Embedded Files
Information Aggregation - Part 1 - Helge Klepper
What is so Difficult and Interesting About Aggregation? - Video
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 organizations can be viewed as networks and that the nodes (individuals) interact with other nodes by exchanging information and opinions.
This module highlights two approaches that try to 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 (just copy/paste the DOI into a search engine). 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.
Video and Slides
Exploration and Exploitation (Diffusion Models) - Video and Readings
You have probably read March’s (1991) exploration vs. exploitation model before. It is one of the most cited papers (more than 27,000 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.
Video and Slides
References
March, J. G. (1991). Exploration and Exploitation in Organizational Learning. Organization Science, 2(1), 71-87. doi: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
Wisdom of Crowds Models and Social Influence - Video and Readings
The second group of models we investigate is the wisdom of crowds models. First “discovered” by Francis Galton in the early 20th century, he found that groups are very good at estimating, often even better than the best individual in this group. In this chapter we highlight why this is the case and whether social influence is good or bad for aggregating information.
Video and Slides
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
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.
Other Models Related to Aggregation - Video and Readings
Thorbjørn Knudsen will cover a very important set of models regarding aggregation: information economics (models). But even beyond those models and the ones 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.
Video and Slides
Further Readings
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: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
For any questions, feedback, complaints, or anything else, please email me at klapper@rsm.nl
Information Aggregation - Part 2 - Thorbjørn Knudsen
Contents and Take-Away
This module highlights models of organizational decisions. The basic premise in this line of research is that the organization has a fundamental role in aggregating choice functions, which characterize the organizational members’ ability to pass judgment. A choice function, also known as a screening function, maps an individual’s assessment of the consequences of a choice onto an appropriate action, e.g., screening applicants for credit (Christensen & Knudsen 2020), screening stocks for mutual fund investment (Csaszar 2012), or screening crowdsourced ideas for improving a firm’s operations (Reitzig & Maciejovsky 2015). Given the agents’ inherent ability, organization design aims to reduce random error in choice (discrimination)—and, at the same time, eliminate systemic error (bias).
The core construct in models of organizational decisions is the screening function, which represents a noisy and possibly biased signal about quality.
These models are anchored in a framework for analysis of aggregation, i.e., combining screening functions to reach collective decisions.
The framework captures a broad range of empirical phenomena of interest to organizational designers, including collective decisions, communication structures, and voting.
Take away: You will get an overview of this class of models and their relevance—and you will get hands on experience with a toolbox that allows you to produce state-of-the-art models of organizational decisions.
Key Terms
Organizational decision: aggregation of recommendations made by several people (e.g., executive committee, mgmt. team).
Bias: refers to statistical bias, defined in terms of deviation from optimal reservation level.
Reservation level: the organization’s best estimate of a threshold above which net gains are positive.
Discrimination: uncertainty about actual value of a proposal (how noisy an evaluation is).
Endogenous adaptation: evaluators adapt bias and/ or discrimination conditional on situation (type of structure).
Background
von Neumann’s (1952) problem: Can reliable computing circuits be constructed by the redundant use of unreliable components?
Answer: Yes, in some cases.
Moore & Shannon (1956): Can electrical circuits be built which are arbitrarily reliable, regardless of how unreliable the original relays are?
Answer (to von Neumann): Yes! This answer has strong implications, e.g., for theory of computing.
Sah & Stiglitz (1985, 1986, 1988, 1991): Developed organization design using basic human version of Moore & Shannon’s structures (2-person hierarchy and polyarchy). Did not explore more complex structures, or limit theorems.
Christensen & Knudsen (2002, 2010): Do Moore & Shannon’s results generally hold for human organizations?
Answer: Yes! Strong implications for design of collective decisions.
References
Background
Moore EF, Shannon CE (1956a) Reliable circuits using less reliable relays, part I. J. Franklin Inst. 262(September): 191–208.
Moore EF, Shannon CE (1956b) Reliable circuits using less reliable relays, part II. J. Franklin Inst. 262(October): 281–297.
Sah RK, Stiglitz JE (1985) Human fallibility and economic organization. American Economic Review. 75(2): 292–297.
Sah RK, Stiglitz JE (1986) The architecture of economic systems: Hierarchies and polyarchies. The American Economic Review. 76(4): 716-727.
Sah RK, Stiglitz JE (1988) Committees, hierarchies and polyarchies. The Economic Journal. 98(391): 451- 470.
Sah RK, Stiglitz JE (1991) The Quality of Managers in Centralized Versus Decentralized Organizations, The Quarterly Journal of Economics, 106(1): 289-295.
Sah, RK (1991) Fallibility in Human Organizations and Political Systems. Journal of Economic Perspectives, 1991, 5(2), 67-88.
Stiglitz JE (2002) Information and the Change in the Paradigm in Economics, The American Economic Review, 92(3): 460-501.
Models of Organizational Decisions in Management
Joseph J, Gaba V (2020) Organizational Structure, Information Processing, and Decision-Making: A Retrospective and Road Map for Research. Academy of Management Annals. 14(1): 267–302.
Modeling
Christensen M, Knudsen T (2002) The architecture of economic organization: Toward a general framework. (Mimeo, University of Southern Denmark: Odense, Denmark).
Christensen M, Knudsen T (2008) Entry and exit decisions in flexible teams, Journal of International Business Studies, 39, 1278–1292.
Christensen M, Knudsen T (2010) Design of Decision-Making Organizations. Management Science. 56(1): 71-89.
Christensen M, Knudsen T (2020) Division of Roles and Endogenous Specialization. Industrial and Corporate Change. 29 (1): 105-124.
Csaszar FA (2013) An efficient frontier in organization design: Organizational structure as a determinant of exploration and exploitation. Organization Science. 24(4): 1083-1101
Csaszar FA, Eggers JP (2013) Organizational decision making: An information aggregation view. Management Science. 59(10): 2257-2277.
Knudsen T, Levinthal DA (2007) Two faces of search: Alternative generation and alternative evaluation. Organization Science. 18(1):39-54.
Empirics - Large n
Csaszar FA (2012) Organizational structure as a determinant of performance: Evidence from mutual funds. Strategic Management Journal. 33(6): 611–632.
Reitzig M, Maciejovsky B (2015) Corporate hierarchy and vertical information flow inside the firm—a behavioral view. Strategic Management Journal. 36(13): 1979-1999.
Empirics - Laboratory Studies
Christensen M, Dahl CM, Knudsen T, Warglien M (2021). Context and Aggregation: An Experimental Study of Bias and Discrimination in Organizational Decisions. Organization Science, Special issue: Experiments in Organizational Theory (forthcoming).
Piezunka H & Schilke O (2021). The effect of organizational aggregation structures on individuals’ voting behavior: An experimental investigation. Working paper.
Code for the session is linked here:
https://www.dropbox.com/sh/qf4ttd6k0uzbbq2/AADtMlri6HdcV3_GnMufukKTa?dl=0
Slides for the session are linked here:
https://www.dropbox.com/s/px4yz1eyyncbejs/TOM_summer_school_2021.pdf?dl=0
For any questions, feedback, complaints, or anything else, please email me at tok@sam.sdu.dk
Page updated
Google Sites
Report abuse