"Creative Environments" is a follow-up on the book Creative Space in the same series and by the same authors, serving this time as editors of a broader book on computational intelligence and knowledge engineering tools for supporting knowledge creation. This book contains four parts. The first part presents a further development of models of knowledge creation presented already in Creative Space, in particular the Triple Helix of normal academic knowledge creation and a new, integrated model of normal academic and organizational knowledge creation, called Nanatsudaki (seven waterfalls) Model. The second part presents computational intelligence tools for knowledge acquisition by machine learning and data mining, for debating, brainstorming, for roadmapping and for integrated support of academic creativity. The third part presents the use of statistics for creativity support, virtual laboratories, gaming and role playing for creativity support, methods of knowledge representation and multiple criteria aggregation, distance and electronic learning. The last part addresses knowledge management and philosophical issues and contains chapters: on management of technology and knowledge management for academic R and D; on knowledge management and creative holism or systems thinking in the knowledge age; on technology and change or the role of technology in knowledge civilisation; on the emergence of complex concepts in science; and the final chapter on summary and conclusions, including a proposal of an integrated episteme of constructive evolutionary objectivism, necessary for the knowledge civilization age.

This book provides readers the idea of systemically synthesizing various kind of knowledge, which needs to combine analytical thinking and synthetic thinking. Systems science is expected to help in solving contemporary complex problems, utilizing interdisciplinary knowledge effectively and combining analytical thinking and synthetic thinking efficiently. However, traditional systems science has been divided into two schools: one seeks a systematic procedure to give a correct objective answer; the other develops an emergent, systemic process so that the user can continue exploratory learning. It is not an exaggeration to say that analytical thinking and synthetic thinking have been developed independently, in different schools. This book integrates approaches developed in these two schools, using ideas in knowledge science that have been emerging recently under the influence of Eastern thinking. It emphasizes the importance of utilizing intuition in systems approaches, whereas other books usually try to solve problems rationally and objectively, rejecting subjectivity. This book never denies rationality and objectivity; however, complex problems of today do not always yield to complete analysis. The novelty of this present volume is that it takes in the ideas of synthetic thinking in knowledge science to develop systems science further. The chapter contributors, who are experienced systems scientists with a profound understanding of knowledge management, discuss knowledge synthesis from the Western and Eastern cultural perspectives. The book introduces a theory on systemic knowledge synthesis in an odd chapter and then presents an application of the theory in the next chapter in order to contribute to developing translational systems science. 

This book constitutes the refereed proceedings of the 17th International Symposium, KSS 2016, held in Kobe, Japan, in November 2016.

The 21 revised full papers presented were carefully reviewed and selected from 48 submissions. The papers cover topics such as: Algorithms for Big Data; Big Data and education; Big Data and healthcare; Big Data and tourism; Big Data and social media oriented knowledge discovery and data mining, text mining, recommendation system, etc; Big Data, social media and societal management; creation of agent-based social systems sciences; collective intelligence; complex system modeling and complexity; decision analysis and decision support systems; internet+ and agriculture; internet+ and open innovation; knowledge creation, creativity support, awareness support, etc.; knowledge systems engineering and knowledge management; meta-synthesis and advanced modeling; opinion dynamics and opinion mining; OR on knowledge and systems sciences; problem structuring methods and system methodologies toward wicked problems; service systems science; smart city; social dynamic network modeling; Web intelligence.

This book demonstrates that innovative ideas are systematically constructed in the creative space spanned by the dimensions of systems thinking and knowledge management. Readers will be introduced to this proposition in the final chapter, after learning about the key innovation theories, design thinking, systems thinking, and idea creation methods in systems science and knowledge science.

This book reviews creative technologies that underpin the human activities of converting data and information into knowledge, creating new ideas based on that knowledge, and validating those ideas. The book calls such creative technology “knowledge technology” and explores its challenges in the age of big data. Today, artificial intelligence and big data are successfully performing pattern recognition and decision making on behalf of humans, who are incapable of processing large amounts of information instantly and accurately.

In using only past data, however, there are limits to future predictions with artificial intelligence and big data. Humans need to take the lead in predicting or creating the future by trying to understand why data emerged. Knowledge technology contributes to that understanding, regardless of the field of origin. As a matter of fact, knowledge technology includes many technologies developed in informatics, management study, and systems science. The desirable features of knowledge technology are the complementary use of rational and intuitive approaches, the integration of results from quantitative and qualitative analyses, and the cooperation between artificial intelligence and humans.

This book discusses technology and systems to create valuable ideas from data through the construction of knowledge. The primary concern is to make better decisions about economic and management issues in today’s information-flooded society. Human creative activity is in the realm of soft technology, with no physical entity to operate. Focusing on the ability of knowledge as judgment power, this definition results: “Knowledge technology is soft technology that underpins the human creative activities of converting data and information into knowledge, creating new ideas based on that knowledge and validating those ideas.” That definition includes a wide range of soft technologies developed in informatics, management studies, and systems science.

The knowledge system creates ideas from data and knowledge through knowledge technologies. Based on the proposition that knowledge emerges by the interaction between explicit and tacit knowledge, another definition is possible: “The knowledge system is a system that promotes interaction between codified and personalized knowledge and creates ideas for solving a specific problem.” Codified knowledge includes data and information, while personalized knowledge is empirical knowledge or wisdom that is difficult to put into words.

Building a knowledge system requires mathematical or intelligent knowledge technology and participatory knowledge technology to create or manage codified knowledge and personalized knowledge. For example, a company builds cross-sectional knowledge systems by gathering human resources from various departments, according to the purpose, as in new product development or sales promotion. Chapter 1 defines knowledge technology and the knowledge system and organizes the challenges in their development, while Chapters 2 through 9 introduce mathematical or intelligent knowledge technologies by researchers at the forefront of knowledge technology development.

"Creative Environments" is a follow-up on the book Creative Space in the same series and by the same authors, serving this time as editors of a broader book on computational intelligence and knowledge engineering tools for supporting knowledge creation. This book contains four parts. The first part presents a further development of models of knowledge creation presented already in Creative Space, in particular the Triple Helix of normal academic knowledge creation and a new, integrated model of normal academic and organizational knowledge creation, called Nanatsudaki (seven waterfalls) Model. The second part presents computational intelligence tools for knowledge acquisition by machine learning and data mining, for debating, brainstorming, for roadmapping and for integrated support of academic creativity. The third part presents the use of statistics for creativity support, virtual laboratories, gaming and role playing for creativity support, methods of knowledge representation and multiple criteria aggregation, distance and electronic learning. The last part addresses knowledge management and philosophical issues and contains chapters: on management of technology and knowledge management for academic R and D; on knowledge management and creative holism or systems thinking in the knowledge age; on technology and change or the role of technology in knowledge civilisation; on the emergence of complex concepts in science; and the final chapter on summary and conclusions, including a proposal of an integrated episteme of constructive evolutionary objectivism, necessary for the knowledge civilization age.

This book provides readers the idea of systemically synthesizing various kind of knowledge, which needs to combine analytical thinking and synthetic thinking. Systems science is expected to help in solving contemporary complex problems, utilizing interdisciplinary knowledge effectively and combining analytical thinking and synthetic thinking efficiently. However, traditional systems science has been divided into two schools: one seeks a systematic procedure to give a correct objective answer; the other develops an emergent, systemic process so that the user can continue exploratory learning. It is not an exaggeration to say that analytical thinking and synthetic thinking have been developed independently, in different schools. This book integrates approaches developed in these two schools, using ideas in knowledge science that have been emerging recently under the influence of Eastern thinking. It emphasizes the importance of utilizing intuition in systems approaches, whereas other books usually try to solve problems rationally and objectively, rejecting subjectivity. This book never denies rationality and objectivity; however, complex problems of today do not always yield to complete analysis. The novelty of this present volume is that it takes in the ideas of synthetic thinking in knowledge science to develop systems science further. The chapter contributors, who are experienced systems scientists with a profound understanding of knowledge management, discuss knowledge synthesis from the Western and Eastern cultural perspectives. The book introduces a theory on systemic knowledge synthesis in an odd chapter and then presents an application of the theory in the next chapter in order to contribute to developing translational systems science. 

This book constitutes the refereed proceedings of the 17th International Symposium, KSS 2016, held in Kobe, Japan, in November 2016.

The 21 revised full papers presented were carefully reviewed and selected from 48 submissions. The papers cover topics such as: Algorithms for Big Data; Big Data and education; Big Data and healthcare; Big Data and tourism; Big Data and social media oriented knowledge discovery and data mining, text mining, recommendation system, etc; Big Data, social media and societal management; creation of agent-based social systems sciences; collective intelligence; complex system modeling and complexity; decision analysis and decision support systems; internet+ and agriculture; internet+ and open innovation; knowledge creation, creativity support, awareness support, etc.; knowledge systems engineering and knowledge management; meta-synthesis and advanced modeling; opinion dynamics and opinion mining; OR on knowledge and systems sciences; problem structuring methods and system methodologies toward wicked problems; service systems science; smart city; social dynamic network modeling; Web intelligence.

This book demonstrates that innovative ideas are systematically constructed in the creative space spanned by the dimensions of systems thinking and knowledge management. Readers will be introduced to this proposition in the final chapter, after learning about the key innovation theories, design thinking, systems thinking, and idea creation methods in systems science and knowledge science.

This book reviews creative technologies that underpin the human activities of converting data and information into knowledge, creating new ideas based on that knowledge, and validating those ideas. The book calls such creative technology “knowledge technology” and explores its challenges in the age of big data. Today, artificial intelligence and big data are successfully performing pattern recognition and decision making on behalf of humans, who are incapable of processing large amounts of information instantly and accurately.

In using only past data, however, there are limits to future predictions with artificial intelligence and big data. Humans need to take the lead in predicting or creating the future by trying to understand why data emerged. Knowledge technology contributes to that understanding, regardless of the field of origin. As a matter of fact, knowledge technology includes many technologies developed in informatics, management study, and systems science. The desirable features of knowledge technology are the complementary use of rational and intuitive approaches, the integration of results from quantitative and qualitative analyses, and the cooperation between artificial intelligence and humans.

This book discusses technology and systems to create valuable ideas from data through the construction of knowledge. The primary concern is to make better decisions about economic and management issues in today’s information-flooded society. Human creative activity is in the realm of soft technology, with no physical entity to operate. Focusing on the ability of knowledge as judgment power, this definition results: “Knowledge technology is soft technology that underpins the human creative activities of converting data and information into knowledge, creating new ideas based on that knowledge and validating those ideas.” That definition includes a wide range of soft technologies developed in informatics, management studies, and systems science.

The knowledge system creates ideas from data and knowledge through knowledge technologies. Based on the proposition that knowledge emerges by the interaction between explicit and tacit knowledge, another definition is possible: “The knowledge system is a system that promotes interaction between codified and personalized knowledge and creates ideas for solving a specific problem.” Codified knowledge includes data and information, while personalized knowledge is empirical knowledge or wisdom that is difficult to put into words.

Building a knowledge system requires mathematical or intelligent knowledge technology and participatory knowledge technology to create or manage codified knowledge and personalized knowledge. For example, a company builds cross-sectional knowledge systems by gathering human resources from various departments, according to the purpose, as in new product development or sales promotion. Chapter 1 defines knowledge technology and the knowledge system and organizes the challenges in their development, while Chapters 2 through 9 introduce mathematical or intelligent knowledge technologies by researchers at the forefront of knowledge technology development.