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.
Lectures
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.
Week 3. … and Beyond
We see whether the insights of the Condorcet Jury Theorem survive when we relax some of the basic assumptions.
Week 4. Social Networks: Why They Are Important
Some outstanding examples of the importance of social networks in our lives.
Readings:
Granovetter, M. S. (1973). The Strength of Weak Ties. The American Journal of Sociology, 78(6), 1360–1380.
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.
Week 5. Social Networks: What They Are
We learn how to represent social networks and some important statistics. We learn about centrality measures, and in particular network centrality.
Readings:
Jackson (2010). Chapter 2.
Week 6. Naive Learning and Wise Crowds
We learn about the DeGroot model of opinion dynamics and consensus to a true opinion.
Readings:
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 7. No Lecture Today
Whit Monday.
Week 8. Wisdom in the Lab
We examine some experiments on opinion dynamics.
Readings:
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 9. The Hegselmann-Krause model
Readings:
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 10. Information Cascades
The folly of crowds: information cascades.
Readings:
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.
Week 11. TBD
Week 12. TBD
Week 13. TBD
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.
- Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011). How social influence can undermine the wisdom of crowd effect. PNAS, 108(22), 9020–9025.
- Jackson, M. O. (2019). The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors. Knopf Doubleday.