The Aggregation–Learning Trade-off

Abstract

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.

Location-Independent Organizations: Designing Collaboration Across Space and Time

“…This finding aligns with past research that has identified behavioral practices used to complement an organization’s hierarchical structure. For example, organizations adopt methods to manage deficient or uncertain systematic authority; these include using decision rules to guide behavior from top-down principles (Davis, Eisenhardt, and Bingham, 2009; Piezunka, Aggarwal, and Posen, 2022), allowing local behaviors to emerge from the ground up (Brown and Eisenhardt, 1997; Anderson et al, 1999; Nan, 2011; Moffett et al, 2021), and substituting structural oversight with enforcement via strong cultural norms (Ouchi, 1979; Von Krogh et al, 2012).…”

Training with AI: Evidence from chess computers

“…For example, the sequence of actions and responses results in numerous potential scenarios, making it hard to gain an overview of the performance landscape (Adner et al, 2014; Siggelkow, 2002). Also, the effect of any action is contingent on the actions taken by other actors—making learning harder (Piezunka et al, 2022; Rivkin & Siggelkow, 2003). Moreover, while credit assignment is generally difficult (Clough & Piezunka, 2020; Rahmandad et al, 2009), this is particularly true when performance is revealed only at the end (Christensen & Knudsen, 2010; Fang & Levinthal, 2009)—like it is often the case in strategic interactions.…”

Knowledge acquisition throughout the lifecycle: product and industry learning frameworks

“…We believe that this initial classification enables the emergence of more integrative empirical research by connecting the organizational learning and knowledge management streams more effectively to the broader marketing and innovation literatures. Finally, the proposed frameworks for illustrating the interplay between learning and (Piezunka et al, 2021).…”

“…One particular context in which this has an effect is organizational learning. The type/process of knowledge acquisition largely depends on the level of aggregation within the organization or the business ecosystem (Piezunka et al , 2021). In our context, industry evolution is characterized by a high degree of collective learning and knowledge aggregation (Woolley et al , 2010; Bernstein et al , 2018).…”

“…Finally, more conceptual work is needed to relate decision-making structures with the proposed learning frameworks throughout the industry and PLCs. In this way, future work should picture the trade-off of information aggregation and individual knowledge in different phases of the ILC and PLC (Piezunka et al , 2021).…”