Tracking Truth by Weighting Proxies in Liquid Democracy
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Abstract (arXiv)
We study wisdom of the crowd effects in liquid democracy when agents are allowed to apportion weights to proxies by mixing their delegations. We show that in this setting -- unlike in the standard one where votes are always delegated in full to one proxy -- it becomes possible to identify delegation structures that optimize the truth-tracking accuracy of the group. We contrast this centralized solution with the group accuracy obtained in equilibrium when agents interact by greedily trying to maximize their own individual accuracy through mixed delegations, and study the price of anarchy of these games. While equilibria with mixed delegations may be as bad as in the standard delegations setting, they are never worse and may sometimes be better.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
3 papers.
- 2021When Can Liquid Democracy Unveil the Truth?cited by 2
- 2022Towards Group Learning: Distributed Weighting of Expertscited by 0
- 2025Snowveil: A Framework for Decentralised Preference Discoverycited by 0
- 2025Snowveil: A Framework for Decentralised Preference Discovery20 Dec 2025
- 2022Towards Group Learning: Distributed Weighting of Experts3 Jun 2022
- 2021When Can Liquid Democracy Unveil the Truth?5 Apr 2021