Measuring External Complexity of Complex Adaptive Systems Using Onicescu’s Informational Energy

During the last decades the complexity theory of complex adaptive systems has captured the attention of a plethora of people, ranging from scientists and to the general public, through its discussions on emergence, behavior, self-organization, computer simulations and so on with a strong emphasis in the globalization context. The intuitively given explanations about produced phenomena are hardly accepted, by regular people, without giving them scientifically proofs with some measurements and figures. Thus we are forced to find new measurements and analysis tools or to adapt the existing ones, for better understand those systems. The social systems and economic systems become more complex and unpredictable and necessitate the use, among other things, of the acquirements of social psychology to better understand and monitor them. Having a large number of mutually internetworked and interdependent parts the complex adaptive systems are hard to model and understand, so that any theory or practice defined/ applied to reduce the area of the analyzed complexity and making analysis, modeling and simulation feasible is welcome and useful. The analysis of both, external complexity and internal complexity of complex adaptive systems, as defined by Jüergen Jost, become a starting point. The external complexity addresses the volume of inputs (as information/ energy), from system’s environment, that the system is capable to manipulate or to process, while, the internal complexity is a measure of input representation by the system, in fact, the model complexity. For both categories the existing model uses entropy, or other formulas based on, to measure the system. This paper introduces the usage of Onicescu's Informational Energy theory and measures, based on frequencies used in statistics, to approximate the measurements of external complexity. The aim of the paper is not to use a technology instead another, but a way to simplify and express some measurements in terms of normal frequencies, the general people encounter daily and better understand, and not in terms of probabilities that requires mathematics skills and, maybe, sophisticated computational tools from the normal reader part, for better understanding. DOI: 10.5901/mjss.2014.v5n22p40