Department of Computer Science and Automation
Ph.D. Thesis Defense

Speaker             :  Mr. Swaprava Nath
Title               :  Mechanism Design for Strategic Crowdsourcing

Faculty Advisor     :  Prof.  Y. Narahari

Date                :  Tuesday, December 17, 2013
Time                :  11:30 AM
Venue               :  CSA Multimedia Class (Room No. 252, First Floor)

Abstract

This thesis looks into the economics of crowdsourcing using
game theoretic modeling. The art of aggregating information and expertise
from a diverse population has been in practice since a long time.

The Internet and the revolution in communication and computational
technologies has made this task easier and given birth to a new era of
online resource aggregation, which is now popularly referred to as
crowdsourcing. Two important
features of crowdsourcing are: (a) crowdsourcing
is always human driven, hence the participants are rational and intelligent
and they experience a payoff in some form through their participation, and
(b) the participants are connected over a social network. To understand

the behavior and the outcome of such a strategic crowd, we need to
understand the economics of a crowdsourcing network. In the thesis,
we have considered the following three major facets of the crowdsourcing
problem.

(i) Elicitation of the true qualities of the crowd workers:
as the crowd is often unstructured and unknown to the designer, it is
important to determine if the crowdsourced job is indeed performed at the
highest quality.

(ii) Resource critical task execution:  due to the diverse geographical,
cultural, socio-economic reasons, crowdsourcing entails certain
manipulations that are unusual in the classical theory. The design
has to
be robust enough to handle fake identities or information provided
by the crowd.

(iii) Improving the productivity of the crowdsourcing network: as the
designer's goal is to maximize some measurable output of the crowdsourcing

system, an interesting question is how one can design the network and/or
the incentive scheme so that the system performs at the optimal level
considering the strategic nature of the individuals.

In the thesis, we provide novel solutions to all the questions above

using game theoretic modeling and mechanism design innovations. Our
investigation helps in understanding certain limits of achievability,
and provides design protocols in order to make crowdsourcing more
efficient.

ALL ARE WELCOME