Gravitation-Simulation:     Use DRAG to spin and MOUSEWHEEL to zoom

Complexity

tl;dr

Handling complexity in smart way is a key success factor in the development of innovative solutions.

The complexity of contemporary, highly-interconnected working environments frequently leads to challenging problems that cannot be solved satisfactorily on an ad hoc basis.
In particular, the development of innovative products, services or business models, combined with the strategic alignment of new business areas and collaboration within and between businesses can often be implemented successfully only once they are examined systematically.

Systematic cause-effect-analysis

Complexity cannot be managed! In most cases, it is difficult to thoroughly get to grips with the full context. Suitable methodologies and descriptive models can, however, make it possible to get closer to the actual situation, resulting in causes and effects becoming easier to understand.

Predator-prey scenario

The first step in a constructive solution strategy could be to clarify whether the question at hand is a trivial, complicated, or complex problem. Many problems that appear simple at first glance are in no way trivial when examined more closely.

For example, the gravity simulation shown in the page header may indeed be complicated, but it is not complex. However, what may seem like a simple predator-prey scenario at first glance can very quickly assume all kinds of complex forms.

Even easily-visualized models demonstrate contexts with a multiplicity of causes, resulting in significantly enhanced recognition.
The example website success, set out on the right-hand side, quickly makes it clear with around 30 factors as to why the quality of website content, a highly motivated editorial team, and the competitive landscape can each play an important role.

Click to see the interactive model.

Not all factors have equal effects, and feedback loops are of particular interest in this respect. These show whether factors result in equilibrium (as in the predator-prey model) or, more significantly, reinforce each other or cancel each other out.
In the example above, quality content leads to satisfied visitors. These recommend the website to others, leading to a reinforcing feedback effect.

To the extent that measurements are available, or causation is understood, models can also be simulated. Easy to understand, interactive depictions help to achieve an intuitive understanding of dynamic models.

Qualitative models can be used to test new business models with new or changed factors, thereby visualizing possible developments and interconnected causation factors.

Background

From the mid-20th century onwards, methods for modeling cybernetic systems were developed based on the work of Jay W. Forrester[3]. Frederic Vester's sensitivity model[4] made systemic (networked) thinking well known. Although some easy to use, cost-effective tools[5] have become available in the meantime, linear (rather than networked or interconnected) thinking remains the dominant model. The fatal consequences that linear thinking can have are described by Dietrich Dörner[6] in The Logic of Failure.


Novel, complex situations often leave experienced decision makers powerless in the face of adversity.

When confronted with problems of this nature, solution strategies such as trial and error, wait and see, simplification, and heuristics are of little benefit.

Gaining clarity and finding innovative solutions requires different strategies.


We support our customers in particular by exploring new business areas and new business models with systemic tools and a great deal of analytical and creative energy ;-)