Algorithms for fair, manipulation-robust, and transparent sortition
From citizens’ assemblies to decisions on the blockchain, we are in the midst of a surge in potential applications of sortition, the age-old practice of selecting political representatives by lottery: sortition. Sortition offers several normatively desirable ideals: (1) a panel selected via sortition is likely to be nearly proportionally representative of all population subgroups; (2) it gives all eligible participants an equal opportunity to participate; and (3) it resists subversion by both organizers and potential participants. But in most modern sortition applications, people cannot be compelled to participate. As a result, a uniform lottery is not viable because it would replicate the strong selection bias in who opts in. With a uniform lottery off the table, how should we do sortition, and to what extent can we retain the ideals above?
Bailey Flanigan (Harvard) discusses results from her work on designing sortition algorithms. Motivated by input from multiple nonprofit organizations who deploy citizens’ assemblies, her algorithms enforce approximate proportional representation (ideal (1)) as a constraint, and then try to optimally achieve ideals (2) and (3) within these constraints. In the process, she offers new mathematical formalizations of theoretical sortition ideals using the tools of optimization, algorithms, and game theory. Algorithms from this line of work have been deployed broadly by citizens’ assembly practitioners, they have been used to select high-profile citizens’ assemblies.
These algorithms are available for public use on Panelot.org.
About the presenter
Bailey is an HDSI postdoctoral fellow, hosted at the Harvard Ash Center for Democratic Governance and Innovation. She received her PhD in computer science from Carnegie Mellon University, advised by Ariel Procaccia. In Fall of 2025, she will join MIT as an assistant professor of political science and computer science.
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