Type
Text
Type
Dissertation
Advisor
Ortiz, Luis E | Mitchell, Joseph | Gao, Jie | Chen, Jing | Parkes, David.
Date
2013-12-01
Keywords
Causality, Computational Game Theory, Economic Networks, Microfinance, Social Influence, Social Networks | Computer science
Department
Department of Computer Science.
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/77286
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Who are the most influential senators in Congress? Is there a small coalition of senators who are influential enough to prevent filibusters? In a different setting of microfinance markets, can we predict the effects of interventions to help policy makers? In order to pursue such diverse questions, we propose causal strategic inference, a game-theoretic counterpart of causal probabilistic inference. Using this general framework, we study two different sets of problems, broadly on social networks and networked microfinance economies. In the first study, we introduce a new approach to the study of influence that captures the strategic aspects of the complex interactions in a network. We design influence games, a new class of graphical games, as a model of the behavior of a large but finite networked population. Influence games can deal with positive as well as negative influence without having to consider network dynamics. We characterize the computational complexity of various problems on influence games, propose effective solutions to the hard problems, and design approximation algorithms, with provable guarantees, for identifying the most influential individuals in a network. Our empirical study is based on the real-world data obtained from congressional voting records and Supreme Court rulings. Our second study is on microfinance economies. It is motivated by the challenge of formulating economic policies without the privilege of conducting trial-and-error experiments. First, we model a microfinance market as a two-sided economy. We then learn the parameters of the model from real-world data and design algorithms for various computational problems. We show the uniqueness of equilibrium interest rates for a special case and give a constructive proof of equilibrium existence in the general case. Using data from Bangladesh and Bolivia, we show that our model captures various real-world phenomena and can be used to assist policy makers in the microfinance sector. Despite contrasting application areas, these two studies bear a common signature that is prevalent in many other domains as well: the actions of the entities in a network-structured complex system are strategically inter-dependent. This dissertation presents a computational game-theoretic framework for studying causal questions in such scenarios. | 159 pages
Recommended Citation
Irfan, Mohammad Tanvir, "Causal Strategic Inference in Social and Economic Networks" (2013). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 3107.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/3107