Магистратура
2024/2025
Сети: теория и приложения
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Статус:
Курс по выбору (Экономика и экономическая политика)
Направление:
38.04.01. Экономика
Кто читает:
Департамент теоретической экономики
Где читается:
Факультет экономических наук
Когда читается:
1-й курс, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Тетерятникова Мария Александровна
Прогр. обучения:
Экономика и экономическая политика
Язык:
английский
Кредиты:
3
Course Syllabus
Abstract
Networks are ubiquitous in modern society. The World Wide Web, which 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 embedded. Our social lives are organised around networks of friends and colleagues that shape our information, influence our opinions, and mould our political attitudes, while also connecting us, often through weak but important ties, to the wider world. Economic and financial markets, too, resemble networks far more than anonymous marketplaces: firms interact repeatedly with the same suppliers and customers through web-like supply chains, and financial linkages among banks, consumers, and companies form a network over which funds flow and risks are shared. Systemic risk in financial markets often arises precisely from the counterparty exposures created within this network. Beyond economics, food chains, interacting biological systems, and the spread and containment of epidemics such as COVID-19 all exhibit a markedly networked structure. This course introduces the tools for studying networks. It shows how certain common principles permeate the functioning of these diverse systems and how the same issues of robustness, fragility, and interlinkage recur across very different types of networks. We begin with an overview of social and economic networks and the embeddedness of economic activity. We then examine how to describe and measure networks and discuss key empirical observations about their structure. Next, we turn to models of strategic network formation, diffusion through networks, opinion formation, games on networks in which structure influences behaviour, and models of networked markets.
Learning Objectives
- The course aims to introduce students to network economics, demonstrate its relevance for studying a wide range of economic and social phenomena, and equip them with the main tools for describing, measuring and analysing networks. Upon completion of the course, students should be able to apply and adapt canonical models of network theory to measure key characteristics of networks; derive predictions about the diffusion of behaviours and the formation of opinions; analyse how local network effects shape individual choices; and characterise stable and efficient networks using a range of theoretical concepts. The knowledge and skills acquired by the end of the course should enable students to understand real-world interactions more effectively, critically assess scientific research on economic and social networks, and apply 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.
- 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.
- Identify strategic complementarities and strategic substitutes in network games.
- Explain how network structure and individual location shape strategic behaviour in public goods game, peer effects, and research collaboration.
- Understand and be able to apply in examples DeGroot learning model, know conditions for reaching consensus, understand the concept of wisdom of crowds.
- Understand intuition behind main results in Calvó-Armengol and Jackson (2004).
- Explain how financial networks create interdependence among institutions.
- Distinguish between integration and diversification in financial networks, and explain how each affects the propagation of shocks through the system.
- Apply network-based reasoning to financial regulation and systemic risk, assessing how policymakers might monitor central institutions, limit exposures, or design interventions to reduce the risk of cascading failures.
- Describe the structure of production networks and explain how input-output linkages connect different firms and sectors within an economy, moving beyond the traditional view of isolated markets.
- Explain the "network origins of aggregate fluctuations", evaluating why idiosyncratic, firm- or sector-level shocks can generate significant macroeconomic volatility.
- Apply the Leontief inverse framework to mathematically map and calculate the direct and indirect multiplier effects of a shock to one sector across the entire economic system.
- Be able to describe and derive main results in the Bass model.
- Be able to solve the Morris contagion model.
- Know the literature results on optimal seeding.
- Be able to explain main features of the model of Calvó-Armengol and Jackson (2004).
Course Contents
- Introduction to social and economic networrks
- Describing and measuring networks
- Strategic network formation
- Games on networks
- Imitation and social influence in opinion formation: De Groot model and extensions
- Social networks in labour markets
- Financial networks and contagion
- Production networks and macroeconomic fluctuations
- Diffusion of innovations and behaviours in networks. Optimal seeding
Interim Assessment
- 2024/2025 2nd module0.3 * Home assignments + 0.3 * Presentation + 0.4 * Final test
Bibliography
Recommended Core Bibliography
- Connections : an introduction to the economics of networks, Goyal, S., 2007
- Social and economic networks, Jackson, M. O., 2008
Recommended Additional Bibliography
- A primer in game theory, Gibbons, R., 1992
- Game theory, Fudenberg, D., 1991
- Game theory, Fudenberg, D., 1996