Networked Minds: Opinion Dynamics and Collective Intelligence in Social Networks
Our opinions are often formed, and changed, in response to the opinions of our peers. Discussion, deliberation, or imitation can be just as important as silent reflection in shaping what we believe. But can we trust such processes, when compounded across the whole of society, to deliver more accurate beliefs, or do they steer us away from the truth?
The course will focus on the interplay between opinion dynamics and the wisdom of crowds. We will look at the crucial, and sometimes overlooked, role of social networks in the way beliefs are formed and spread in a group. We will want to understand how consensus and polarization can arise in a network of agents, and under what conditions we can expect to see wisdom of the crowds.
The coursework will consist of weekly discussions based on the core texts cited in the bibliography. We will study both case-studies and formal models. For the latter, a prerequisite is some basic knowledge of algebra and probability theory.
Week 1. Intro
We kick things off by introducing ourselves, followed by a breakdown of the logistics of the course. We then get a first glimpse of the wisdom of crowds in a guessing game, and hear about the wisdom of the stock market and epistemic democracy.
Slides
Adrian. Logistics.
Adrian. Wise Crowds
Bonus
Surowiecki, J. (November 5, 2008). The power and the danger of online crowds. TED Talk.
Week 2. The Condorcet Jury Theorem…
We see the Condorcet Jury Theorem, the landmark result of the wisdom of crowds.
We learn about what the theorem says, and see the proof.
Slides
Adrian. The Condorcet Jury Theorem.
Week 3. … and Beyond
We see whether the insights of the Condorcet Jury Theorem survive when we relax some of the basic assumptions.
Slides
Adrian. Relaxing the Assumptions of The Condorcet Jury Theorem.
Week 4. The Optimal Voting Rule & Trade Routes in the Middle Ages
We learn about the optimal voting rule when competences are known: a weighted voting rule that assigns weights proportional to competences.
And then we segue into social networks, by seeing an example with trade routes in the Middle Ages.
Slides
Adrian. Weighted Voting Rules.
Adrian. Trade Routes in the Middle Ages.
Week 5. The Strength of Weak Ties
The celebrated strength of weak ties.
Reading
Granovetter, M. S. (1973). The Strength of Weak Ties. The American Journal of Sociology, 78(6), 1360–1380.
Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. Proceedings of the 21st International Conference on World Wide Web. WWW 2012: 21st World Wide Web Conference 2012, Lyon France.
Slides
Adrian. The Strength of Weak Ties.
Week 6. Reasoning About Social Networks
We start with a puzzle: how did the Medici become so important in 15th century Florence, especially when Cosimo de Medici, the leader of the family, was not a particularly leader? The answer, it has been argued, has something to do with networks. Along the way, we learn how to represent social networks and see some important statistics.
Reading
Jackson (2010). Chapter 2.
Slides
Adrian. Quantifying Networks.
Week 7. No Lecture Today
Whit Monday.
Week 8. Centrality Measures
We see three ways to measure the centrality of a node in a social network: degree centrality, betweenness centrality and eigenvector centrality. And we finally find out why the Medici were crucial in the network of elite 15th century Florentine families.
Reading
Jackson (2010). Chapter 2.
Slides
Adrian. Centrality Measures in Social Networks.
Week 9. No Lecture Today
Not feeling well, sorry!
Week 10. The DeGroot Model and Naive Wisdom
We learn about the DeGroot model, an influential model of opinion dynamics, and we study the conditions under which the group can converge to a true opinion.
Reading
Golub, B., & Jackson, M. O. (2010). Naïve Learning in Social Networks and the Wisdom of Crowds. American Economic Journal: Microeconomics, 2(1), 112–149.
Week 11. The Hegselmann-Krause Model
Reading
Hegselmann, R., Krause, U., & Others. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5(3).
Hegselmann, R., & Krause, U. (2006). Truth and Cognitive Division of Labour: First Steps Towards a Computer Aided Social Epistemology. Journal of Artificial Societies and Social Simulation, 9(3), 10.
Week 12. TBD
Reading
Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011). How social influence can undermine the wisdom of crowd effect. PNAS, 108(22), 9020–9025.
Mercier, H., & Claidière, N. (2022). Does discussion make crowds any wiser? Cognition, 222, 104912.
Week 13. Information Cascades and Polarization
The folly of crowds: information cascades and polarization.
Reading
Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. The Journal of Political Economy, 100(5), 992–1026.
Ideas for essays
Bibliography
- Granovetter, M. S. (1973). The Strength of Weak Ties. The American Journal of Sociology, 78(6), 1360–1380.
- Banerjee, A. (1992). A Simple Model of Herd Behavior. The Quarterly Journal of Economics, 107(3), 797–817.
- Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. The Journal of Political Economy, 100(5), 992–1026.
- Hegselmann, R., Krause, U., & Others. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5(3).
- Hegselmann, R., & Krause, U. (2006). Truth and Cognitive Division of Labour: First Steps Towards a Computer Aided Social Epistemology. Journal of Artificial Societies and Social Simulation, 9(3), 10.
- Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357(4), 370–379.
- Christakis, N. A., & Fowler, J. H. (2009). Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. Little Brown and Company.
- Easley, D., & Kleinberg, J. (2010). Networks, Crowds, and Markets. Cambridge University Press.
- Jackson, M. O. (2010). Social and Economic Networks. Princeton University Press.
- Golub, B., & Jackson, M. O. (2010). Naïve Learning in Social Networks and the Wisdom of Crowds. American Economic Journal: Microeconomics, 2(1), 112–149.
- Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011). How social influence can undermine the wisdom of crowd effect. PNAS, 108(22), 9020–9025.
- Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. Proceedings of the 21st International Conference on World Wide Web. WWW 2012: 21st World Wide Web Conference 2012, Lyon France.
- Jackson, M. O. (2019). The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors. Knopf Doubleday.