
Creative Environments
by Wierzbicki, Andrzej P.; Nakamori, Yoshiteru-
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Summary
Table of Contents
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 |
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