Abstract
The software domain is an environment that has produced a wide variety of exaptation-based innovations through the repurposing of data, algorithms, and visualizations to problems other than the ones they were originally developed to solve. Unfortunately these innovations have largely been the result of serendipity. Because modern software development is fundamentally aligned to the same principles of evolution that lead to biological innovation—modularity, fluidity, community, diversity, translatability, and combinatorial flexibility—it is an ideal environment in which to leverage our understanding of exaptation to actively facilitate innovations instead of leaving them to chance. Achieving this, however, requires a departure from traditional programming paradigms and the implementation of development systems specifically oriented toward innovation. Preliminary experiments show that when explicit innovation-oriented programming systems and practices are leveraged, innovations occur, suggesting opportunities to leverage the advantages of the virtual domain for the production of both repeatable and scaleable radical innovation.
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Notes
- 1.
The natural domain is simply an analogical domain to the cognitive domain, so our ability to exert control in this domain does not help us facilitate mental exaptation unless the organisms we are exerting control over are somehow analogous to the problem space we are trying to innovate in. Interestingly, if we did encode our problem organically, this would be a form of biological computing and it could be argued that it was, therefore, equivalent to software development using chemistry instead of code. As for the cognitive domain, there are some ways to exert control, through methods like priming and through processes like brainstorming, but since cognition remains a mostly black-box process it is difficult to assess the effects of these interventions or tailor them to the thought process of the individual. The physical domain offers many opportunities to exert control, but these are frequently expensive from a financial, time, and/or process overhead perspective.
- 2.
Programs can be written to be self-modifying or use non-deterministic algorithms that may return different results based on the conditions in which they are run. For example, the object code of a program might generate random numbers based on the time on the clock of the computer it is running on. In these cases, the reproduction of exactly the same object code could exhibit high variation when run. However, I will argue that these variations can be thought of as analogous to source code modifications performed at run-time by the computer instead of at design-time by the programmer. For the purposes of this chapter, I will choose to consider just the simpler case of human-programmed source code.
- 3.
There is much interesting research about the neurology of insight, in which researchers use brain imaging techniques including EEG and fMRI to understand what roles different parts of the brain play during problem-solving, and how brain activity differs when solving “insight” problems versus brute-force problem solving (Kounios and Beeman 2014; Bowden et al. 2005; Kounios et al. 2008; Luo et al. 2004).
- 4.
While Wattenburg recognized, and even demonstrated, the possibility of applying the arc diagram technique to gene sequences, he didn’t view biology as the best use-case. He wrote in the summary of his paper explaining the arc diagram, “We have shown examples of their potential use in domains ranging from text to DNA, although analysis of musical form is perhaps the most promising application.” (Wattenberg 2002).
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King, D. (2020). Innovation-Oriented Programming: Software Development as a Medium for Exaptation and Implications for the Active Facilitation of Innovation Within Virtual Environments. In: La Porta, C., Zapperi, S., Pilotti, L. (eds) Understanding Innovation Through Exaptation. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-030-45784-6_10
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