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We decided to write and edit this book – a cooperative effort, involving two main authors and more than 20 co-authors – in order to show how the micro-theories of knowledge creation, developed and elaborated in the book , can contribute to a further development of , of a contemporary infrastructure for creativity, including both business-oriented creativity at companies and science-oriented creativity in academia. Before we explain the concept of in more detail, we must recall some conclusions from and comment on our reflections since the publication of that book.

Knowledge discovery, possession, handling, and enhancement are issues of increasing importance and actuality in contemporary society. In order to sustain competitive competencies, new knowledge and technologies are required ever more quickly by individuals, organisations, even nations. Thus, the creation of knowledge and technology has attracted increasing attention in scientific research and practice. This attention is also explained by a shift of emphasis from purely to , which requires even more concentration on creativity.

This chapter is organised as follows. In these introductory remarks, we discuss also the divergence of the contemporary episteme and the need of a reflection on the contemporary situation in knowledge management, technology management, as well as the emergence of . Then we discuss the issues of emerging knowledge sciences in more detail. We substantiate the need for a prescriptive synthesis of normal knowledge creation processes with diverse organisational knowledge creation processes and introduce the idea of the Nanatsudaki Model. Then we proceed to the more detailed description of the concept of the Nanatsudaki Model, followed by comments on its consecutive parts. We discuss the relation of the Nanatsudaki Model to the survey of knowledge creation support described in the preceding chapter and add conclusions.

A critical problem in the development of knowledge-based systems is capturing knowledge from the experts. There are many knowledge elicitation techniques that might aid this process, but the fundamental problem remains: tacit knowledge that is normally implicit, inside the expert's head, must be externalised and made explicit. Knowledge acquisition (KA) thus has been well recognised as a bottleneck in the development of knowledgebased systems and is a key issue in knowledge engineering. Traditionally, KA techniques can be grouped into three categories: manual, semi-automated (interactive) and automated (machine learning (ML) and data mining). Since the early days of artificial intelligence (AI), the problem of KA, the elicitation of expert knowledge in building knowledge bases, has been recognised as a fundamental issue in knowledge engineering.

This chapter begins with a short review of the history of the concept of brainstorming. Models of the brainstorming process are recalled, including the counter-positions of group brainstorming and individual brainstorming. Existing software for brainstorming support is reviewed, with special attention paid to Japanese developments in this field. New development directions for brainstorming support are indicated; they mostly concern the issue of how to combine organisational knowledge creation with normal academic knowledge creation. Conclusions finalise the chapter.

Debate, or intersubjective deliberation, is one of the oldest and most basic human activities. It is much older and often essentially different than brainstorming, because the aim of debate is to critically select ideas that are useful. In China, it is often said “the more truth is debated, the clearer it becomes”; the objective of debate is . The debate has long history in both West and East, especially in China; however, Western culture has concentrated more on the logical and rational aspects of thought, and less on its intuitive, preverbal, image-related aspects. In Occidental culture, debate is even taught as an important course in highschool education, because debate is considered to be one of the fundamental aspects of democracy (such as a parliamentary debate). It also has an extensive literature, starting with Plato and extending to contemporary . Eristic and democratic debate concentrates, however, on the political aspects of convincing the audience that the adversary is not right.

Today the term “roadmap” is used liberally by planners in many different types of communities. It appears to have a multiplicity of meanings, and is used in a wide variety of contexts: by commercial organizations, industry associations, governments, and academia, see, e.g., Kostoff and Schaller (2001). Perhaps the most widely accepted definition of a roadmap was given by Robert Galvin, former CEO of Motorola (Galvin 1998):

Creative environments comprise the contemporary infrastructure of creativity, including both business-oriented creativity at companies and scienceoriented creativity in academia. Many companies today use diverse forms of knowledge management that aim to organize and aid the process of knowledge creation. However, future creative environments should go a step further: They should become tools for supporting creativity. To this end, these environments can utilize the findings of contemporary microtheories of knowledge creation (Wierzbicki and Nakamori 2006a).

The main issue addressed by this chapter is an interpretation of statistics: is it just a functionalist set of tools for diverse measurements, or is it a toolbox for supporting creativity? We represent the latter opinion and try to describe our reasons for it.

This chapter is organised as follows. After these introductory remarks, we review the concept of statistics as the grammar of technology development. We follow with lessons from applications of statistical tools for quality control in Japan. Methods for the statistical design of experimentsand their importance in applications are reviewed. The extension of theseapproaches of statistical experiment design to support experimental research in scientific and technological laboratories is discussed. These tools of designing experiments can be also modified for application in virtual laboratories.

The term virtual laboratory consists of two words that might appear, for many readers, to have contradictory characteristics. Thus, we shall first explain why we use this term.

A is most commonly understood as a place equipped for scientific research, experiments, and testing. Until the proliferation of Internet, a laboratory was associated with a work site (from a dedicated room to a complex of buildings) at a single location. At universities, laboratory also can mean either a practical component accompanying a lecture, or a classroom where practical demonstrations and exercises take place. Scientific laboratories (particularly in engineering, physics, chemistry, and medicine) are characterised by a controlled uniformity of procedures and conditions (such as cleanliness, temperature, humidity) as well as by sophisticated instruments necessary for advanced studies. Because laboratories have been considered a basic element supporting scientific research, the word laboratory also has acquired a metaphorical organisational meaning: it might refer to an elementary division of a scientific organisation, or a part of a faculty or a school devoted to a specific field of research. This metaphorical meaning is frequently used, for example, in Japanese universities. In this chapter we discuss the meaning of a laboratory in its more traditional sense. However, in all these situations the word laboratory is commonly associated with being a real place as well as being thoroughly defined and organised.