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Tracking Truth with Liquid Democracy

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arxiv 2107.11868 v3 pith:YT5NDN6Q submitted 2021-07-25 cs.DM cs.GT

classification cs.DMcs.GT
keywords delegationdemocracyliquidvotersdelegationsparadigmtheoreticaltruth
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abstract

The dynamics of random transitive delegations on a graph are of particular interest when viewed through the lens of an emerging voting paradigm, liquid democracy. This paradigm allows voters to choose between directly voting and transitively delegating their votes to other voters, so that those selected cast a vote weighted by the number of delegations they received. In the epistemic setting, where voters decide on a binary issue for which there is a ground truth, previous work showed that a few voters may amass such a large amount of influence that liquid democracy is less likely to identify the ground truth than direct voting. We quantify the amount of permissible concentration of power and examine more realistic delegation models, showing they behave well by ensuring that (with high probability) there is a permissible limit on the maximum number of delegations received. Our theoretical results demonstrate that the delegation process is similar to well-known processes on random graphs that are sufficiently bounded for our purposes. Along the way, we prove new bounds on the size of the largest component in an infinite P\'olya urn process, which may be of independent interest. In addition, we empirically validate the theoretical results, running six experiments (for a total of $N=168$ participants, $62$ delegation graphs and over $11k$ votes collected). We find that empirical delegation behaviors meet the conditions for our positive theoretical guarantees. Overall, our work alleviates concerns raised about liquid democracy and bolsters the case for the applicability of this emerging paradigm.

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Cited by 1 Pith paper

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  1. Fluid Democracy in Federated Data Aggregation

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A delegation-based voting mechanism, viscous-retained democracy, is proposed to select federated learning clients for weight transfer, with theoretical claims that it never performs worse than FedAvg in an idealized c...

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