). Decentralized Update Selection with Semi-strategic Experts. In: Kanellopoulos, P., Kyropoulou, M., Voudouris, A. (eds) Algorithmic Game Theory. SAGT 2022. Lecture Notes in Computer Science, vol 13584. Springer, Cham. https://doi.org/10.1007/978-3-031-15714-1_23
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