Agent-based model of collective decision-making

Organizations bring together individuals with different areas of functional expertise and use mechanisms, such as teams, routines, and hierarchy, to integrate their diverse information and utilize information sharing strategies to make intelligent decisions.

However, as organizations face increasing information overload with the adoption of new information technologies, it has become unclear whether such strategies remain adequate or bounded nationality will prevail.  Bounded rationality refers to how individuals are subject to bounds on their rationality.

In this paper, Dirk-Jan Van Veen, external page Asst Prof. Ravi S. Kudesia, and external page Prof. Hans R. Heinimann developed an agent-based model that simulates information sharing in teams, where critical information is distributed across its members. They then examined how bounded rationality and information sharing strategies impact the speed and accuracy of collective decision-making with respect to information overload.

The results of their study suggest that there is value in assembling teams with diverse information sets and also in ensuring that they interact using strategies to guide their search through this information space. The researchers suggest for teams to use distinct strategies depending on whether speed or accuracy is imperative. 

For organizations that value accurate decisions, they should search through the information space as thoroughly as possible while organizations that value speed in decision-making should leverage the wisdom of disagreement, which yields the highest accuracy with minimum cost to decision speed and greater robustness to bounded rationality.

The paper external page "An Agent-Based Model of Collective Decision-Making: How Information Sharing Strategies Scale With Information Overload" was published in IEEE Transactions on Computational Social Systems.