Operationalizing Sustainability

<p>Note to all Contributors xv</p>
<p>Note to the Reader xvii</p>
<p>List of Acronyms xxi</p>
<p>Introduction xxvii</p>
<p>Part 1. Sustainability: Toward the Unification of Some Underlying Principles and Mechanisms 1</p>
<p>Chapter 1. Toward a Sustainability Science 3</p>
<p>1.1. Introduction 3</p>
<p>1.2. What does unification mean? 4</p>
<p>1.3. Coming back to sustainability: how many sustainabilities ? 7</p>
<p>1.4. Sustainability: what kind of unification? An integration issue? 10</p>
<p>1.5. What kind of paradigm do we have to integrate? 12</p>
<p>1.6. The issue and the implementation of a new dimension 14</p>
<p>1.6.1. Preamble: code of matter, power of laws and balance of powers 14</p>
<p>1.6.2. The addition of a new dimension: gimmick or necessity? 16</p>
<p>1.6.3. Integration of time and dynamics 17</p>
<p>1.6.4. Application 19</p>
<p>1.7. Extensions of the concept 20</p>
<p>1.7.1. Comments 20</p>
<p>1.7.2. Life sciences: power laws, evolution, life and death phenomena 21</p>
<p>1.7.3. The power laws 24</p>
<p>Chapter 2. Sustainability in Complex Systems 29</p>
<p>2.1. Preamble: theories of interconnected systems 29</p>
<p>2.2. Analysis of feedback phenomena in an assembly manufacturing cell 30</p>
<p>2.2.1. Preliminary considerations 30</p>
<p>2.2.2. Case study 1: modeling the limitation of work in progress (WIP) by a threshold called MAQ 32</p>
<p>2.2.3. Case study 2: modeling the WIP through assignment rules 33</p>
<p>2.2.4. Case study 3: model building based on dynamic management of bottlenecks 34</p>
<p>2.2.5. Main comments 36</p>
<p>2.3. Application to complex systems: quantitative characteristics of a deterministic chaos 37</p>
<p>2.3.1. Introduction 37</p>
<p>2.3.2. Quantification of state variables in a production system 39</p>
<p>2.4. General considerations about interactions in networked organizations 39</p>
<p>2.5. Role of feedback in mimicry and ascendancy over others 41</p>
<p>2.6. Network theory: additional characteristics due to their new structure 43</p>
<p>2.6.1. Mycorrhization networks 46</p>
<p>2.7. Simplexification 47</p>
<p>2.8. Convergences in network theory 51</p>
<p>Chapter 3. Extension: From Complexity to the Code of Thought 53</p>
<p>3.1. The code of thought: effects of cognition and psyche in global sustainability 53</p>
<p>3.2. Is sustainability the only technological and technocratic approach? 56</p>
<p>3.3. The three laws of sustainability: prediction and anticipation in complex systems 57</p>
<p>3.3.1. Is sustainability a consistent property of any complex system? 58</p>
<p>3.3.2. Sustainability is also the art of combining paradoxes 59</p>
<p>3.3.3. Adaptation of a manufacturing process: what is so important in planning and scheduling? 59</p>
<p>3.3.4. Predicting the future? Is it a necessity? 60</p>
<p>3.4. Consequence: toward a new dimension 63</p>
<p>3.5. Conclusion 64</p>
<p>3.6. Indicators for monitoring the EU sustainable development strategy 65</p>
<p>Part 2. Operationalization: Methods, Techniques and Tools the Need to Manage the Impact 69</p>
<p>Chapter 4. From Context to Knowledge: Building Decision–making Systems 71</p>
<p>4.1. Introduction 71</p>
<p>4.1.1. In the back part of the brain, there is the cerebellum 72</p>
<p>4.1.2. In the temporal lobe of the cerebrum and limbic system 73</p>
<p>4.1.3. The frontal lobe of the cerebrum (frontal neocortex) 73</p>
<p>4.2. How about obtaining a sustainable knowledge? 74</p>
<p>4.2.1. The first question: how do we learn from experience? 74</p>
<p>4.2.2. The second question: how do we learn from experiences and develop a conceptual understanding? 75</p>
<p>4.2.3. The third question: how do we model a sustainable information and knowledge processing system? 76</p>
<p>4.3. Preliminary consideration: the nature of the problems encountered in test and diagnosis 77</p>
<p>4.3.1. The world of industry 78</p>
<p>4.3.2. Health and medical care 78</p>
<p>4.3.3. Consequences 80</p>
<p>4.4. Preamble: basic concepts for creating knowledge 80</p>
<p>4.4.1. Description of the basic reasoning techniques 80</p>
<p>4.4.2. Conventional collaborative techniques for creating knowledge 81</p>
<p>4.5. Retroduction and abduction 83</p>
<p>4.5.1. The retroduction technique 84</p>
<p>4.5.2. The abduction technique 86</p>
<p>4.6. Deduction and induction 87</p>
<p>4.6.1. The inductive reasoning technique 88</p>
<p>4.6.2. Linear characteristics and limitations of induction and deduction 89</p>
<p>4.7. The development of a relational reasoning graph 90</p>
<p>4.8. A complete integrated reasoning process 92</p>
<p>4.9. How can a computer analyze different types of reasoning? 94</p>
<p>4.9.1. Theorem proving by semantic techniques 95</p>
<p>4.9.2. Theorem proving by syntactical techniques 95</p>
<p>4.9.3. Theorem proving by grammatical techniques 96</p>
<p>4.10. Applications 96</p>
<p>4.10.1. Building the planning and scheduling involved in an industrial production system 97</p>
<p>4.10.2. Diagnosis or classification in qualitative processes (medical, system testing, etc.) 97</p>
<p>4.10.3. Comments 98</p>
<p>Chapter 5. From Context to Knowledge: Basic Methodology Review 101</p>
<p>5.1. Application of abduction and retroduction to create knowledge 101</p>
<p>5.2. Analysis and synthesis as modeling process 102</p>
<p>5.2.1. Fundamental analytic process 102</p>
<p>5.2.2 Modeling process 103</p>
<p>5.2.3. Abnormal or paranormal analysis and synthesis 106</p>
<p>5.2.4. Application: the main influences due to basic emotions 107</p>
<p>5.2.5. Comment 108</p>
<p>5.3. Background on empirical results: integration principles 109</p>
<p>5.3.1. Cyclical and hierarchical theories about theorizing; Heron and Kolb 109</p>
<p>5.3.2. Complementary advice: how to get good knowledge? 111</p>
<p>5.4. A review and comparison of some common approaches: TRIZ and C–K theory 112</p>
<p>5.4.1. TRIZ is about design problem solving 112</p>
<p>5.4.2. C–K is dealing with design innovation 113</p>
<p>5.4.3. C–K INVENT: toward a methodology for transformational K 114</p>
<p>Chapter 6. From Knowledge to Context and Back: The C–K Theory and Methodology 117</p>
<p>6.1. Introduction 117</p>
<p>6.2. A primer on C–K theory 118</p>
<p>6.3. On the nature of the knowledge space 120</p>
<p>6.4. On the nature of the concept space120</p>
<p>6.5. Discussing the theory 122</p>
<p>6.6. Some differentiating points and benefits of C–K theory 123</p>
<p>6.7. On fielding C–K theory in organizations 124</p>
<p>6.8. A summary on C–K theory 124</p>
<p>6.9. A short glossary on C–K theory 126</p>
<p>6.10. Links with knowledge management 128</p>
<p>6.11. Example on a specific futuristic conceptual case: a man who can travel through time 130</p>
<p>6.12. Methodological findings 130</p>
<p>Part 3. Reformulating the Above Into Business Models and Solutions for New Growth and Applications 135</p>
<p>Chapter 7. Principles and Methods for the Design and Development of Sustainable Systems 137</p>
<p>7.1. Introduction 137</p>
<p>7.2. How to go further? 139</p>
<p>7.3. Examples of methods and learning related to complex adaptive systems 140</p>
<p>7.3.1. Why and how to mix different theories? 141</p>
<p>7.3.2. Errors and mistakes not to make 142</p>
<p>7.4. First example: crisis management 143</p>
<p>7.5. Second example: urban organizations 144</p>
<p>7.5.1. A village infrastructure 144</p>
<p>7.5.2. Urban networks 146</p>
<p>7.6. Third example: education and career evolution 148</p>
<p>7.7. A review of survival, resilience and sustainability concepts 149</p>
<p>7.7.1. Definition of resilience 150</p>
<p>7.7.2. Definition of sustainability 151</p>
<p>7.7.3. Definition of reliability 153</p>
<p>7.7.4. Structure and organization of the concepts 154</p>
<p>7.8. Methodologies in sustainability 155</p>
<p>7.8.1. Modeling a sustainable system 156</p>
<p>7.8.2. Evaluation of the sustainability 157</p>
<p>7.8.3. Causes of non–achieving sustainability 158</p>
<p>7.9. Resilience: methodology 162</p>
<p>7.9.1. Problem of attitude change 162</p>
<p>7.9.2. Solving approaches 164</p>
<p>7.9.3. Methods associated with structured scenarios 165</p>
<p>7.9.4. Adaptive management in the Everglades and the Grand Canyon 166</p>
<p>7.9.5. Living together and empathy 167</p>
<p>7.10. Information system sustainability 171</p>
<p>7.10.1. General approach to assess reliability and sustainability in a complex system 171</p>
<p>7.10.2. Favoring a step–by–step approach 172</p>
<p>7.10.3. Comments about sustainability assessment 173</p>
<p>7.11. Application: managing the skill mismatch in a company 177</p>
<p>7.11.1. Assumptions 177</p>
<p>7.11.2. Methodological approach 178</p>
<p>7.11.3. Model development and results 180</p>
<p>7.12. Sustainability of the organizations in a company 181</p>
<p>7.13. Conclusions 183</p>
<p>Chapter 8. Toward the Mass Co–design: Why is Social Innovation so Attractive? 189</p>
<p>8.1. Introduction 189</p>
<p>8.2. How can we define innovation and social innovation? 190</p>
<p>8.2.1. Innovation: main principles 190</p>
<p>8.2.2. Social innovation: an evolution 191</p>
<p>8.3. Sustainability: how can we position social innovation? 193</p>
<p>8.4. Social innovation examples 195</p>
<p>8.4.1. Application 1: research and development of future technologies 195</p>
<p>8.4.2. Application 2: marketing and sales: I think to you 197</p>
<p>8.4.3. Application 3: inclusivity and cognition 200</p>
<p>8.4.4. Consequences 201</p>
<p>8.5. A contextual change in society 203</p>
<p>8.5.1. Networks are everywhere 203</p>
<p>8.5.2. Advantages of the Web approach 203</p>
<p>8.6. Basic concepts and mechanisms 205</p>
<p>8.6.1. The social concept of a process: principle of emergence 206</p>
<p>8.6.2. The social innovation process mechanism 207</p>
<p>8.6.3. Social innovation: conditions for sustainable implementation 209</p>
<p>8.7. The principle of circularity: a paradigm shift 211</p>
<p>8.8. Generalization: how to turn back time 212</p>
<p>8.9. Problems of technological evolution 214</p>
<p>8.9.1. In nature, evolution is consistent with Moore s law 214</p>
<p>8.9.2. The limits of new technologies and sciences 215</p>
<p>8.9.3. Application in industry: where are we going? 216</p>
<p>8.10. Evolution: application to cellular networks 218</p>
<p>8.10.1. Extended environments 218</p>
<p>8.10.2. Social networking 219</p>
<p>8.11. Conclusions: the new sustainable environment 220</p>
<p>8.11.1. Generalities 220</p>
<p>8.11.2. Global process engineering 221</p>
<p>8.11.3. Intelligence economy 222</p>
<p>Chapter 9. On Integrating Innovation and CSR when Developing Sustainable Systems 225</p>
<p>9.1. The new Smartphones: a tool for an inclusive society 225</p>
<p>9.2. Innovation and corporate social responsibility (CSR) behaviors 228</p>
<p>9.3. Integrating business objectives (CBO) and corporate social responsibility (SCR) 230</p>
<p>9.3.1. Implementation comments 230</p>
<p>9.4. Lessons gained from this study case: toward a citizen democracy 234</p>
<p>9.5. Conclusion on crowd and social approaches 238</p>
<p>Part 4. Reformulating Future Thinking: Processes and Applications 239</p>
<p>Chapter 10. Sustainability Engineering and Holism: Thinking Conditions are a Must 241</p>
<p>10.1. Introduction to holism 241</p>
<p>10.1.1. What do we mean by holism? 242</p>
<p>10.1.2. Application to decision and management systems 243</p>
<p>10.2. Toward a holistic company 244</p>
<p>10.3. Culture: on what positive factors can we rely? 246</p>
<p>10.4. Sustainability: a framework 249</p>
<p>10.5. Application: holonic industrial systems 250</p>
<p>10.5.1. Definitions 250</p>
<p>10.5.2. The design of a holonic manufacturing system (HMS) 251</p>
<p>10.5.3. Holism: a contribution to a better sustainability 253</p>
<p>10.6. Consequences 254</p>
<p>Chapter 11. Sustainable Cognitive Engineering: Brain Modeling; Evolution of a Knowledge Base 257</p>
<p>11.1. Introduction 257</p>
<p>11.2. Sustainable cognition: definition and concepts 258</p>
<p>11.3. Concepts and slippage needs: effects related to new generations 260</p>
<p>11.4. Basic structure of our brain: a probabilistic approach 261</p>
<p>11.4.1. Application to a human population: macro behavior and conditional probabilities 262</p>
<p>11.4.2. Bayes theorem: a universal statistical concept 264</p>
<p>2.4.3. Impact of the Bayes theorem on information system sustainability and decision theory 265</p>
<p>11.5. Application and probabilistic reasoning in updating a knowledge base: a more sustainable model 266</p>
<p>11.5.1. Two applications 266</p>
<p>11.5.2. Complex reasoning: a question of plausibility and probabilistic estimates 269</p>
<p>11.6. Sustainable cognition: brain structure, understanding micro–to–macro links 271</p>
<p>11.7. More recent developments 271</p>
<p>11.8. Detection of novelties through adaptive learning and fractal chaos approaches 274</p>
<p>11.9. Neuro computing: new opportunities provided by quantum physics 277</p>
<p>11.10. Applications 279</p>
<p>11.11. Quantum physics: impact on future organizations 280</p>
<p>Chapter 12. Brain and Cognitive Computing: Where Are We Headed? 283</p>
<p>12.1. State of the art 283</p>
<p>12.2. Achievements: is neuroscience able to explain how to perform sustained assumptions and studies? 284</p>
<p>12.3. Artificial brain: evolution of the simulation models 289</p>
<p>12.4. Examples of challenges to be well controlled 290</p>
<p>Part 5. Towards an Approach to the Measurement of Sustainability and Competitivity 293</p>
<p>Chapter 13. On Measuring Sustainability 295</p>
<p>13.1. Introduction 295</p>
<p>13.2. Some basic criteria specific to the new Sustainable era 296</p>
<p>13.3. What are the nature and limits of the new paradigm, in terms of sustainability evolution? 297</p>
<p>13.4. A reminder about competitivity and sustainability properties 299</p>
<p>13.5. Synthesis: the present dimensions of a production system 302</p>
<p>13.6. An under–assessed value: time 305</p>
<p>13.7. Application and results 307</p>
<p>13.7.1. Time is the source of streams and flows 307</p>
<p>13.7.2. Time and power: some considerations about streams and throughputs 308</p>
<p>13.7.3. Measurement of sustainability in a chaotic system: Lyapunov experiments 310</p>
<p>13.7.4. Consequences at governance level to get a sustainable system 312</p>
<p>13.8. Two new dimensions: thought and information within network theory 313</p>
<p>13.8.1. From storytelling 314</p>
<p>13.8.2. to talking bullshit 315</p>
<p>13.8.3. An improved understanding of a New World complexity 315</p>
<p>13.9. Synthesis: cognitive advances provided by the new exchange and communication tools 316</p>
<p>13.9.1. The cognitive behaviors associated with this classification 317</p>
<p>13.9.2. Synthesizing the cognitive advances 319</p>
<p>13.10. Consequences and characteristics linked to a global network notion 321</p>
<p>13.10.1. Generalizing the knowledge at organization level 321</p>
<p>13.10.2. The behaviors associated with human beings psychological features 322</p>
<p>13.11. Back to the code of matter: contributions to Simultaneous Time and Network Theory 323</p>
<p>13.12. Application of quantum interactions 326</p>
<p>13.13. Sustainability: how to widen the scope of competitiveness indicators? 328</p>
<p>13.14. Conclusion 330</p>
<p>13.15. Social interactions and massively multiplayer online role playing games 330</p>
<p>General Conclusion 333</p>
<p>Bibliography 355</p>
<p>Index 375</p>
<p>&nbsp;</p>