Master
2024/2025
Networks: Theory and Applications
Type:
Elective course (Economics and Economic Policy)
Area of studies:
Economics
Delivered by:
Department of Theoretical Economics
Where:
Faculty of Economic Sciences
When:
1 year, 2, 3 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Mariya Teteryatnikova
Master’s programme:
Economics and Economic policy
Language:
English
ECTS credits:
3
Course Syllabus
Abstract
Networks are ubiquitous in our modern society. The World Wide Web that links us to the rest of the world is the most visible example. But it is only one of many networks in which we are situated. Our social life is organized around networks of friends and colleagues. These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through weak but important ties, to everybody else. Economic and financial markets also look much more like networks than anonymous marketplaces. Firms interact with the same suppliers and customers and use web-like supply chains. Financial linkages, both among banks and between consumers, companies, and banks, also form a network over which funds flow and risks are shared; systemic risk in financial markets often results from the counter-party risks created within this financial network. Food chains, interacting biological systems and the spread and containment of epidemics like Covid-19 are some of the other natural and social phenomena that exhibit a marked networked structure. This course will introduce the tools for the study of networks. It will show how certain common principles permeate the functioning of these diverse networks and how the same issues related to robustness, fragility, and interlinkages arise in many different types of networks. More specifically, the course will be split into two parts. The first, larger part is a series of lectures on the analytic modelling of networks and games played on networks. The second part is presentations by students of the selected papers (the list of topics will be discussed in class). The first part of the course will begin with an overview of social and economic networks, and the embeddedness of economic activity. We then will examine how to describe and measure networks and discuss some empirical observations about network structure. Next, we will examine models of network formation: random network models and strategic formation models. After that we will take a long look at models of diffusion through networks, learning on networks, as well as models of how networks impact behavior (games played on networks and networked markets).
Learning Objectives
- The objective of the course is to acquaint the students with the field of network economics, demonstrate its importance and usefulness for studying many economic, social and other phenomena and to provide the students with the main tools for describing, measuring and analysing networks. After taking the course the students should be able to use and modify the canonical models of network theory to measure the key characteristics of networks, to derive the predictions about the spread of epidemics, fashion and opinions, to analyse the local network effects on choices and behaviour, to describe stable and efficient networks using a number of theoretical definitions, as well as understand the advantages and disadvantages of using random versus stratagic approach to network formation. The knolwledge and skills acquired by the end of course should facilitate better understanding of many real-world interactions and phenomena. They should also be sufficient to allow students reading and presenting scientific papers on economic and social networks and using the network analysis in their own research projects.
Expected Learning Outcomes
- Understand the concept of networks, their importance in economics and sociology.
- Know the definitions of different measures and properties of networks, be able to calculate these measures in network examples
- Know the features of random and strategic network formation, understand the difference between the two.
- Know what Erdos-Renyi (Poisson) networks are, describe small worlds and scale free network models.
- Know the definitions and be able to identify in examples pairwise stable, Nash stable, strongly stable networks and compare those with efficient networks. Know the pros and cons of each concept.
- Understand and be able to apply in examples DeGroot learning model, know conditions for reaching consensus, understand the concept of wisdom of crowds
- Be able to describe and derive main results in Bass model, SIR and SIS models of diffusion.
- Understand main features of the behavioral SIR model and ideas of seeding and targeting.
- Understand the definition of strategic complements and substitutes. Know the meaning of tipping points and local network effects. Be able to derive the key-player result in a quadratic utility model. Be able to solve linear best-response games.
- Be able to derive the key-player result in a quadratic utility model. Be able to solve linear best-response games.
- Be able to define production networks model and explain the idea and main result of Leontief input-out analysis.
- Know the definition of games with incomplete information, the notion of beliefs and Bayesian equilibrium. Be able to find Bayesian equilibrium in simple games of incomplete information.
- Understand the model of herding and information cascades.
- Be able to give examples of networks in the real world and name the types of questions we can address using network analysis/network data.
Course Contents
- Introduction to social and economic networrks
- Describing and measuring networks
- Models of network formation
- Social learning on networks 1: De Groot learning
- Diffusion through networks and societies
- Strategic aspects of diffusion and contagion
- Network effects
- Production networks
- Incomplete information and social learning on networks 2: Bayesian learning
- Presentations of selected papers by students
Interim Assessment
- 2024/2025 2nd module0.4 * Final test + 0.3 * Home assignments + 0.3 * Presentation
