A Computational Model of Collective Sensemaking: Differential Effects of Communication Network Structure on Collective Sensemaking Abilities

Abstract

Collective sensemaking is a form of socially-distributed cognition (see Hutchins, 1995) in which multiple agents attempt to interpret (make sense of) specific bodies of environmental information. In order to optimize performance at the collective level, agents often need to share information about the results of their own processing activity, and this raises questions about how the structure of communication networks affects collective sensemaking abilities. In the current study, we used a computational model of collective sensemaking in which individual agents are implemented as constraint satisfaction networks (CSNs) (see Smart & Shadbolt, 2012). We then investigated how the cognitive responses of agents were affected by different kinds of communication network structure.