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Game-Theoretic Analysis of Strategic Behaviour in Networks, Crowds and Classrooms

Electronic Theses of Indian Institute of Science

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Field Value
 
Title Game-Theoretic Analysis of Strategic Behaviour in Networks, Crowds and Classrooms
 
Creator Vallam, Rohith Dwarakanath
 
Subject Game Theory
Networks-Strategic Behavior
Crowds-Strategic Behavior
Classrooms-Strategic Behavior
Graph Theory
Organizational Networks
Social Networks
Online Labor Markets
Recommender Systems
Online Classrooms
Massively Open Online Courses (MOOC)
Strategic Networks-Localized Payoffs
Crowdsourcing
Crowdsourced Tree Networks
Online Educational Forums
Network Formation with Localized Payoffs (NFLP)
Continuous Scoring Rules
Computer Science
 
Description Over the past decade, the explosive growth of the Internet has led to a surge of interest to understand and predict aggregate behavior of large number of people or agents, particularly when they are connected through an underlying network structure. Numerous Internet-based applications have emerged that are as diverse as getting micro-tasks executed through online labor markets (also known as crowd sourcing) to acquiring new skills through massively open online courses (also known as MOOCs). However, there has been a major inadequacy in existing studies with respect to evaluating the impact of strategic behavior of the agents participating in such networks, crowds, and classrooms. The primary focus of this doctoral work is to understand the equilibrium behaviour emerging from these real-world, strategic environments by blending ideas from the areas of game theory, graph theory, and optimization, to derive novel solutions to these new-age economic models. In particular, we investigate the following three research challenges:
(1) How do strategic agents form connections with one another? Will it ever happen that strategically stable networks are social welfare maximizing as well?
(2) How do we design mechanisms for eliciting truthful feedback about an object (perhaps a new product or service or person) from a crowd of strategic raters? What can we tell about these mechanisms when the raters are connected through a social network?
(3) How do we incentivize better participation of instructors and students in online edu-cation forums? Can we recommend optimal strategies to students and instructors to get the best out of these forums?
 
Contributor Narahari, Y
 
Date 2018-01-02T20:33:45Z
2018-01-02T20:33:45Z
2018-01-03
2014
 
Type Thesis
 
Identifier http://hdl.handle.net/2005/2955
http://etd.ncsi.iisc.ernet.in/abstracts/3817/G26689-Abs.pdf
 
Language en_US
 
Relation G26689