Komplexität & Entrepreneurship

Whether you are exploring entrepreneurship as an individual, founding a start-up with a team, or building an entire entrepreneurial ecosystem, all of this involves complex self-organisation at various levels.

The lecture series ‘Komplexität und Entrepreneurship’ highlights the diverse theories (chaos theory, synergetics, etc.) and mathematical-quantitative methods related to the topic of complexity, supported by fascinating empirical studies from recent years, and demonstrates the potential of this approach. 

In the context of start-ups in particular, we encounter a high degree of uncertainty and complexity – sometimes even chaos. Purely prediction-oriented business management methods are hardly applicable here, as they require stable framework and reliable historical data – conditions that are often lacking in a highly dynamic start-up environment, whether in the development of new markets (‘blue ocean’) or in the context of disruptive innovations in existing markets (‘red ocean’). Schumpeter referred to this as ‘creative destruction.’ But without existing experience, without historical data and without a reliable vision of the future, predictive business management methods reach their limits.

In this context, entrepreneurship is a new discipline that helps us to navigate through the fog that lies ahead. This fog can be scientifically understood as complexity. Some of the proposed and empirically tested entrepreneurial methods are more helpful than others.

However, it is certain that complexity manifests itself in the loss of (long-term) predictability, and its cause is by no means a lack of information or even a lack of knowledge. On the contrary, complexity manifests itself in the presence of strict and indisputably valid scientific laws, so that classical business administration approaches often (inevitably) come to nothing. Entrepreneurs are therefore often confronted with a wall of fog in which they have to question or even reorient their thinking. There is probably no simple concept to follow, no ‘business plan’ that would last for five years.

For a long time, economic thinking was guided by a mechanistic paradigm whose mathematical foundations successfully shaped the field for many years, but which reach their limits in phases of complexity. Some economists argue for abandoning mathematics in economics altogether. However, this path is neither necessary nor expedient, and in the words of Hölderlin (2015):

 Wo aber Gefahr ist, wächst auch die rettende Kraft.
(Where there is danger, there also grows the saving power.)

And so the established quantitative, empirical-mathematical methods of complexity science, which are finding their way into economics more and more, help us, based on synergetics, to find a path through the complex tangle and to design our start-up in a self-organising way. The course tries to avoid as much maths as possible and to explain the few fundamentals in an accessible way.