Learning-By-Participating in Decision-Making: Broadening Participation, Narrowing Feedback

A central tenet of work in the Carnegie School tradition is the notion of “learning-by-doing”— organizations learn over time through feedback. In this paper we argue that the learning-by-doing account overlooks the fact that an organization’s decision-making structure is often participatory—i.e., organizational decisions often involve multiple individuals aggregating opinions through a process such as voting. In such contexts, individuals in the organization do not themselves learn-by-doing. Rather, when participating in the decision, they may vote for an alternative that is different from the one eventually selected by the organization. A key consequence of this is that these individual participants do not always receive feedback on their own choices; rather, they receive feedback on the choice made by the organization. We call this “learning-by-participating,” and we seek to understand the implications of this form of learning by comparing it to learning-by-doing, where an individual in the organization (such as the CEO) makes decisions on her own. Using a computational model of decision-making under uncertainty, we find that learning-by-participating leads to distinct patterns of individual learning that create trade-offs at the individual and organizational-levels. For example, while learning-by-participating is beneficial with respect to organization-level performance, it causes a minority of individuals within the organization to hold overly-optimistic views of low-payoff alternatives. We discuss the implications of our findings for research on learning and information aggregation.