A Caltech Library Service

Data: Implications for Markets and for Society


Ziani, Juba (2019) Data: Implications for Markets and for Society. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/XZHX-1M46.


Every day, massive amounts of data are gathered, exchanged, and used to run statistical computations, train machine learning algorithms, and inform decisions on individuals and populations. The quick rise of data, the need to exchange and process it, to take data privacy concerns into account, and to understand how it affects decision-making, introduce many new and interesting economic, game theoretic, and algorithmic challenges.

The goal of this thesis is to provide theoretical foundations to approach these challenges. The first part of this thesis focuses on the design of mechanisms that purchase then aggregate data from many sources, in order to perform statistical tasks. The second part of this thesis revolves around the societal concerns associated with the use of individuals' data. The first such concern we examine is that of privacy, when using sensitive data about individuals in statistical computations; we focus our attention on how privacy constraints interact with the task of designing mechanisms for acquisition and aggregation of sensitive data. The second concern we focus on is that of fairness in decision-making: we aim to provide tools to society that help prevent discrimination against individuals and populations based on sensitive attributes in their data, when making important decisions about them. Finally, we end this thesis on a study of the interactions between data and strategic behavior. There, we see data as a source of information that informs and affects agents' incentives; we study how information revelation impacts agent behavior in auctions, and in turn how a seller should design auctions that take such information revelation into account.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Data; mechanism design; privacy; fairness; strategic behavior
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computer Science
Awards:Bhansali Family Dissertation Prize in Computer Science, 2019.
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Ligett, Katrina A. (advisor)
  • Wierman, Adam C. (co-advisor)
Thesis Committee:
  • Echenique, Federico (chair)
  • Ligett, Katrina A.
  • Wierman, Adam C.
  • Doval, Laura
  • Roth, Aaron
Defense Date:29 May 2019
Record Number:CaltechTHESIS:05292019-162418941
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Chapter 6. adapted for Chapter 4. adapted for Chapter 3 and Chapter 5. adapted for Chapter 7.
Ziani, Juba0000-0002-3324-4349
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:11563
Deposited By: Juba Ziani
Deposited On:31 May 2019 21:57
Last Modified:04 Oct 2019 00:25

Thesis Files

PDF - Final Version
See Usage Policy.


Repository Staff Only: item control page