Assessing A Novel Approach To Identifying Optimal Threshold Levels For Cognitive Consensus Structures: Implications and general applications

Previous research has demonstrated the importance of cognitive social network structures to better understanding human behavior and thought. Yet network members may deviate in perceiving whether relations exist between pairs of nodes in a network, which can present a challenge in modeling cognitive consensus structures. It has been suggested to define cognitive consensus structures (CCS) to yield a minimum threshold level of 50% of network members perceiving that a relation exits. Here I suggest an improved operational definition, labeled optimal cognitive consensus structures (OCCS). The OCCS threshold level is a function of a consensus structure; yielding the maximum correlate with the summation of nodes’ cognitive interpretations of a social network. Revisiting two datasets, I find that the OCCS’ predictive validity outperforms the CCS concept in most cases. I also argue how the OCCS can be further developed as a general tool for optimally dichotomizing valued relational data. Authors Jarle Aarstad, Ph.D. is an Associate Professor in Organization Studies at Bergen University College, Faculty of Engineering. His major research interests are social network theory, innovation studies, and entrepreneurship.