Front-loading complexity for innovation

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articles / innovation

An interesting paper on influencing the rates of innovation – how more likely we are to come up with new or more complex products given a set of technologies, or components, that are already available to us now.

(…) we measure how the number of makeable products (words, recipes, cocktails, and software products) grows as we acquire new components (letters, ingredients, beverages, and development tools). We do this for an arbitrary order of component acquisition and the average over all possible component orderings. Second, we prove a conservation law for how innovation occurs through time: The average size of the product space times a complexity discount is constant over every stage of the innovation process. We use this law to forecast the size of the product space in the future based on the complexity of the products we can make now. Third, we show that the growth of the average product space depends only on the distribution of product complexity and not on details about which components make up which products. Front-loaded complexity distributions—those that have a lot of simple products, the average product complexity being equal—have much higher innovation rates. We apply our insights to lean methodology, frugal innovation, and tinkering.

What matters for the rate of innovation is how current product complexity is distributed. Technologies with product complexity distributions that are broader and include many simple products as well as some very complex ones have faster innovation rates.

This is true even though all these distributions may have the same average complexity (the average abstracts component usefulness – read the full paper for details about this).

The benefit of being front-loaded

(…) Even when the mean product complexity  is the same across different domains, a domain with a front-loaded distribution of complexity yields much faster innovation. A front-loaded distribution, such as Poisson, has many simple products, whereas a distribution that is not front-loaded, such as constant complexity, has no simple products. A binomial distribution lies between these two. The more front-loaded the distribution, the faster the innovation rate tends to be.

These results help to explain the different rate of innovation for different technologies. Moreover, based on the current state of the technology, we can also prescribe the most adequate innovation methodology for firms and startups.

Our work suggests that the scope for applying lean methodology depends on how front-loaded the distribution of product complexity is. In domains that are not front-loaded, there will be a scarcity of products that can be made at the start of the innovation process. Such domains are best suited to firms with the resources to weather sustained investment with little return early on. On the other hand, a front-loaded distribution of products will enable the rapid expansion of the product space straightaway. Resource-poor start-ups and developing communities are more likely to thrive in such domains. Many organizations are confronted with a choice about which domain to enter, and anticipating these differences ahead of time can help them choose the right one.

T. M. A. Fink and M. Reeves, How much can we influence the rate of innovation? Science Advances, 5, 1, eaat6107 (2019)

Abstract
Innovation is how organizations drive technological change, but the rate of innovation can vary considerably from one technological domain to another. To understand why some domains flourish more rapidly than others, we studied a model of innovation in which products are built out of components. We derived a conservation law for the average size of the product space as more components are acquired and tested our insights using historical data from language, gastronomy, mixed drinks, and technology. We find that the innovation rate is partly influenceable and partly predetermined, similar to how traits are partly set by nurture and partly set by nature. The predetermined aspect is fixed solely by the distribution of the complexity of products in each domain. Different distributions can produce markedly different innovation rates. This helps explain why some domains show faster innovation than others, despite similar efforts to accelerate them. Our insights also give a quantitative perspective on lean methodology, frugal innovation, and mechanisms to encourage tinkering.

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The Author

Knowledge architect, futurist, enthusiast of new technologies and innovations, avid reader

1 Comment

  1. Pingback: Decentralized innovation: data analytics and access to differentiated knowledge – Oragious

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