Bailey Flanigan

Expanding our Participatory Democracy Toolkit using Algorithms, Social Choice, and Social Science Degree Type: Ph.D. in Computer Science
Advisor(s): Ariel Procaccia
Graduated: May 2024

Abstract:

In most of the world's democracies, policy decisions are primarily made by elected political officials. However, under mounting dissatisfaction with representative government due to issues ranging from social inequality to public distrust, a new proposal is taking off: to augment representative democracy with mechanisms by which the public can directly participate in policymaking.

The guiding application of this thesis will be one particular model of participation, deliberative minipublics (DMs), though we will argue that our contributions may apply to many models of direct participation. In a DM, a panel of citizens is selected by lottery from the population; then, this panel convenes around a particular policy issue to study background information, deliberate amongst themselves, and then weigh in on the issue. DMs have been gaining momentum over the past decade, and they are now being used at national and supranational levels, and are even being integrated into representative governments.

Motivated by this application domain, we make the following main contributions: In Part I, we design algorithms for performing the random selection of DM participants, a process known as sortition. Our sortition algorithms permit users to make optimal trade-offs between descriptive representation and other desirable properties conferred by randomness, and we characterize these tradeoffs using game theory, optimization, and empirics. In Part II, we use a novel social choice theory framework to investigate a notion of representation that departs from descriptive representation in a key way: it accounts for the political reality that people may be affected to widely varying degrees by any given policy decision. In Part III, we study an important hypothesized impact of deliberation: increasing the extent to which participants consider how others in their society may be affected by different policies. In Part IV, we highlight how the enclosed research illustrates new ways to combine tools from political science and computer science.

Thesis Committee:
Ariel Procaccia (Chair, Harvard University)
Nihar Shah (Carnegie Mellon University)
Anupam Gupta
Ashish Goel (Stanford University)
Nika Haghtalab (University of California, Berkeley)

Srinivasan Seshan, Head, Computer Science Department
Martial Hebert, Dean, School of Computer Science

Keywords:
Social Choice, Algorithms, Voting, Deliberative Democracy, Sortition