Collective sensemaking is a form of socially-distributed cog- nition (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 collec- tive level, agents often need to share information about the results of their own processing activity, and this raises ques- tions about how the structure of communication networks af- fects collective sensemaking abilities. In the current study, we used a computational model of collective sensemaking in which individual agents are implemented as constraint satis- faction networks (CSNs) (see Smart & Shadbolt, 2012). We then investigated how the cognitive responses of agents were affected by different kinds of communication network struc- ture.