My broad academic interests are in Complex Adaptive Networks applied to human cognition and bounded rationality.
These are dynamical systems by virtue of varying initial conditions, non-linear processes, individual learning, and interaction among agents. Consequently, these systems operate far from equilibrium, co-evolve in their eco-systems, and display emergent outcomes.
Notes on Social Complexity – Part 1 Complex Adaptive Systems Research focuses on an interesting and maturing class of social problems where individuals dynamically interact with others and make local choices consistent with the principles of behavioral economics. It is my primary research area of human behavior. See the complete Part 1 overview here:
Reed, 2025 – Social Complexity – Part 1.pdf 
A more general description of Complexity is provided here:
A brief introduction – Reed 2019 
Social Thresholds
Individuals turn their ideas and beliefs into action, at least partly, when their confidence in achieving a desired result reaches a threshold of value. A threshold is crossed at a particular time and place, when the combined valence of emotional, social and cognitive motivations add together. These can be described as “triggers” that change the path of individual actions and likely have influence on local networks of similarly situated individuals.
Far From Equilibrium
The semi-autonomous and interconnected nature of social networks make system level outcomes largely unpredictable on preliminary analysis, because the internal pathways are dependent on idiosyncratic human choices. And these are unlikely to be repeated as individuals co-evolve with their environments. Thus, commonly used population statistics can be more misleading then helpful.
Attractor Basins and Outcomes
Modeling and simulating complex processes is often an iterative approach to understanding the internal mechanisms that exert unexpected forces inside the system. An Attractor Basin is the name given to a specific region that receives a disproportionate amount of activity in a complex system. While these may be obscured in diagrammatic representations, they may be found and understood during simulation. These discoveries can be clues in predicting outcomes.
More information is available by request.
