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MODULE 1
MODULE 1
Helge Klapper, Assistant Professor in Strategy, Krannert School of Management, Purdue University
Link for synchronous sessions: https://hbs.zoom.us/j/97071216335?pwd=MUZRMFB0L2xXUDRxdU9nSHVMNWtYQT09
Meeting ID: 970 7121 6335
Password: tom
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 (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.
1. Introduction
1. Introduction
Here is why aggregation problems are so interesting and important.
Simple visualization of the Schelling model:
Schelling model paper:
http://www.stat.berkeley.edu/~aldous/157/Papers/Schelling_Seg_Models.pdf
2. Aggregation
2. Aggregation
This video explains what the core components of an aggregation model are:
3. Exploration Exploitation
3. Exploration Exploitation
You have probably read March’s (1991) exploration vs. exploitation model before. It is one of the most cited papers (more than 32,000 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
4. Wisdom of Crowds
4. 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
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.
Available Code
https://github.com/joshua-a-becker/wisdom-of-partisan-crowds
5. Adjacent Models
5. 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.
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: 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
MODULE 2
MODULE 2
Thorbjørn Knudsen, Professor of Strategic Organization, Frankfurt School of Finance & Management
Link for synchronous sessions: https://hbs.zoom.us/j/97071216335?pwd=MUZRMFB0L2xXUDRxdU9nSHVMNWtYQT09
Meeting ID: 970 7121 6335
Password: tom
Material
Material
Contents
Contents
This module highlights models of organizational decision-making processes. 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 of individual actors to reach collective decisions. The primary objective of this aggregation process is to minimize both Type I errors (false positives) and Type II errors (false negatives). The way decision making is organized determines the way screening functions are combined and thereby how effective this objective is achieved.
The framework captures a broad range of empirical phenomena of interest to organizational designers, including collective decisions, communication structures, and voting.
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.
Takeaways:
Takeaways:
Gain an in-depth understanding of the class of models employed in organizational decision-making.
Acquire a practical toolbox that enables you to develop cutting-edge models for organizational decisions.
Analyze real-world applications and cases to understand the implications of various models in organizational settings.
Key Terms
Key Terms
Information aggregation: Aggregation of recommendations made by several people (e.g., executive committee, mgmt. team). Stephen M. Stigler’s The Seven Pillars of Statistical Wisdom provides a canonical reference to information aggregation, which is considered the first pillar.
Organizational decision: Decision made by a group of people through information aggregation.
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
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
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.
Stigler SM (2016) The Seven Pillars of Statistical Wisdom, Harvard University Press.
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. (Research note, Copenhagen Business School).
Free access: https://research.cbs.dk/files/59184864/link02_07.pdf
Christensen M, Knudsen T (2008) Entry and exit decisions in flexible teams, Journal of International Business Studies, 39, 1278–1292.
Free access: https://link.springer.com/article/10.1057/palgrave.jibs.8400413
Christensen M, Knudsen T (2010) Design of Decision-Making Organizations. Management Science. 56(1): 71-89.
Free access: https://pubsonline.informs.org/doi/epdf/10.1287/mnsc.1090.1096
Christensen M, Knudsen T (2020) Division of Roles and Endogenous Specialization. Industrial and Corporate Change. 29 (1): 105-124.
Free access: https://academic.oup.com/icc/article/29/1/105/5684883
Csaszar FA (2013) An efficient frontier in organization design: Organizational structure as a determinant of exploration and exploitation. Organization Science. 24(4): 1083-1101
Free access: https://pubsonline.informs.org/doi/10.1287/orsc.1120.0784
Csaszar FA, Eggers JP (2013) Organizational decision making: An information aggregation view. Management Science. 59(10): 2257-2277.
Free access: https://pubsonline.informs.org/doi/abs/10.1287/mnsc.1120.1698
Knudsen T, Levinthal DA (2007) Two faces of search: Alternative generation and alternative evaluation. Organization Science. 18(1):39-54.
Free access: https://pubsonline.informs.org/doi/abs/10.1287/orsc.1060.0216
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 (2023). Context and Aggregation: An Experimental Study of Bias and Discrimination in Organizational Decisions. Organization Science, Special issue: Experiments in Organizational Theory (forthcoming).
Free download: https://pubsonline.informs.org/doi/full/10.1287/orsc.2021.1502
Piezunka H & Schilke O (2023). The Dual Function of Organizational Structure: Aggregating and Shaping Individuals’ Votes. Organization Science, Special issue: Experiments in Organizational Theory (forthcoming).
Free download: https://pubsonline.informs.org/doi/full/10.1287/orsc.2023.1653
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