Bio-inspired understanding and engineering of business innovations

Business innovation can be understood as a systemic process of value creation that involves the various dimensions of a business system (Sawhney, Wolcott & Arroniz, 2006). Hence, an organization can innovate in the products and services it creates, the client segments it targets, the processes it employs, and the strategies devised to put its products on the market. Although several models of the innovation process have been proposed (Marinova & Phillimore, 2003), it has been shown that those based on biological evolution logic are better able to capture in a robust and precise manner some of the most distinctive features of business innovation (Kell & Lurie-Luke, 2015). To date, most of the literature exploring the link between biological evolution and business innovation discusses how the latter could be conceptually understood using the theoretical framework developed for the former. We argue that the biological analogy or metaphor is useful not only conceptually, but also methodologically. Over the last decades, engineering and computer science have both used biological processes as inspiration to develop artificial systems that are capable of evolution, information processing and problem solving, in an analogous way to living organisms in their natural environment (Maldonado & Gómez-Cruz, 2012). Due to their focus on populations of agents that interact with each other and with the environment, these artificial systems engage in bottom-up processes that result in behavioral, functional, or structural patterns that can be thought of as producing innovations. Biologically motivated methods developed in engineering and computing, such as artificial life (Kim & Cho, 2006), evolutionary computation (Goldberg, 2002), organic computing (Würtz, 2008), morphogenetic engineering (Doursat, Sayama & Michel, 2013), guided self-organization (Prokopenko, 2014), agent-based simulation (Ma & Nakamori, 2005) and bio-inspired computation (Forbes, 2004) can be tapped into, not only to understand innovation, but to engineer it. These methods, we argue, when transferred to the organizational context, can act as innovation accelerators. In this contribution, we, first, outline a conceptual framework, developed after a systematic literature review, that describes how different bio-inspired methods could be repurposed for the study, development and evaluation of innovations produced within the business innovation context. Later, we discuss three potential applications of the framework outlined: as mechanisms that promote the constant generation of novelty (Banzhaf et al., 2016), as computational processes in which the space of possibilities for an innovation is explored (Wagner & Rosen, 2014), and as simulated scenarios (artificial societies or markets) in which the impact of specific innovations can be tested (Ma & Nakamori, 2005). References Banzhaf, W., Baumgaertner, B., Beslon, G., Doursat, R., Foster, J. A., McMullin, B., … White, R. (2016). Defining and simulating open-ended novelty: requirements, guidelines, and challenges. Theory in Biosciences, 135(3), 131-161. Doursat, R., Sayama, H. & Michel, O. (2013). A review of morphogenetic engineering. Natural Computing, 12(4), 517-535. Forbes, N. (2004). Imitation of Life: How Biology Is Inspiring Computing. Cambridge, MA: MIT Press. Goldberg, D. E. (2002). The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Dordretch: Springer. Kell, D. B. & Lurie-Luke, E. (2015). The virtue of innovation: innovation through the lenses of biological evolution. Journal of the Royal Society Interface, 12(103), 20141183. Kim, K. J. & Cho, S. B. (2006). A comprehensive overview of the applications of artificial life. Artificial Life, 12(1), 153-182. Ma, T. & Nakamori, Y. (2005). Agent-based modeling on technological innovation as an evolutionary process. European Journal of Operational Research, 166(3), 741-755. Maldonado, C. E. & Gómez-Cruz, N. A. (2012). The complexification of engineering. Complexity, 17(4), 8-15. Marinova, D. & Phillimore, J. (2003). Models of innovation. In L. V. Shavinina (Ed.), The International Handbook on Innovation (pp. 44-53). Oxford: Elsevier. Prokopenko, M. (Ed.) (2014). Guided Self-Organization: Inception. Heidelberg: Springer. Sawhney, M., Wolcott, R & Arroniz, I. (2006). The 12 different ways for companies to innovate. MIT Sloan Management Review, 47(3), 75-81. Wagner, A. & Rosen, W. (2014). Spaces of the possible: universal Darwinism and the wall between technological and biological innovation. Journal of the Royal Society Interface, 11(97), 20131190. Würtz, R. (Ed.). (2008). Organic Computing. Berlin: Springer.