Dynamic Network Theory (DNT) and Behavioral Reasoning Theory (BRT)

DNT Overview

Going beyond our micro work on behavioral reasoning theory (BRT) (Westaby and colleauges 2005 to 2020), our integrative work has culminated in the development of Dynamic Network Theory (DNT) (Westaby, 2012; Westaby & Parr, 2020; Westaby, Pfaff, & Redding, 2014; etc.).  The theory not only describes and explains the dynamics underlying more complex interpersonal systems, it also aims to predict and understand the way social networks impact goal pursuit, performance, emotional reactions, and climates in various social, organizational, and international domains, including those experiencing conflict. Our network goal analyses (NGA) provide a new way to visualize how social networks impact goal pursuits  in specific cases. To compare how this approach differs from traditional organizational charts and social network analysis, click here.


Individuals have used the theory to understand their general life networks, work networks, or specific goals, such as getting jobs, starting businesses, losing weight, running marathons, quitting tobacco, and improving performance in various contexts.  The areas of potential application are nearly limitless, as network goal analysis can be applied to any goal or behavior.

Social Network Roles

In contrast to the classic study of social network structure alone, dynamic network theory examines how a taxonomy of only 8 social network roles can explain the complex ways in which networks wield their influence on our goals, dreams, and aspirations.  What is unique about the approach is that goal nodes are directly inserted into social networks so see the connection to the goal.  This can be visualized through our creation of network goal analysis (Westaby & Parr, 2020; Westaby & Shon, 2017).  This terminology is synonymous or interchangeable with our original use of dynamic network chart terminology (Westaby, 2012; Westaby et al., 2014), but helps further clarify that our approach explicitly examines social networks in relation to target goals, as compared to traditional social network analysis that describes the relationship between social network entities alone, which is also important, depending on the research and practice question. 

Theoretically, the 8 social network roles show how people are involved in goal or behavioral pursuits, such as by helping or hindering the process, which in turn is predicted to impact emergent outcomes, such as performance, climates, and system well-being. Our analyses demonstrate how these links function through our computer visualization tools.  These visualizations are generated after participants engage in our network goal surveys online, or through our algorithms that apply DNT concepts to massive online behavior.


The 8 social network roles

  1. Goal striving (G): Entities independently pursing the goal, such as on their own time or by themselves.
  2. System supporting (S): Entities supporting others in the goal pursuit.
  3. Goal preventing (P): Entities independently obstructing or resisting the goal.
  4. Supportive resisting (V): Entities that have relationships that are obstructing or resisting the goal.
  5. System negating (N): Entities feeling upset about those involved with the goal. This newer formulation broadens previous versions of system negation in DNT.
  6. Constructive system reacting (R): Entities reacting constructively to conflicts with others involved with the goal.  This extends our previous conceptualization of system reactance in DNT, providing a richer account of reactive, but affirming dynamics in human systems.
  7. Observing (O): In non-multiplex relations, entities observing those involved with the goal, but not helping or hurting the process.  We use the information gained from observing the goal pursuit to visualize the feedback (FB) path from the goal node back to other people in the social network.
  8. Interacting (I): In non-multiplex relations, entities interacting around those involved with the goal, but are not helping, not hurting, not observing, and not closely paying attention to those involved with the goal.

See our publications for more details that provide the empirical evidence that supports each role (e.g., Chapter 2, Westaby, 2012).

Underlying Processes

The following figure visually illustrates some of the presumed underlying causal processes in the theory, many of which have scientific support.  This figure builds from the recent publication in the American Psychologist, the flagship journal of the American Psychological Association:  . This figure represents more recent theorizing in our Lab.


What Else Does DNT Explain?


  • Higher-level concepts , such as network motivation, conflict, and emergent system-level concepts (e.g., overall goal achievement/performance, climates, system satisfaction, and relationship quality)
  • Other complex dynamics, such as network rippling of emotions, dynamic network intelligence, centrality dynamics, and multiplex linkages. 
  • Group and team dynamics, such as showing how organizational structure is subsumed under multiple dynamic network systems.
  • Network goal interventions, such as coming up with strategies to positively influence goal achievement and positive climates.

Our Dynamic network theory (Westaby, Pfaff, & Redding, 2014) article provides a good overview of the theory. However, please note that our current conceptualization of system reactance (R) in our surveys and visualization tools have been modified to examine constructive ways to try to resolve conflicts.  Moreover, we now use color-coded links with letters to denote the roles in our new analytics, instead of the solid black or dashed paths shown in this article and in Westaby's (2012) original formulation of dynamic network theory.  These changes improve our computer visualizations of complex systems, making complex systems easier to visualize and understand.  These more recent applications can be seen in our recent empirical tests of the theory: Network goal analysis paper (Westaby & Parr, 2020).



How does reasoning and decision-making activate the roles?

Dynamic network theory states that people use various judgement and decision-making processes that activate each social network role (or multiplex combination of the roles).  This then impacts important emergent outcomes in dynamic network systems, such as performance, emotional reactions, and climates.

Please go to the following to see how behavioral reasoning impacts decision processes:

Reasoning and Behavioral Decision-Making Processes  (Westaby, 2005; Westaby et al., 2010). This demonstrates how reasoning, attitudinal, and intention processes are linked in a causal network.  This is a contemporary behavioral intention theory that inserts reasons and justifications into the behavioral prediction process at the individual level.  Behavioral intention models, some most widely applied and validated specific behavioral models in the world, are part of our Lab's direct lineage.  



Copyright James D. Westaby (C).  All rights reserved. 

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