It is an interesting phenomenon that the degree of chaos in the environment itself can lead to adaptive changes: It is known that mutation rates (or error rates in transcribing DNA) for certain species change depending on the rate at which environmental conditions change. For environments that are fairly stable reproduction errors decrease and higher levels of adaptation to the stable environment can be observed. In terms of technical innovations very similar patterns can be seen: Mobility and change in a country like Switzerland are very different from corresponding parameters in the US. Correspondingly one would not expect that innovations like mobile homes that would support a fast changing life style would be made in a country like Switzerland.
The feature of self-organizing computer programs like Tierra to be able to improve under the influence of stochastic fluctuations seems to be especially relevant in the domain of nano-computers where error rates are naturally much higher than in conventional electronic computers. There is very strong evidence that external fluctuations not only are tolerated by complex adaptive systems but that they are even systematically exploited to improve the system's generalized fitness parameter. For instance in the phenomenon of stochastic resonance signals received by sensory hair cell receptors of lobsters could be shown to display a higher performance in a noisy environment. This concept has been generalized to spatio-temporal stochastic resonance in which moving visual patterns can be enhanced in a noisy environment.
Deterministic chaotic dynamics can be sometimes be actively used in strategies to simulate stochastic environments: Learning of patterns by neural networks can be accelerated using chaotic learning strategies. The performance of such a strategy can sometimes even be better than the stochastic strategy itself (simulated annealing) if the chaotic dynamics has been adapted to the intrinsic dynamics of the system using the concept of "dynamical key". In the context of organizational learning strategies including a limited amount of chaos can reduce the degree of predictability for competitors.
The perspective presented in this paper is admittedly very broad and goes well beyond the above mentioned, more specific definitions of the concept of innovation that is restricted to human behavior and organizations. From the perspective of complex systems there is no difference in innovations as the result of careful and methodological planning of intelligent inventors and those that are triggered by random events. As a matter of fact, even in innovations that were rewarded by the Nobel price chance events often played a crucial role.