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

TL;DR

The paper addresses how inter-group boundary-spanning actors emerge in volatile environments to improve information exchange. It introduces a quantitative method to identify emergent IBSs at the micro-level and couples it with an agent-based model to study the learning mechanisms behind IBS emergence, illustrated via a disaster-response case in Jakarta. The results show that learning (via reinforcement learning) increases both the number of emergent IBSs and their effectiveness when information origins are stable, and that higher inter-group connectivity coupled with turbulence further enhances IBS emergence and performance. Practically, the work implies that fostering stable information sources, crowd-sourcing shocks, and building bridging social capital across groups can promote collective intelligence and resilient coordination in crises.

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.

Figures (12)

• Figure 1: Information flow paths illustrating the spread of information from its origin (i.e. node in the network in which new information is introduced) through a series of information sharing activities carried out by the network nodes representing the actors.
• Figure 2: Graphical overview of the Agent-Based Model (ABM) developed for this study. The model involves two distinct groups, namely professional responders (on the left, in black) and communities (on the right, in blue) that exchange information. The grey lines represent the formal and informal ties used for information exchange. The ties that cross the border between professional responders and communities are the inter-group ties.
• Figure 3: Graphical description of entities, their properties, states, and tasks in the developed ABM. The actors and environment are agents in the ABM given they carry out tasks.
• Figure 4: Frequency of occurrence of # FEs (number of informational boundary-spanning Function Executions) obtained by the IBS candidates, and the threshold used to capture the emergent IBSs.
• Figure 5: Relationship between the number of IBSs that emerged and the % of external information found by the groups who need it for different values of the threshold set as a percentile in the distribution of FEs from Figure \ref{['fig:exp-0_individual']}.