Startups and Chaotic Dynamics

Our hypothesis is that the startups also demonstrate the key properties for a system behaving as a Chaotic Dynamic system. The key properties are that the system evolves over time and that it is sensitive to initial conditions. As a result, the performance of startups often appears to be random, as does the performance of all Chaotic Dynamic systems. The realization that startups are Chaotic Dynamic systems is groundbreaking, we believe. This recognition focuses the research on startup failure rate on the key initial conditions, which can cause rapid (non-linear) error propagation, as opposed to the traditional approach of focusing on an almost infinite number of other variables (internal and external) which may influence startup performance over its life. Identification and analysis of the key initial conditions, of course, is the first critical step before one can recommend how to set better initial conditions and establish processes to control (non-linear) error propagation.

We have built a model of the Chaotic Startup which is the base for a paper called Startups, VCs, and Chaos. The paper has been submitted for publication. Below is the NPV frequency development simulation animation result of the model. As it can be seen, the first few simulations show apparent random results, but as the number for simulations increase patterns start to emerge.

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