The Aggregation–Learning Trade-off

2021 ◽  
Author(s):  
Henning Piezunka ◽  
Vikas A. Aggarwal ◽  
Hart E. Posen

Organizational decision making that leverages the collective wisdom and knowledge of multiple individuals is ubiquitous in management practice, occurring in settings such as top management teams, corporate boards, and the teams and groups that pervade modern organizations. Decision-making structures employed by organizations shape the effectiveness of knowledge aggregation. We argue that decision-making structures play a second crucial role in that they shape the learning of individuals that participate in organizational decision making. In organizational decision making, individuals do not engage in learning by doing but, rather, in what we call learning by participating, which is distinct in that individuals learn by receiving feedback not on their own choices but, rather, on the choice made by the organization. We examine how learning by participating influences the efficacy of aggregation and learning across alternative decision-making structures and group sizes. Our central insight is that learning by participating leads to an aggregation–learning trade-off in which structures that are effective in aggregating information can be ineffective in fostering individual learning. We discuss implications for research on organizations in the areas of learning, microfoundations, teams, and crowds.

2021 ◽  
pp. 147612702110038
Author(s):  
Mark P. Healey ◽  
Adrien Querbes ◽  
Mercedes Bleda

Behavioral theories of organizational decision making highlight the importance of decision rules for information aggregation, which are used to combine individuals’ evaluations of alternatives into collective choice. We examine how resource constraints affect the ability to make effective decisions with aggregation rules, including majority voting, averaging opinions, and delegating to experts. Using a series of computational experiments, we find that which rule is best depends on how an organization uses scarce resources to improve members’ knowledge of alternatives (accuracy resources) and include them in decisions (inclusion resources). By demonstrating the interdependence of resources and aggregation rules, we draw attention to the resource costs required to harness collective wisdom in organizations. We discuss implications for research on information aggregation and the design of organizational decision architectures and outline new directions for research.


2021 ◽  
Vol 16 (2) ◽  
pp. 571-603
Author(s):  
Juan F. Escobar ◽  
Qiaoxi Zhang

Learning is crucial to organizational decision making but often needs to be delegated. We examine a dynamic delegation problem where a principal decides on a project with uncertain profitability. A biased agent, who is initially as uninformed as the principal, privately learns the profitability over time and communicates to the principal. We formulate learning delegation as a dynamic mechanism design problem and characterize the optimal delegation scheme. We show that private learning gives rise to the trade‐off between how much information to acquire and how promptly it is reflected in the decision. We discuss implications on learning delegation for distinct organizations.


Author(s):  
W. Lee Meeks ◽  
Subhasish Dasgupta

For several years GIS has been expanding beyond its niche of analyzing earth science data for earth science purposes. As GIS continues to migrate into business applications and support operational decision-making, GIS will become a standard part of the portfolio that information systems organizations rely on to support and guide operations. There are several ways in which GIS can support a transformation in organizational decision-making. One of these is to inculcate a geospatial “mindset” among managers, analysts, and decision makers so that alternative sources of data are considered and alternative decision-making processes are employed.


Author(s):  
Bahador Bahrami

Evidence for and against the idea that “two heads are better than one” is abundant. This chapter considers the contextual conditions and social norms that predict madness or wisdom of crowds to identify the adaptive value of collective decision-making beyond increased accuracy. Similarity of competence among members of a collective impacts collective accuracy, but interacting individuals often seem to operate under the assumption that they are equally competent even when direct evidence suggest the opposite and dyadic performance suffers. Cross-cultural data from Iran, China, and Denmark support this assumption of similarity (i.e., equality bias) as a sensible heuristic that works most of the time and simplifies social interaction. Crowds often trade off accuracy for other collective benefits such as diffusion of responsibility and reduction of regret. Consequently, two heads are sometimes better than one, but no-one holds the collective accountable, not even for the most disastrous of outcomes.


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