review articles
DOI: 10.1145/2960403
Social computing benefits from mathematical
foundations, but research has barely
scratched the surface.
BY YILING CHEN, ARPITA GHOSH, MICHAEL KEARNS,
TIM ROUGHGARDEN, AND JENNIFER WORTMAN VAUGHAN
SOCIAL COMPUTING ENCOMPASSES the mechanisms
through which people interact with computational
systems: crowdsourcing systems, ranking and
recommendation systems, online prediction markets,
citizen science projects, and collaboratively edited
wikis, to name a few. These systems share the
common feature that humans are active participants,
making choices that determine the input to, and
therefore the output of, the system. The output of
these systems can be viewed as a joint computation
between machine and human, and can be richer than
what either could produce alone. The term social
computing is often used as a synonym for several
related areas, such as “human computation” and
subsets of “collective intelligence;” we use it in its
broadest sense to encompass all of these things.
Social computing is blossoming
into a rich research area of its own,
with contributions from diverse disciplines including computer science,
economics, and other social sciences.
The field spans everything from systems research directed at building
scalable platforms for new social computing applications to HCI research
directed toward user interface design,
from studies of incentive alignment in
online applications to behavioral experiments evaluating the performance
of specific systems, and from understanding online human social behavior to demonstrating new possibilities
of organized social interactions. Yet a
broad mathematical foundation for social computing is yet to be established,
with a plethora of under-explored opportunities for mathematical research
to impact social computing.
In many fields or subfields, mathematical theories have provided major
contributions toward real-world applications. These contributions often
come in the form of mathematical
models to address the closely-related
problems of analysis—why do existing systems exhibit the outcomes they
do?—and design—how can systems
be engineered to produce better outcomes? In computer science, mathematical research led to the development of commonly used practical
machine learning methods such as
Mathematical
Foundations
for Social
Computing
key insights
˽ The output of a social computing system
can be viewed as a joint computation
between humans and machines,
and can be richer than what either
can produce alone.
˽ Mathematical research has led to
innovations in social computing such as
crowdsourced democracy, prediction
markets, and fair division are all examples
of social computing applications.
˽ Social computing systems cannot
be properly modeled or analyzed
without accounting for the behavior
of their human components,
which requires a dialog between
theoretical, experimental, and
empirical research and across
disciplinary boundaries. I M
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