Understanding Disruptive Innovation Through Evolutionary Computation Principles
This chapter explores the nature of innovation from the perspectives of evolutionary computation principles. So far, the disciplines of innovation have been mainly discussed in management science literature, however, some of the recent articles address th
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Abstract This chapter explores the nature of innovation from the perspectives of evolutionary computation principles. So far, the disciplines of innovation have been mainly discussed in management science literature, however, some of the recent articles address the transdisciplinary characteristic of innovation-related issues from system science standpoints. In such discussions, evolutionary computation, which is a flexible but strong computational methodology inspired by biological evolution, has had one of the major roles to explain the disruptive innovation phenomena in new businesses, organizations, or new products. This chapter surveys the ideas of innovation with evolutionary computation from management, computer, system, and biological sciences. Then it discusses the system requirements for open or free innovation. The chapter concludes some comments on the strategies to accelerate the technical innovation processes. Keywords Transdisciplinary innovation · Evolutionary computation · Complex systems · Agent-Based modeling · System creation
1 Introduction This chapter deals with the topics of innovation principles from the perspectives of evolutionary computation. The contents have come from both our discussions at the Smarter World Research Group [1] and my own experience on Agent-Based Modeling research [2–5]. So far, the creation of innovative business, organizations, or products has been considered to be the issue in the field of management science,
Adapted from Takao Terano “Evolutionary Computation Approach to Understand Mechanisms of Interdisciplinary Innovation (written in Japanese),” Journal of The Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 692–697 (2016). Partly translated by permission of The Society of Instrument and Control Engineers. T. Terano (B) Chiba University of Commerce, Chiba 272-8512, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_9
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focusing case analyses and implications from traditional activities. There are surprisingly few studies that discuss the nature of innovation from a systems science perspective. For example, in Artificial Intelligence literature, they only discuss how advanced artificial intelligence technologies are used for innovation from a technical perspective, but do not discuss the nature of innovation, itself. In this chapter, we introduce the nature of innovation, which is derived from the observations of biological systems and their evolutionary mechanisms, as well as the similarities and properties of innovation. We introduce the contents of the representative four books, all of which apply the framework of complex adaptive systems and evolutionary computation, which have been developing rapidly in recent years. By introducing these, we would like to get some hints on how to achieve “open innovation” or “free innovation”. The structure of this paper is as follows. First,
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