Course Overview

Our every day life is shaped by social and economic networks: the
information we have, how we learn and form opinions, the diseases that
hurt us, our job decisions, the prices we charge or pay and even how
we make friends and enjoy life. In this course we provide advanced
materials on some of the main themes in the theory of networks that
will help us understand and deal will all those
questions, and more. Studying networks is fun and open minded as it
lies at the cross roads of different fields of science. We present and
discuss in class theoretical models from economics, sociology, maths,
physics, statistics and computer science. Last but not least, we deal
with the empirics of networks and learn how to make regressions and
test hypotheses on such data, something that becomes increasingly
important in the social networks / internet industry.

Learning Achievement

Competence

Course prerequisites

This course builds on IREF M1 class UE 2.1.6 "Analyse des réseaux". I
thus assume you are familiar to the following notions: basic graph
notations, Poisson random networks, preferential attachment in growing
networks, Milgram experiment, clustering coefficient, small worlds à
la Watts & Strogatz, network centrality notions (including Bonacich,
Katz and Page Rank), degree distribu-
tion. Please contact me as soon as possible if, for whatever reason,
you did not attended this class: nicolas.carayol@u-bordeaux.fr

Grading Philosophy

Students may match in pairs to realize a mini research project with
will bring three grades: Data treatments (40%), Reasoning (40%),
Presentation (20%). A specific dataset will be provided to each group
with a mission" to be fulfiled.

Course schedule

1. NETWORK DIFFUSION
The Bass model (without networks)
Percolation
Random networks
Epidemics (SIR and SIS models)

2. LEARNING IN NETWORKS

The De Groot model
Bayesian learning and informational cascades
The wisdom and folly of crowds

Optimally extracting information from crowds

3. GAMES ON NETWORKS
Coordination games on networks
Graphical games
Games with strategic substitutes
Games with strategic complements
Katz and Page rank centrality
Pricing in networks
Incomplete information games and the friendship paradox

4. STRATEGIC NETWORK FORMATION

Networks formation games and equilibrium notions
Games with positive network externalities
Games with negative network externalities
Dynamic stochastic network formation

5. NETWORK DATA AND IDENTIFICATION

Network Data
Identification of peer effects on networks

Peer effects and the reflection problem

Policy interventions on network structure

Policy interventions and the social multiplier
Estimating network formation

Course type

- Lectures and interactive learning. - Data base exploitation.

Online Course Requirement

Instructor

Other information

1. Matthew Jackson, 2019, _The Human Network_, Pantheon Books: New
York.
2. Matthew Jackson, 2008, _Social and Economic Networks_, Princeton
University
Press : Princeton and Oxford.
3. David Easley and Jon Kleinberg, 2010,_ Networks, Crowds, and
Markets_: _Reasoning about a Highly Connected World_, Cambridge
University Press : Cambridge, New York. free pdf version
4. Mark E.J. Newmann, 2010, _Networks, An Introduction_, Oxford
University

Press: Oxford, New York.

The easiest to read and most recent book is (1). You can easily buy it
and read it on your own (starting before rst class meeting would be
great). The most important reference for this class is (2). (3) has
some nice chapters and is also a great book. (4) is the best reference
for a very complete introduction of the physics literature.Duration: One semester

Language of instruction: English
Mode of delivery: Face-to-face teaching

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