Other Processes in DNT

The following concepts were also introduced in the original theory to explain important dynamics in human systems, among many others (Westaby, 2002; Westaby et al. 2014; etc).

Network Rippling of Emotions

This concept was created and coined in our theorizing to explain how emotions become contagious in networks based on goal achievement or failure. For example, "joy" linkages would be predicted to spread throughout a network to goal striver and system supporter entities when a goal is accomplished and communicated. Other entities having goal preventing linkages would be predicted to become more upset by this success, illustrating negative contagion. This illustrates the theoretical importance of having goals in social networks.

Dynamic Network Intelligence

We also developed and coined the concept of “dynamic network intelligence” (DNI) in the context of goal pursuit, which illustrates how accurate entities are in their knowledge of other entities’ social network roles in their goals. For example, Chris may think Pat is supportive of Jane's goal striving, but when asked, Pat states no support for Jane's goal. This disconnect between perception and reality indicates that Chris has low DNI regarding this linkage. There are many ways our lab can now assess this when data is collected on two or more people involved in the same system. This work builds from other scholars' important research on "cognitive accuracy" in social networks. We extend such accuracy notions to network goal systems.

Positive or Negative Effects of High Centrality

The concept of centrality is important in dynamic network theory as it is in most social network research, but the theory indicates that not all types of centrality are positive for goal pursuit. For example, while a person with high ego-centric support (S) centrality would be predicted to be an important entity in facilitating goal achievement, another entity that has high supportive resistance centrality (V) would be predicted to have a negative impact on the system. Thus, the roles in the theory illustrate when centrality could be good or bad, thereby challenging general assumptions about centrality as positive in social networks. Moreover, our research sheds light on how centrality concepts can help us further describe and explain different dynamics occurring in social networks involved with goals (or not). For example, we can contrast centrality levels on the social network alone or when the goal is also included, which illustrates how central the goal is to the system, a unique view provided through a dynamic network theory lens.

Multiplex Linkages

In dynamic network theory, the eight social network roles can serve as independent variables in complex systems. This means that people may activate multiple roles toward another entity in the system. For example, one person may strongly support another entity most of the time (i.e., a strong S linkage), but occasionally be mildly upset with the entity (i.e., a weak N linkage). Thus, the linkage between this person and the other entity is multiplex: There is both system support and system negation. Because most of our surveys assess the degree of role activation, one can visualize such multiplex linkages by degree (i.e., a thick S link and a thin N link would be seen in the network goal graph). Our research suggest that multiplex linkages are extremely common in complex systems.