
Creative Environments
Issues of Creativity Support for the Knowledge Civilization Age
By: Andrzej P. Wierzbicki (Editor), Yoshiteru Nakamori (Editor)
Hardcover | 3 July 2007
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532 Pages
22.86 x 15.88 x 3.18
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"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.
Industry Reviews
From the reviews:
"This book demonstrates the numerous types of reciprocal relationships between knowledge, creativity, and documentation ... and many more cognitive/intellectual inter-dependencies. ... The style of presentation (layout and format) is superb because of the many diagrams/graphics and various types, tables and models ... . as all professions deal with knowledge, creativity, innovation, data bases, documentation, [...] there is in reality no limit to the book's appeal: all theoretical/abstract and pragmatic/applied data are fundamentally highly relevant." (Karl H. Wolf, Journal of Documentation, Vol. 65 (3), 2009)
| Basic Models of Creative Processes | |
| Preliminaries | p. 3 |
| Introductory Remarks | p. 3 |
| Conclusions from Creative Space | p. 3 |
| Descriptive and Prescriptive Models of Knowledge Creation | p. 10 |
| Ba and Creative Environments | p. 11 |
| Philosophical Questions | p. 13 |
| Knowledge Management and Creative Holism | p. 13 |
| Technology and Change | p. 14 |
| The Processes of Emergence of Basic Concepts and the Problem of a New Episteme in Knowledge Civilisation | p. 16 |
| The Contents of this Book | p. 18 |
| Acknowledgements | p. 23 |
| Testing the Triple Helix Model | p. 25 |
| Introductory Remarks | p. 25 |
| Knowledge Creation Processes | p. 27 |
| A Survey of Scientific Creativity Support | p. 30 |
| Background and Goals | p. 30 |
| Questionnaire Design | p. 31 |
| Study Instruments | p. 32 |
| Analysis of Survey Results | p. 32 |
| Reference Profiles and Achievement Functions | p. 34 |
| The Application of Reference Profiles in Our Research | p. 37 |
| Survey Findings | p. 40 |
| Discussion | p. 42 |
| Conclusions | p. 44 |
| Knowledge Sciences and JAIST Nanatsudaki Model | p. 47 |
| Introductory Remarks | p. 47 |
| Knowledge Management versus Technology Management | p. 48 |
| The Emergence of Knowledge Sciences | p. 50 |
| The Need for a Prescriptive Synthesis of Knowledge Creation Processes | p. 52 |
| The Nanatsudaki Model | p. 54 |
| Objective Setting | p. 56 |
| Hermeneutics | p. 58 |
| Socialization | p. 59 |
| Brainstorming | p. 60 |
| Debate | p. 61 |
| Roadmapping | p. 62 |
| Implementation: Experimental Work | p. 63 |
| Closure: A Different Cycle of the Entire Process | p. 64 |
| Relation to Experimental Results | p. 65 |
| Conclusions | p. 66 |
| Tools for Supporting Basic Creative Processes | |
| Knowledge Acquisition by Machine Learning and Data Mining | p. 69 |
| Introductory Remarks | p. 69 |
| Machine Learning, Knowledge Discovery and Data Mining | p. 71 |
| Examples of Progress in Machine Learning and Data Mining | p. 78 |
| Scientific Data Mining | p. 79 |
| Mining Medical Data | p. 81 |
| Mining Genomic and Proteomic Data | p. 83 |
| Mining Materials Science Data | p. 84 |
| Experiences of Data Mining in Telecommunications | p. 85 |
| An Example of Complex Interaction Process | p. 87 |
| Event Mining | p. 89 |
| Exchanging Tacit Knowledge | p. 91 |
| Conclusions | p. 91 |
| Creativity Support in Brainstorming | p. 93 |
| Contents and Introductory Remarks | p. 93 |
| The Meaning and the History of Brainstorming | p. 93 |
| Models of the Brainstorming Process | p. 95 |
| Software for Brainstorming Support | p. 99 |
| The KJ Method and Creative Problem Solving Systems | p. 100 |
| GRAPE Decision Support Groupware | p. 103 |
| Support System for Consensus Making: Group Coordinator | p. 108 |
| Novel Approaches to Brainstorming Support | p. 115 |
| The Use of Brainstorming in Normal Academic Knowledge Creation | p. 116 |
| The Enrichment of Brainstorming by Normal Academic Creative Processes | p. 119 |
| Concluding Remarks | p. 125 |
| Debating and Creativity Support | p. 127 |
| Introduction | p. 127 |
| Existing Software for Supporting Debate and Knowledge Creation | p. 129 |
| General Groupware | p. 129 |
| Specific Software or Platforms for Debate and Knowledge Creation | p. 130 |
| PathMaker | p. 130 |
| Group Argumentation Environment (GAE) | p. 134 |
| Electronic Common Brain (ECB) | p. 146 |
| Rational and A-rational Aspects of Debate and Related Software Requirements | p. 148 |
| Conclusions | p. 153 |
| Creativity Support for Roadmapping | p. 155 |
| Introductory Remarks and Contents | p. 155 |
| Science and Technology Roadmaps | p. 156 |
| Roadmapping as a Knowledge Creation Process | p. 161 |
| I-System and Knowledge Creation Support in Roadmapping | p. 163 |
| Intervention | p. 165 |
| Intelligence | p. 165 |
| Involvement | p. 166 |
| Imagination | p. 167 |
| Integration | p. 169 |
| General Features of Information Technology Support for Roadmapping | p. 169 |
| Case Studies - Making Academic Research Roadmaps in JAIST | p. 170 |
| An Interactive Planning (IP) - Based Roadmapping Approach | p. 172 |
| A Web-based Roadmapping Support System | p. 178 |
| Experience in Applications of Roadmapping at JAIST | p. 181 |
| Individual Research Roadmaps | p. 181 |
| Case Study: Roadmaps for Development of Fuel-Cell Technology | p. 183 |
| Conclusions | p. 188 |
| Integrated Support for Scientific Creativity | p. 191 |
| Introduction | p. 191 |
| User Requirements for a CE | p. 192 |
| Models of Creative Processes | p. 194 |
| Three Models of Knowledge Creation | p. 194 |
| Nanatsudaki Model | p. 195 |
| Experiences with Implementation of CE Prototypes | p. 195 |
| Creative Environment at JAIST | p. 196 |
| SCI-Blog: a Prototype CE at PJIIT | p. 199 |
| Scenarios of User Interaction with a CE | p. 201 |
| Planning a New Research Project | p. 201 |
| Searching for Related Work | p. 202 |
| Describing and Sharing Read Literature | p. 203 |
| Review of Other Users' Work | p. 203 |
| Seminar Discussions | p. 203 |
| Planning an Experiment | p. 204 |
| Modular Architecture of a CE | p. 204 |
| Personal Workspace Module | p. 205 |
| Information Retrieval Module | p. 205 |
| Group Communication Module | p. 206 |
| Planning and Roadmapping Module | p. 207 |
| Experiment Module | p. 208 |
| Data Representation and Metadata in a CE | p. 209 |
| Database Structure of a CE | p. 209 |
| RDF/XML File Repositories for Semantic Web Documents | p. 210 |
| Security of Information in a CE | p. 211 |
| Authentication and Privacy | p. 211 |
| Access Control | p. 212 |
| Evaluation of Creative Environments | p. 212 |
| Conclusions | p. 213 |
| Diverse Tools Supporting Creative Processes | |
| Statistics for Creativity Support | p. 217 |
| Introductory Remarks | p. 217 |
| The Grammar of Technology Development | p. 217 |
| Lessons from Applications of Statistical Tools for Quality Control | p. 218 |
| Statistical Experiment Design | p. 222 |
| Orthogonal Experiment Design and Its Applications | p. 222 |
| History of Statistical Experiment Design and the Taguchi Method | p. 227 |
| A Quadratic Response Surface Approximation | p. 228 |
| Possibilities of Creativity Support and Conclusions | p. 230 |
| Virtual Laboratories | p. 233 |
| Introductory Remarks | p. 233 |
| Knowledge-based Problem Solving | p. 234 |
| Knowledge Integration | p. 237 |
| Models for Knowledge Integration and Creation | p. 237 |
| Knowledge Integration in Models | p. 239 |
| Collaborative Modelling | p. 241 |
| Model Specification | p. 242 |
| Data | p. 242 |
| Model Analysis | p. 244 |
| Virtual Organisations | p. 244 |
| Laboratory World | p. 246 |
| Knowledge Creation by Model Analysis | p. 247 |
| Model-based Problem Solving | p. 247 |
| Modelling Technology | p. 248 |
| Model Analysis | p. 249 |
| Structured Modelling Technology (SMT) | p. 251 |
| Conclusions: Virtual Modelling Laboratories | p. 253 |
| Gaming and Role Playing as Tools for Creativity Training | p. 255 |
| Introductory Remarks | p. 255 |
| Current Directions in Gaming, Negotiation, and Game Theory | p. 256 |
| Gaming in Business Education | p. 258 |
| What is the Aim of Gaming Simulation? | p. 258 |
| Gaming Simulation Efforts in a Business School | p. 259 |
| Procedure of the Gaming Simulation | p. 260 |
| Macro-cycle and Micro-cycle | p. 260 |
| Experiences in Gaming Simulations | p. 261 |
| Significance of Gaming Simulation at a Business School | p. 262 |
| Development of Business Simulation Exercises | p. 263 |
| Relations in Gaming Simulation: Facilitator and Designer | p. 264 |
| Gaming Simulation and Knowledge Creation | p. 265 |
| Role Playing and Negotiations for Problem Solving and Idea Formation | p. 266 |
| Basic Concepts of Coalition Game Theory | p. 266 |
| Usual Reference Points | p. 268 |
| Achievement Functions and Reference Point Approach | p. 270 |
| Special Reference Points | p. 272 |
| The Case of Empty and Extended Core | p. 274 |
| Example: Negotiating a Merger of High-tech Firms | p. 275 |
| Lessons from the Examples and Simulated Negotiations | p. 278 |
| Conclusions | p. 279 |
| Knowledge Representation and Multiple Criteria Aggregation | p. 281 |
| Introduction: the Need for Knowledge Representation and Integration | p. 281 |
| Knowledge Definitions | p. 283 |
| Representing Knowledge in Logical Form | p. 285 |
| Production (Decision) Rules | p. 285 |
| Decision Tables | p. 287 |
| Decision Trees | p. 289 |
| Representing Knowledge in Structural Form | p. 290 |
| Networks | p. 290 |
| Frames | p. 292 |
| Description Logics | p. 293 |
| The Problem of Integration of Knowledge | p. 294 |
| Multiple Criteria Aggregation for Knowledge Integration | p. 295 |
| An Approach to Multiple Criteria Aggregation, Ranking and Classification | p. 297 |
| Compensatory vs. Noncompensatory Criteria, Subjective vs. Objective Ranking | p. 302 |
| Hierarchical Aggregation of Criteria | p. 306 |
| Example of Six Divisions of a Corporation | p. 307 |
| Multiple-Attribute Aggregation under Uncertainty for Decision Making | p. 309 |
| Problem Description | p. 310 |
| Evaluation Analysis Model | p. 313 |
| Dempster-Shafer Theory of Evidence | p. 314 |
| The ER Approach for Attribute Aggregation | p. 315 |
| From Extended Decision Matrix to Evaluation Matrix | p. 318 |
| Conclusions | p. 319 |
| Distance and Electronic Learning | p. 321 |
| Introductory Remarks | p. 321 |
| The Role of Electronic and Distance Learning and Teaching in the Knowledge Civilisation Era | p. 322 |
| Current Achievements and Trends of Electronic and Distance Learning | p. 325 |
| Types of e-Learning | p. 325 |
| The Characteristics of e-Learning in a Narrow Sense | p. 326 |
| Searching for a Better Combination of e-Learning Technologies | p. 328 |
| The Importance of Education Strategy in an Organisation: the Concept of a Learning Organisation | p. 330 |
| Integrated Evolutionary Learning Model from a Practical Point of View | p. 331 |
| Establishment of Learning Strategy | p. 331 |
| What Should Be Learned? | p. 331 |
| Evolutionary Cycling | p. 333 |
| Conceptual Model of Integrated Evolutionary Learning | p. 333 |
| Market Driven Development vs. Long Term Trends | p. 334 |
| Current Trends and Problems of Multimedia Technology | p. 336 |
| Ambient Intelligence vs. Electronic Learning | p. 338 |
| Features of Intelligent Tutoring Systems and Commercial Standards | p. 340 |
| SLIT: A Conceptual Model of an Intelligent Tutoring System | p. 342 |
| The Use of Data Mining in Intelligent Tutoring Systems | p. 344 |
| Course Model, Log Files and Decision Tables | p. 345 |
| Virtual Students for Testing the Effectiveness of Data Mining Methods | p. 346 |
| Simulations Conditions and Test Results | p. 348 |
| Conclusions: Creativity Support vs. Electronic Learning | p. 349 |
| Knowledge Management and Philosophical Issues of Creativity Support | |
| Management of Technology in Academic Research | p. 353 |
| Introduction | p. 353 |
| What is Management of Technology (MOT)? | p. 354 |
| Establishment of MOT Courses at JAIST | p. 357 |
| Development of the Foundations of MOT | p. 360 |
| Development of MOST | p. 361 |
| The Significance of MOST: from Implicit to Explicit Knowledge | p. 363 |
| Experiences and Problems with MOST | p. 365 |
| Conclusions | p. 368 |
| Knowledge Management and Creative Holism in the Knowledge Age | p. 369 |
| Introduction | p. 369 |
| Creative Holism - Basic Concepts | p. 371 |
| The Implication of Knowledge in Organisations | p. 373 |
| Static Substance Knowledge | p. 375 |
| Dynamic Process Knowledge | p. 377 |
| Knowledge Management, Creative Holism, and Creative Space | p. 378 |
| Conclusions | p. 383 |
| Technology and Change: The Role of Technology in the Knowledge Civilization Era | p. 385 |
| Introductory Remarks | p. 385 |
| The Big Change in Last Fifty Years | p. 386 |
| The Era of Knowledge Civilization | p. 387 |
| Diverse Perceptions of a New Era | p. 387 |
| The Conceptual Platform and the Episteme of a Civilisation Era | p. 388 |
| What Happened at the End of the Industrial Civilization Era | p. 391 |
| The Three Separate Spheres of Technology, Hard Sciences and Social Sciences with Humanities | p. 393 |
| Why Separate Spheres? | p. 393 |
| The Dominant Episteme of a Sphere and Its Limitations | p. 395 |
| The Views of Philosophy of Technology | p. 396 |
| The General Impression of a Technologist | p. 396 |
| A Few Acceptable Views | p. 397 |
| The Dangers of Misunderstandings | p. 398 |
| How Social Sciences and Humanities Present the Episteme of Hard Sciences and of Technology | p. 398 |
| Theories of Instructional Design | p. 399 |
| Soft vs. Hard Systems Thinking | p. 402 |
| Post-modern Social Science and Sociology of Science | p. 404 |
| What Technology Is and What It Is Not | p. 406 |
| The Definition of Technology by Heidegger as Understood By a Technologist | p. 406 |
| The Warnings of Heidegger as Understood By a Technologist | p. 406 |
| The Sovereign though not Autonomous Position of Technology | p. 407 |
| The Reverse Relation of Science and Technology | p. 408 |
| Two Positive Feedback Loops | p. 410 |
| What Will Be the Technology of the Knowledge Era | p. 413 |
| Some Examples of Technology of the Knowledge Era | p. 414 |
| New Warnings: What We Must Be Careful About | p. 415 |
| Conclusions | p. 415 |
| The Emergence of New Concepts in Science | p. 417 |
| Introductory Remarks | p. 417 |
| Conceptual and Scientific Change | p. 418 |
| Mathematical Intuition and Platonism in Mathematics. The Idea of the Reconstruction of the Hermeneutical Horizon | p. 420 |
| Platonism and Hermeneutical Conditions for Emergence of Concepts | p. 425 |
| An Example of Emergence of Concepts in Mathematics | p. 427 |
| The Ancient Intuitive Model of Euclidean Geometry | p. 429 |
| The Emergence of Absolute Space | p. 432 |
| The Intuitive Analysis of Concepts | p. 433 |
| The Schema of the Intuitive Analysis of Concepts | p. 437 |
| Conclusions and Remarks | p. 442 |
| Summary and Conclusions | p. 445 |
| Introductory Remarks | p. 445 |
| Summary of Contributions | p. 445 |
| The Emergence of an Integrated Episteme of the Knowledge Civilisation Era | p. 456 |
| What Technology and Hard Science Can Proposeas an Emerging Episteme of the Knowledge Civilisation Era | p. 457 |
| Constructive Evolutionary Objectivism | p. 462 |
| The Problem of Truth in the Knowledge Era | p. 464 |
| Concluding Remarks | p. 466 |
| References | p. 469 |
| Index | p. 497 |
| Table of Contents provided by Publisher. All Rights Reserved. |
ISBN: 9783540714668
ISBN-10: 3540714669
Series: Studies in Computational Intelligence
Published: 3rd July 2007
Format: Hardcover
Language: English
Number of Pages: 532
Audience: Professional and Scholarly
Publisher: Springer Nature B.V.
Country of Publication: DE
Dimensions (cm): 22.86 x 15.88 x 3.18
Weight (kg): 0.89
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