Complexity science research and analysis of business and other social systems

Abstract

Universal scientific principles from the field of complexity theory are creating great advances in the understanding of phenomena throughout science. In the social sciences this has the potential to produce new ways of researching and managing business and other social systems. However, this approach has not become widespread in business research and other areas of social science. As a result, much of the social sciences still utilize concepts and research methodologies that evolved in a piecemeal manner over time, limiting the scientific advances that can be made. Currently, nearly all of the statistical analytics used in business and other social science research is based in traditional concepts such as independence, linearity and stable distributions which do not reflect the inherent complex nature of social systems. As a result, improper research and analytical methods are creating inaccurate results and misleading theories, which are producing inadequate tools for practitioners. Therefore, the research reported in this thesis develops a social science research and analytical methodological framework based on the principles of complexity theory. Social systems are conceptualized as complex information processes that receive information, make changes to the definable and measurable concept of information, and produce output. This involves bringing together universal concepts from complexity science, information theory, statistics as well as various business and other social theories. The research in this thesis, therefore, contributes to the development of a methodology that changes the fundamental basis of analytics. Instead of analysis being based in traditional notions such as independence, a complex analytics approach is explored and developed that analyzes complex interdependence based patterns in space and time utilizing measures of complex structure. Elements of this methodology are demonstrated by analyzing the dynamic complex properties of business relationships using empirical data. Quantitative metrics for studying the evolution of social systems are developed and utilized which demonstrate the usefulness of this complex analytics approach in practical social research. This thesis contributes knowledge in a number of ways. It assists in creating a better understanding of how social systems behave dynamically, process information and solve problems in their environments. It contributes to the development of a better complex research framework and analytical tools, making advanced analytical approaches more accessible to practical research. And by doing so, it helps open up the study of social systems as examples of universal complex information processes using state of the art complex scientific thinking.

Date of Award	2013
Original language	English