Computer Science Thesis Oral
— 3:30pm
Location:
In Person and Virtual - ET
-
Reddy Conference Room, Gates Hillman 4405 and Zoom
Speaker:
BAILEY FLANIGAN
,
Ph.D. Candidate, Computer Science Department, Carnegie Mellon University
https://sites.google.com/andrew.cmu.edu/baileyflanigan/home
Strengthening our Participatory Democracy Toolkit using Algorithms, Social Choice, and Social Science
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 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 a key potential impact of the background information/deliberation phase of a DM: increases in the extent to which participants consider how others in their society may be affected by different policy options. In Part IV, we discuss why our contributions can be useful regardless of how DMs ultimately fare in the political sphere, and we highlight how the enclosed research illustrates new ways to combine tools from political science and computer science.
Thesis Committee
Ariel Procaccia (Chair) (Carnegie Mellon University / Harvard University)
Nihar Shah
Anupam Gupta (New York University)
Nika Haghtalab (University of California, Berkeley)
Ashish Goel (Stanford University)
In Person and Zoom Participation. See announcement.