Complexity’s Methods & Models
Complexity is a term that has become highly popular, however the application of this field needs to be further defined and refined, especially since there is no unified view of how to define complexity. There are different definitions of complexity, which become apparent depending upon the research area, and while there are some common threads, computer scientists, biologists, physicists, mathematicians, social scientists will each have their own particular flavour in how they define complexity; these differences become more apparent when applying complexity in each research area.
Complexity is a powerful paradigm. Understanding the distinction between complexity thinking and complexity science is important in order to define the methods and tools, which should be used when applying complexity in exploratory research. The challenge for us, is to develop clear methods and approaches in how we study complexity according to the needs of the reality we are studying. Complexity science has developed well-defined methods of studying complexity ranging from network science to dynamical modelling. These methods of modelling complexity provide us with well-defined approaches of studying complexity, and scientific innovations will arise in how we develop the mathematical concepts and tools to study complexity in every field.
There is also I think another aspect, which is what I would describe as complexity thinking, which is simply recognising that different disciplines are needed to understand complexity in different settings. Each discipline will understand reality in a particular way, and it is the convergence of these different perspectives, which is what I would describe as complexity thinking. The challenge is to unify these different perspectives in order to create unique insights, and this requires more fluidity in thought. Developing an understanding of different perspectives and how those perspectives were arrived at enables us to both enrich our thinking and develop new models to describe reality. Consequently it is important to have a wide knowledge of different modelling approaches from different fields. The challenge my colleagues and me is how we translate that into developing models that draw from different disciplines into models that are 'all encompassing'.