July 8 Information Aggregation

Thorbjørn Knudsen, Professor of Strategic Organization, Frankfurt School of Finance and Management

Helge Klapper, Assistant Professor in Strategy, Daniels School of Business, Purdue University

Synchronous session info:
Date: July 8, 2025
Start Time: 14:00 UTC (10:00 EST / 16:00 CET)
End Time: 15:20 UTC
Link: https://georgetown.zoom.us/j/99313919043?pwd=huivLfbwQDaS6ooRlhbTTm28bqQn9M.1
Meeting ID: 993 1391 9043
Passcode: 957 810

More is different. - Paul Anderson

A central question of organization theory is: how can we explain what an organization is doing based on the beliefs, preferences, and actions of individuals? A core idea of this module (and the following module by Thorbjørn Knudsen) is that (information) aggregation is an important phenomenon for organization theory.

This module highlights approaches that describe and model how such an information aggregation process could look like. The first chapter covers one of the most famous (aggregation) models in organization research: Exploration and Exploitation by March (1991). The second chapter discusses wisdom of crowds as a different (modeling) approach to aggregation.

All chapters have some references attached to them which I discuss in the video. There is also further reading (which you are not required to read). When possible, I give links to codes of relevant models.

As an add-on, in the last chapter I give an overview of related models and research from adjacent fields that also tackle in some form or shape information aggregation problems.

Introduction

Here is why aggregation problems are so interesting and important.

Illustration of the Schelling model: Parable of the Polygons

References

Schelling, T. C. (1969). Models of segregation. The American economic review, 59(2), 488-493.

Assumptions

This video explains what the core components of an aggregation model are

Wisdom of crowds

This stream of research on information aggregation looks at crowds, groups with no interaction. Despite this lack of interaction, large groups of people can give pretty good estimates. In the video I will explain why and when.

References

Page, S. E. (2010). Diversity and complexity. Princeton, NJ: Princeton University Press.

Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations. New York, NY, US: Doubleday & Co.

social interaction and structure

References

Becker, J., Brackbill, D., & Centola, D. (2017). Network dynamics of social influence in the wisdom of crowds. Proceedings of the National Academy of Sciences, 114(26), E5070-E5076. doi:10.1073/pnas.1615978114

Additional Reading

Asch, S. E. (1956). Studies of independence and conformity: A minority of one against a unanimous majority. Psychological Monographs: General and Applied, 70(9), 1-70. doi:10.1037/h0093718

Janis, I. L. (1972). Victims of groupthink: A psychological study of foreign-policy decisions and fiascoes. Oxford, UK: Houghton Mifflin.

Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011). How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences, 108(22), 9020-9025. doi:10.1073/pnas.1008636108

Available Code

https://github.com/joshua-a-becker/wisdom-of-partisan-crowds

Exploration and exploitation

You have probably read March’s (1991) exploration vs. exploitation model before. It is one of the most cited papers (more than 35,000 citations on Google Scholar!) in organization research. This chapter shows how March’s model and its descendants describe an aggregation process from individual beliefs to aggregated outcome.

References

March, J. G. (1991). Exploration and Exploitation in Organizational Learning. Organization Science, 2(1), 71-87. doi:10.1287/orsc.2.1.71

Fang, C., Lee, J., & Schilling, M. A. (2010). Balancing exploration and exploitation through structural design: The isolation of subgroups and organizational learning. Organization Science, 21(3), 625-642. doi:10.1287/orsc.1090.0468

Additional Reading

Schilling, M. A., & Fang, C. (2014). When hubs forget, lie, and play favorites: Interpersonal network structure, information distortion, and organizational learning. Strategic Management Journal, 35(7), 974-994. doi.org/10.1002/smj.2142

Lazer, D., & Friedman, A. (2007). The network structure of exploration and exploitation. Administrative Science Quarterly, 52(4), 667-694. doi:10.2189/asqu.52.4.667

Available Code

https://github.com/Mac13kW/March_1991_Exploration_and_Exploitation

https://github.com/Mac13kW/March_1991_Original

adjacent models

Beyond the models covered in prior chapters, many different streams of research focus on aggregation. Economics, sociology, political science, psychology, and many more are interested in explaining a group outcome based on individuals’ beliefs, information, and preferences. This short chapter summarizes a few of them. 

References

Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A Theory of fads, fashion, custom, and cultural-change as informational cascades. Journal of Political Economy, 100(5), 992-1026. doi: 10.1086/261849

Davis, J. H. (1973). Group Decision and Social Interaction: A theory of Social Decision Schemes. Psychological Review, 80(2), 97-125. doi:10.1037/h0033951

Friedkin, N. E., & Johnsen, E. C. (2011). Social influence network theory : a sociological examination of small group dynamics. New York, US: Cambridge University Press.

Stasser, G. (1988). Computer simulation as a research tool: The DISCUSS model of group decision making. Journal of Experimental Social Psychology, 24(5), 393-422. doi:https://doi.org/10.1016/0022-1031(88)90028-5

Stasser, G., & Titus, W. (1985). Pooling of unshared information in group decision-making - Biased information sampling during discussion. Journal of Personality and Social Psychology, 48(6), 1467-1478. doi:10.1037//0022-3514.48.6.1467

Organizational Decisions

Click here to access the module manual.