The Model of Hierarchical Complexity (MHC) is a discrete state model that posits a series of nominal-scale orders of increasing task complexity. The Model provides a mathematical expression of each order of complexity. Uses of the Model to date include the behaviors of individual animals and humans, organizations, and social institutions, and indicate its applicability to tasks at various fractal scales, including those of time and social complexity. Dialectical processes of transition from any order to the next higher order are comprised of a sequence of discrete-state transition steps. The sequence of tasks in transition is identical from order to order. Thus, the transition steps pattern is also fractal. The transition steps result in increasingly less partial organization of combinations of elements at the next order and at the same time, those elements increase in complexity from one order to another, with fractal similarities to the overall model. This paper uses the MHC's mathematical expressions of the orders as the foundation for this first description of the fractal nature of the transition steps. It includes scored examples of transitions at several different scales of time, social complexity, and hierarchical complexity. These will show scales, steps, and transition data. It invites collaboration to develop the mathematical models of these fractal transition processes. It indicates an extension of psychophysics and suggests applications to decision theory, problem solving, learning models, time series analyses, game theory, information processing, and other analyses (e.g., policy changes or political polling over time). |