Decision-making at scale
Abstract/Contents
- Abstract
- Recent years have seen an increase in democratic innovations aimed at increasing the participation of the public in policy-making. This observation, coupled with the increasing prevalence of internet-based communication, points to a very real possibility of implementing participatory democracies on a mass-scale in which every individual is invited to contribute their ideas and opinions. This dissertation addresses two challenges to realizing such a vision: scalability and polarization. I first address the challenge of scaling up voting. In the classical theory of social choice, participants submit rankings over all the candidates. This is, unfortunately, not feasible when voting over a large number of contributed ideas. We give algorithms that elicit approximate winners for a large class of social choice functions while only requiring participants to make a small number of comparisons, significantly improving on previous lower bounds. We show that one can further improve these algorithms to preserve voter privacy, resist strategic manipulation, and account for streaming settings. We demonstrate these ideas in Finland's recent off-road traffic law reform. We also propose a model for scaling up aggregation more collaboratively, through sequences of small group deliberations. We consider whether such interactions can be used to find the wisdom of the crowd, defined here to be the median opinion, and show a result indicating the importance of having groups of at least three. Specifically, we show that a small number of triadic interactions are able to find a tight approximation of the generalized median, but that dyadic interactions satisfying natural axioms are unable to. Finally, we briefly describe our work addressing the challenge of polarization in society. Several empirical studies have shown that homophily, i.e. greater interaction between like-minded individuals, results in polarization. However, we show that well-known models of opinion formation based on repeated averaging can never be polarizing, even if individuals are arbitrarily homophilous. We generalize these models to account for a phenomenon well-known in social psychology as biased assimilation and show that a simple model of homophilous networks can result in either polarization, persistent disagreement or consensus depending on how biased individuals are. This model is then used to discuss methods for countering polarization.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Lee, David T |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Goel, Ashish |
| Thesis advisor | Goel, Ashish |
| Thesis advisor | Johari, Ramesh, 1976- |
| Thesis advisor | Saberi, Amin |
| Advisor | Johari, Ramesh, 1976- |
| Advisor | Saberi, Amin |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | David T. Lee. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by David T Lee
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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