Wednesday, September 22, 2021 - 4:00pm to 4:30pm
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LIDS & Stats Tea
LIDS & IDSS
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In response to the frustration with the current democratic institutions that failed to adapt to the evolving society, alternative decision-making processes emerged in recent years. Requests for more representativity of the citizenry resulted in the formation of citizens assemblies, but the latter does not guarantee that the randomly chosen representatives are competent to decide on the issues at stake. We study another voting paradigm, fluid democracy, that relies on letting agents choose between voting themselves and transitively delegating their votes to better-informed agents in their neighborhood. Such a system allows for more direct representativity while also enabling competent voters to be endogenously identified in a social network. While fluid democracy has been viewed as a system that can combine the best aspects of direct and representative democracy, it can also result in situations where few voters amass a large amount of influence. To analyze the impact of this shortcoming, we consider what has been called an epistemic setting, where voters decide on a binary issue for which there is ground truth. Previous work has shown that under certain assumptions on the delegation mechanism, the concentration of power is so severe that fluid democracy is less likely to identify the ground truth than direct voting. We examine different, arguably more realistic, classes of mechanisms, and prove they behave well by ensuring that (with high probability) there is a limit on the concentration of power. Our proofs demonstrate that delegations can be treated as stochastic processes and that they can be compared to well-known random graph processes from the literature such as preferential attachment and multi-types branching processes that are sufficiently bounded for our purposes. Our results suggest that the concerns raised about fluid democracy can be overcome, thereby bolstering the case for this emerging paradigm.
This is joint work with Daniel Halpern, advised by Ali Jadbabaie, Ariel Procaccia, Joe Halpern, and Elchanan Mossel. See here for the pre-print.
Manon is a PhD student in the Social and Engineering Program advised by Ali Jadbabaie and Munther Dahleh, studying democratic decision-making processes in the digital era and media perception.