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Networks
Economics 2040 / Sociology 2090 / Computer Science 2850 / Information Science 2040
Cornell University, Fall 2013
Mon-Wed-Fri 11:15-12:05 Statler Auditorium
The course is designed at the introductory undergraduate level with no formal prerequisites; it satisfies the Arts & Sciences Social and Behavioral Analysis (SBA) distribution and the Engineering Liberal Studies (SBA group) distribution.
See below for more information, including the list of handouts, the outline of topics, the schedule of office hours, and the Blackboard and Piazza site.
Mon 5:30-6:30
Upson 328B, Bay D
Favian or Matt
Tue 1-2
Upson 328B, Bay C
Nicole
Tue 3-5pm
review session, Statler
Professor Tardos
Wed 10-11am
Upson 328B, Bay A
Holly or Yiwei
Wed 4:30-5:30pm
Upson 328B, Bay C
Janice or Zhuo
Thur 1:30-2:30pm
Upson 328B, Bay A
Amanda or Daniel
Thur 2:30-4pm
432 Uris Hall
Professor Easley
Thur 4:30-5:30pm
Upson 328B, Bay A
Caitlin or Rebecca
At the Blackboard
site, you can log in with your Cornell NetID to
find information about your course grades and also to upload solutions to
homework.
Solutions to all problem sets, as well as the final paper, must be submitted through the Blackboard, by the start of class on the days they are due. This means that you should write these up as PDF files. Also, you should check the Blackboard site at the start of the semester to make sure that you are able to log in. Please let us know if you experience any difficulties with this.
(1) Graph Theory and Social Networks
The course begins with a discussion of some of the general
properties of networks. It develops this through examples from social network
analysis, including the famous ``strength of weak ties'' hypothesis in
sociology, and it connects these themes to recent large-scale empirical studies
of on-line social networks.
Reading
Optional further reading:
(This
is Granovetter's original paper describing his work
covered in Chapter 3.)
(2) Game Theory
Since most network studies require us to consider not only
the structure of a network but also the behavior of the agents that inhabit it,
a second important set of techniques comes from game theory. This too is
introduced in the context of examples, including the design of auctions and
some ``paradoxical'' phenomena surrounding network traffic congestion.
Reading
Optional further reading:
(This
is the paper that studies penalty kicks in soccer, as discussed in Section
6.8.)
(3) Markets and Strategic Interaction on Networks
The interactions among participants in a market can
naturally be viewed as a phenomenon taking place in a network, and in fact
network models provide valuable insights into how an individual's position in
the network structure can translate into economic outcomes. This provides a
natural illustration of how graph theory and game theory can come together in
the development of models for network behavior. Our discussion in this part of
the course also builds on a large body of sociological work using human-subject
experiments to study negotiation and power in networked settings.
Reading
(4) Information Networks and the World-Wide Web
The Internet and the Web of course are central to the argument
that computing and information is becoming increasingly networked. Building on
the earlier course topics, we describe why it is useful to model the Web as a
network, discussing how search engines make use of link information for
ranking, how they use ideas related to power and centrality in social networks,
and how they have implemented network-based matching markets for sellling advertising.
Reading
(5) Network Dynamics: Population Models
Networks are powerful conduits for the flow of information,
opinions, beliefs, innovations, and technologies. We begin by considering how
these processes operate at the level of populations, when we can't necessarily
observe the network itself, but only its effects on aggregate behavior. As part
of this, we consider phenomena including information cascades, "tipping
points" in the success of products with network effects, and the
distribution of popularity.
Reading
(6) Network Dynamics: Structural Models
We continue our exploration
of how things flow through networks, focusing on what we can learn from details
of the network structure itself. Here we study how both behaviors and diseases
can spread through a social network, and also some of the network phenomena
that underpin the "six degrees of separation" effect.
Reading
Optional further reading:
(This is the paper that
discusses the role of knowledge in facilitating collective action in social
networks, as described in Section 19.6.)
(7) Institutions and Aggregate Behavior
Finally, a perspective based on networks can provide novel
insights into the structure of social institutions, and into basic policy
questions in many areas. We illustrate this theme with examples based on
markets, voting theory, and property rights.
Reading
Almost no knowledge of specific mathematical content is
assumed, other than some basic probability (random variables, expectation,
independence, and conditional probability), which we will briefly review when
it first arises.
The main goal of the course will be to build mathematical models of the processes that take place in networks. As such, students will be expected to interpret and work with mathematical models as they come up the course; at the same time, students should also think about how to relate these models to phenomena at a qualitative level.
You are expected to maintain the utmost level of academic
integrity in the course. Any violation of the code of academic integrity will
be penalized severely.
You are allowed to collaborate on the homework to the extent of formulating ideas as a group. However, you must write up the solutions to each problem set completely on your own, and understand what you are writing. You must also list the names of everyone that you discussed the problem set with.
Collaboration is not allowed on the other parts of the coursework.
Finally, plagiarism deserves special mention here. Including text from other sources in written assignments without quoting it and providing a proper citation constitutes plagiarism, and it is a serious form of academic misconduct. This includes cases in which no full sentence has been copied from the original source, but large amounts of text have been closely paraphrased without proper attribution. To get a better sense for what is allowed, it is highly recommended that you consult the guidelines maintained by Cornell on this topic. It is also worth noting that search engines have made plagiarism much easier to detect. This is a very serious issue; instances of plagiarism will very likely result in failing the course.