Informed Choices, Inclusive Voices: Epistemic Journeys in Democratic Decision Making

We will explore the idea that democracies tend to produce good decisions, also known as epistemic democracy. According to this view, the value of democratic decision procedures lies not only in the fact that they are fair, but also in the fact that they can be good at approximating the truth, when there is a truth to be discovered. We will explore the mechanisms that make democracies efficient in this sense: laws of large numbers, diversity, deliberation. In doing so we will touch on celebrated results such as the Condorcet Jury Theorem, and get glimpse of ways in which things can go wrong through misinformation, polarization or the systematic suppression of certain voices.


Week 1 (April 15, 2024)

We started by introducing ourselves, followed by a breakdown of the logistics of the course. [pdf]

This was followed by a lecture on the general idea of the wisdom of crowds. [pdf]

Week 2 (April 22, 2024)

A lecture on the Condorcet Jury theorem, the cornerstone result for the wisdom of crowds. [pdf]

Week 3 (April 29, 2024)

A lecture on what happens to the conclusions of the Condorcet Jury Theorem when its assumptions are relaxed. [pdf]

Week 4 (May 6, 2024)

A lecture on social learning and information cascades. [pdf]

Week 5 (May 13, 2024)

Lecture canceled due to illness of speaker.

Week 6 (May 20, 2024)

Public holiday, no lecture.

Week 7 (May 27, 2024)

A discussion on the main tenets of epistemic democracy, through the lens of two prominent political scientists: Cohen (1986) and Anderson (2006).

Week 8 (June 3, 2024)

Julian presented the (in)famous ‘Diversity Trumps Ability’ result from Hong & Page (2004), and Abigail Thompson’s criticism of it in Thompson (2014).

Week 9 (June 10, 2024)

A discussion on deliberation and its epistemic virtues: Dryzek et al (2019), and Chapter 4 of Landemore (2013).


The Wisdom of Crowds Under the Microscope

  1. Surowiecki, J. (2005). The Wisdom of Crowds. Anchor.
  2. Lorenz, J., Rauhut, H., Schweitzer, F., Helbing, D. (2011) How social influence can undermine the wisdom of crowd effect. PNAS, 108(22): 9020–9025.
  3. Landemore, H., & Elster, J. (2012). Collective Wisdom: Principles and Mechanisms. Cambridge University Press.
  4. Becker, J., Brackbill, D., & Centola, D. (2017). Network dynamics of social influence in the wisdom of crowds. PNAS, 114(26).

Jury Theorems

  1. Grofman, B., Owen, G., & Feld, S. L. (1983). Thirteen theorems in search of the truth. Theory and Decision, 15(3), 261–278.
  2. Ladha, K. K. (1992). The Condorcet Jury Theorem, Free Speech, and Correlated Votes. American Journal of Political Science, 36(3), 617–634.
  3. Estlund, D. M. (1994). Opinion leaders, independence, and Condorcet’s Jury Theorem. Theory and Decision, 36(2), 131–162.
  4. Austen-Smith, D., & Banks, J. S. (1996). Information Aggregation, Rationality, and the Condorcet Jury Theorem. The American Political Science Review, 90(1), 34–45.
  5. Paroush, J. (1997). Stay away from fair coins: A Condorcet jury theorem. Social Choice and Welfare, 15(1), 15–20.
  6. Ben-Yashar, R., & Paroush, J. (2000). A nonasymptotic Condorcet jury theorem. Social Choice and Welfare, 17(2), 189–199.
  7. List, C., & Goodin, R. E. (2001). Epistemic Democracy: Generalizing the Condorcet Jury Theorem. The Journal of Political Philosophy, 9(3), 277–306.
  8. Dietrich, F., & List, C. (2004). A Model of Jury Decisions where all Jurors have the same Evidence. Synthese, 142(2), 175–202.
  9. Dietrich, F. (2008). The Premises of Condorcet’s Jury Theorem Are Not Simultaneously Justified. Episteme, 5(1), 56–73.
  10. McCannon, B. C. (2011). Jury size in classical Athens: An application of the Condorcet jury theorem. Kyklos: International Review for Social Sciences, 64(1), 106–121.
  11. McCannon, B. C., & Walker, P. (2016). Endogenous competence and a limit to the Condorcet Jury Theorem. Public Choice, 169(1), 1–18.
  12. Dietrich, F., & Spiekermann, K. (2022). Jury Theorems. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Summer 2022). Metaphysics Research Lab, Stanford University.
  13. Böttcher, L., & Kernell, G. (2022). Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems. Collective Intelligence, 1(2).

Epistemic Democracy and Its Discontents

  1. Cohen, J. (1986). An Epistemic Conception of Democracy. Ethics, 97(1), 26–38.
  2. Anderson, E. (2006). The epistemology of democracy. Episteme, 3(1–2), 8–22
  3. Caplan, B. (2007). The Myth of the Rational Voter: Why Democracies Choose Bad Policies. Princeton University Press.
  4. Elster, J. (2013). Securities Against Misrule: Juries, Assemblies, Elections. Cambridge University Press.
  5. Schwartzberg, M. (2015). Epistemic Democracy and its Challenges. Annual Review of Political Science, 18: 187–203.
  6. Brennan, J. (2016). Against Democracy. Princeton University Press.
  7. Goodin, R. E., & Spiekermann, K. (2018). An Epistemic Theory of Democracy. Oxford University Press.
  8. Landemore, H. (2020). Open Democracy: Reinventing Popular Rule for the Twenty-First Century. Princeton University Press.
  9. Brennan, J., & Landemore, H. (2022). Debating Democracy: Do We Need More Or Less? Oxford University Press.


  1. Hong, L., & Page, S. E. (2004). Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences, 101(46), 16385–16389.
  2. Bohman, J. (2006). Deliberative Democracy and the Epistemic Benefits of Diversity. Episteme; Rivista Critica Di Storia Delle Scienze Mediche E Biologiche, 3(3), 175–191.
  3. Page, S. E. (2007). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press.
  4. Thompson, A. (2014). Does Diversity Trump Ability?. Notices of the AMS, 61(9).
  5. Kuehn, D. (2017). Diversity, Ability, and Democracy: A Note on Thompson’s Challenge to Hong and Page. Critical Review , 29(1), 72–87.


  1. Cohen, J. (1989). Deliberation and Democratic Legitimacy. In The Good Polity: Normative Analysis of the State, ed. A. Hamlin and P. Pettit (Oxford: Blackwell), 17–34.
  2. Gutmann, A. and Thompson, D. (2004). Why Deliberative Democracy? Princeton University Press.
  3. Fishkin, J. (2009). When the People Speak: Deliberative Democracy and Public Consultation. Oxford University Press.
  4. Goeree, J. K. and Yariv, L. (2011). An Experimental Study of Collective Deliberation. Econometrica, 79: 893–921.
  5. Parkinson, J. and Mansbridge, J. (2012). Deliberative Systems: Deliberative Democracy at the Large Scale. Cambridge University Press.
  6. Mercier, H., & Landemore, H. (2012). Reasoning is for arguing: Understanding the successes and failures of deliberation. Political Psychology, 33(2), 243–258.
  7. Ingham, Sean. (2012). Disagreement and Epistemic Arguments for Democracy. Politics, Philosophy & Economics 12(2) (2013): 135–15.
  8. Landemore, H. (2013). Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many. Princeton University Press.
  9. Landemore, H. (2017). Beyond the Fact of Disagreement? The Epistemic Turn in Deliberative Democracy. Social Epistemology, 31(3), 277–295.
  10. Goodin, R. E. (2017). The epistemic benefits of deliberative democracy. Policy Sciences, 50(3), 351–366.
  11. Estlund, D. M., & Landemore, H. (2018). The epistemic value of democratic deliberation. The Oxford Handbook of Deliberative Democracy.
  12. Dryzek, J. S., Bächtiger, A., Chambers, S., Cohen, J., Druckman, J. N., Felicetti, A., Fishkin, J. S., Farrell, D. M., Fung, A., Gutmann, A., Landemore, H., Mansbridge, J., Marien, S., Neblo, M. A., Niemeyer, S., Setälä, M., Slothuus, R., Suiter, J., Thompson, D., & Warren, M. E. (2019). The crisis of democracy and the science of deliberation. Science, 363(6432), 1144–1146.

Social Learning and Cascades

  1. Banerjee, A. (1992). A Simple Model of Herd Behavior. The Quarterly Journal of Economics, 107(3), 797–817.
  2. 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.
  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.
  4. Golub, B., & Jackson, M. O. (2010). Naïve Learning in Social Networks and the Wisdom of Crowds. American Economic Journal: Microeconomics, 2(1), 112–149.
  5. Bikhchandani, S., Hirshleifer, D., Tamuz, O., & Welch, I. (2021). Information Cascades and Social Learning (No. 28887). National Bureau of Economic Research.

Prediction Markets

  1. Wolfers, J., & Zitzewitz, E. (2004). Prediction Markets. The Journal of Economic Perspectives, 18(2), 107–126.
  2. Arrow, K. J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J. O., Levmore, S., Litan, R., Milgrom, P., Nelson, F. D., Neumann, G. R., Ottaviani, M., Schelling, T. C., Shiller, R. J., Smith, V. L., Snowberg, E., Sunstein, C. R., Tetlock, P. C., Tetlock, P. E., … Zitzewitz, E. (2008). The Promise of Prediction Markets. Science, 320(5878), 877–878.


  1. Landemore, H. (2013). Deliberation, cognitive diversity, and democratic inclusiveness: an epistemic argument for the random selection of representatives. Synthese, 190, 1209–1231.
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