Learning to connect in action: Measuring and understanding the emergence of boundary spanners in volatile times

TL;DR

This paper introduces a new method and agent-based model to measure and understand how informational boundary spanners emerge in volatile environments, highlighting the role of learning in facilitating effective inter-group communication.

Contribution

It develops a novel measurement method and an agent-based modeling framework to analyze the emergence of boundary spanners, emphasizing the importance of learning mechanisms in volatile settings.

Findings

Learning promotes emergence of boundary spanners in low and high volatility environments.

Sufficient inter-group connections are crucial for effective boundary spanner emergence.

The method enables systematic analysis of mechanisms driving boundary spanner formation.

Abstract

Collective intelligence of diverse groups is key for tackling many of today's grand challenges such as fostering resilience and climate adaptation. Information exchange across such diverse groups is crucial for collective intelligence, especially in volatile environments. To facilitate inter-group information exchange, Informational Boundary Spanners (IBSs) as pivotal information exchange 'hubs' are promising. However, the mechanisms that drive the emergence of IBSs remain poorly understood. To address this gap there is first a need for a method to identify and measure the emergence of IBSs. Second, an Agent-Based Modelling (ABM) framework is not available to systematically study mechanisms for the emergence of IBSs in volatile environments. Third, even though the ability to learn who provides high-quality information is thought to be essential to explain the emergence of IBSs, a rigorous test of this mechanism is missing. The learning mechanism is formalized using an ABM framework, with the model's outputs analyzed using the proposed IBS emergence measurement method. To illustrate both the method and the learning mechanism, we present a case study focused on information sharing in the volatile environment of a disaster. The study shows that learning constitutes a mechanism for the emergence of effective IBSs in (a) low-volatility environments characterised by low uncertainty and (b) in high-volatility environments characterised by rapid change if the number of inter-group connections is sufficient. With the method and model, this paper aims to lay the foundations for exploring mechanisms for the emergence of IBSs that facilitate inter-group information exchange. This article advances collective intelligence by providing the essential elements for measuring and understanding the emergence of IBSs and exploring the effect of learning on their emergence in volatile environments.