8 citations · 12 across the 12 of their papers we have counts for
12 papers
The New Mathematics of Democracy
Bailey Flanigan, Ismar Volic
This article surveys emerging directions in the mathematics of democracy. It uses three case studies --- voting theory, participatory budgeting, and deliberative democracy --- to h…
Internal Pluralism and the Limits of Pairwise Comparisons
Bailey Flanigan, Michelle Si
Local pairwise comparisons are a standard tool for learning how people want decision rules to work, e.g., in participatory design or alignment. However, their use builds in two str…
The End Justifies the Mean: A Linear Ranking Rule for Proportional Sequential Decisions
Carmel Baharav, Niclas Boehmer, Bailey Flanigan +1
AI alignment and participatory design motivate a new democratic design problem: how to collectively choose a decision rule to use repeatedly. We study this problem for linear ranki…
Strategic Candidacy in Generative AI Arenas
Chris Hays, Rachel Li, Bailey Flanigan +1
AI arenas, which rank generative models from pairwise preferences of users, are a popular method for measuring the relative performance of models in the course of their organic use…
Near-Optimal Dropout-Robust Sortition
Maya Pal Gambhir, Bailey Flanigan, Aaron Roth
Citizens' assemblies - small panels of citizens that convene to deliberate on policy issues - often face the issue of panelists dropping out at the last-minute. Without interventio…
Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies
Angelos Assos, Carmel Baharav, Bailey Flanigan +1
Citizens' assemblies are an increasingly influential form of deliberative democracy, where randomly selected people discuss policy questions. The legitimacy of these assemblies hin…
Fair, Manipulation-Robust, and Transparent Sortition
Carmel Baharav, Bailey Flanigan
Sortition, the random selection of political representatives, is increasingly being used around the world to choose participants of deliberative processes like Citizens' Assemblies…
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Jessica Dai, Bailey Flanigan, Nika Haghtalab +2
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare. In this work, we show that misalign…
Distortion Under Public-Spirited Voting
Bailey Flanigan, Ariel D. Procaccia, Sven Wang
A key promise of democratic voting is that, by accounting for all constituents' preferences, it produces decisions that benefit the constituency overall. It is alarming, then, that…
CS-JEDI: Required DEI Education, by CS PhD Students, for CS PhD Students
Bailey Flanigan, Ananya A Joshi, Sara McAllister +1
Computer science (CS) has historically struggled with issues related to diversity, equity, and inclusion (DEI). Based on how these issues were affecting PhD students in our departm…
Smoothed Analysis of Social Choice, Revisited
Bailey Flanigan, Daniel Halpern, Alexandros Psomas
A canonical problem in social choice is how to aggregate ranked votes: given voters' rankings over candidates, what voting rule should we use to aggregate these votes i…
Neutralizing Self-Selection Bias in Sampling for Sortition
Bailey Flanigan, Paul Gölz, Anupam Gupta +1
Sortition is a political system in which decisions are made by panels of randomly selected citizens. The process for selecting a sortition panel is traditionally thought of as unif…