The Simple Rules of Complex Networks : A Heuristic for Determining the Potential Complexity of Any Network and Making Structural Predictions

Network theory has broad application in the physical, natural, and social sciences. The study of complex networks, and their applications to the study of complex systems, have focused predominantly on: (1) the elemental vertex-edge structure (a.k.a., nodes and relationships), and subsequently (2) the dynamics that occur as a result of this basic structure (e.g., diameter, distribution, small world, contagion, etc). This network theory methodology has provided powerful quantitative tools in the interdisciplinary study of complex systems, and has enriched our thinking about them. However, the simplifying assumptions of the network theory framework have also informed the field of systems thinking, sometimes to its detriment. Network-thinking tends to shoe-horn a number of important elemental structures of complex systems into node-edge relational structure. By assigning these elemental structures to edges, both elemental and emergent complexity can be lost. This paper articulates how DSRP Theory can enrich network thinking about complex systems by: (1) identifying the elemental structures that are typically hidden in network models, (2) quantifying their nature and abundance, and (3) explicating their potential contribution to the intrinsic function and emergent complexity of systems. Specifically, we detail several DSRP heuristics for determining how many elements potentially exist in any network model, demonstrating the effectiveness of DSRP as a “universal cognitive grammar” for identifying and analyzing the structural potentials in complex systems.