Scaling up collective decision making

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While the Internet has revolutionized many human endeavors, commerce, education, social networking, to name a few, it has had little impact on collective decision making. This thesis presents the author's work on understanding how to enable large scale collective decision making, from various angles such as: deploying novel mechanisms in practice, and studying them empirically; analyzing preference aggregation algorithms (social choice rules) with respect to provable optimization guarantees, e.g., of fairness; designing theoretical mechanisms of decision making with strategic interaction among agents. The three parts of this thesis are described below. Knapsack Voting for Participatory Budgeting: This part looks at the phenomenon of Participatory Budgeting, and discusses the author's work in overcoming the challenges faced in building a practical voting platform ( for the same - in particular, the novel Knapsack Voting method, and theoretical and empirical insights as to how it performs better than conventional methods. Metric Distortion and Fairness: Given the promise of algorithmic mechanisms in transforming societal decision making, a theoretical study of their efficiency and fairness becomes imperative. This section of the thesis discusses the usefulness of the metric distortion model in answering such questions. A novel method of quantifying fairness, using ideas from multi-objective optimization, is also presented. In addition to answering several open questions pertaining to metric distortion, it is shown how the simple Copeland rule achieves a constant-factor fairness ratio. Implementing the Lexicographic Maxmin Bargaining Solution: To broaden the scope of decision-making mechanisms to more complex settings, there is a need for protocols that incorporate the interaction of agents into their functioning. To this end, studying bargaining models is the natural first step. This section discusses the author's work on designing bargaining mechanisms which can guarantee (lexicographic) max-min fairness in equilibrium.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2019; ©2019
Publication date 2019; 2019
Issuance monographic
Language English


Author Kollagunta Krishnaswamy, Anilesh
Degree supervisor Goel, Ashish
Thesis advisor Goel, Ashish
Thesis advisor Pilanci, Mert
Thesis advisor Wootters, Mary
Degree committee member Pilanci, Mert
Degree committee member Wootters, Mary
Associated with Stanford University, Department of Electrical Engineering.


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Anilesh Kollagunta Krishnaswamy.
Note Submitted to the Department of Electrical Engineering.
Thesis Thesis Ph.D. Stanford University 2019.
Location electronic resource

Access conditions

© 2019 by Anilesh Kollagunta Krishnaswamy
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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