Smart Decisions in Complex Systems

<p>Contents</p>
<p>Preface xiii</p>
<p>Acknowledgments&nbsp;&nbsp; &nbsp;xvii</p>
<p>List of Acronyms &nbsp;xix</p>
<p>Introduction &nbsp;&nbsp;xxv</p>
<p>Part 1. 1</p>
<p>Chapter 1. The Foundations of Complexity &nbsp;3</p>
<p>1.1. Complexities and simplexities: paradigms and perspectives&nbsp;&nbsp; &nbsp;3</p>
<p>1.1.1. Positioning the problem 4</p>
<p>1.1.2. Reminders, basics and neologisms &nbsp;&nbsp;&nbsp;5</p>
<p>1.1.3. What are the analytical steps in a complex system? &nbsp;&nbsp;16</p>
<p>1.1.4. Organization and management principles in complex systems&nbsp;&nbsp; &nbsp;31</p>
<p>1.1.5. Action and decision processes in self–organized systems&nbsp;&nbsp; &nbsp;35</p>
<p>1.1.6. Notions of centralization and decentralization &nbsp;&nbsp;&nbsp;36</p>
<p>1.2. What is the prerequisite for the handling of a complex system?&nbsp;&nbsp; &nbsp;43</p>
<p>1.3. Applications: industrial complex systems &nbsp;&nbsp;45</p>
<p>1.3.1. Distributed workshop management system &nbsp;&nbsp;&nbsp;&nbsp;45</p>
<p>1.3.2. Analysis and diagnosis of a complex system &nbsp;&nbsp;&nbsp;&nbsp;47</p>
<p>1.3.3. Some recommendations and comments to conclude &nbsp;48</p>
<p>1.4. Time to conclude&nbsp;&nbsp; &nbsp;50</p>
<p>1.4.1. Summary &nbsp;50</p>
<p>1.4.2. Lessons and perspectives &nbsp;&nbsp;51</p>
<p>Part 2. 53</p>
<p>Chapter 2. Evidencing Field Complexity &nbsp;&nbsp;55</p>
<p>2.1. Introduction &nbsp;&nbsp;55</p>
<p>2.2. Qualitative study of deterministic chaos in a dynamic simple system &nbsp;&nbsp;58</p>
<p>2.2.1. Description of a few simple cases &nbsp;&nbsp;&nbsp;58</p>
<p>2.2.2. Initial conditions related to the emergence of chaos &nbsp;59</p>
<p>2.2.3. Modeling and mathematical analysis of chaos &nbsp;&nbsp;&nbsp;62</p>
<p>2.2.4. Application at the level of a simple cell &nbsp;63</p>
<p>2.3. Test for the presence of deterministic chaos in a simple dynamic system &nbsp;68</p>
<p>2.3.1. Characterization of the systems studied &nbsp;69</p>
<p>2.3.2. A general question: is there deterministic chaos? &nbsp;&nbsp;70</p>
<p>2.4. Properties of chaos in complex systems &nbsp;&nbsp;77</p>
<p>2.4.1. Study of an elementary cell &nbsp;&nbsp;77</p>
<p>2.4.2. Complex cellular systems &nbsp;&nbsp;81</p>
<p>2.5. Effects of fractal chaos in Complexity theory &nbsp;&nbsp;&nbsp;&nbsp;83</p>
<p>2.5.1. Organized complexity 83</p>
<p>2.5.2. Innovative complexity 84</p>
<p>2.5.3. Random complexity &nbsp;85</p>
<p>2.5.4. Principles of implementation &nbsp;&nbsp;&nbsp;&nbsp;87</p>
<p>2.6. Self–organization: relations and the role of chaos &nbsp;&nbsp;&nbsp;87</p>
<p>2.6.1. Introduction&nbsp;&nbsp; &nbsp;87</p>
<p>2.6.2. How to combine self–organization and chaos &nbsp;&nbsp;&nbsp;88</p>
<p>2.6.3. Critical self–organized systems &nbsp;&nbsp;&nbsp;&nbsp;89</p>
<p>2.6.4. Networked systems and co–operative systems &nbsp;&nbsp;&nbsp;90</p>
<p>2.6.5. The three states of a dynamic complex system &nbsp;&nbsp;&nbsp;93</p>
<p>2.6.6. Towards a typology of behavioral complexity &nbsp;&nbsp;&nbsp;94</p>
<p>2.7. Applications: introduction of new concepts in systems &nbsp;&nbsp;95</p>
<p>2.7.1. Questions on the management of complex industrial systems &nbsp;95</p>
<p>2.7.2. Implementation of the concepts of chaos and self–organization &nbsp;96</p>
<p>2.8. Conclusions &nbsp;&nbsp;98</p>
<p>Chapter 3. The New Complex Operational Context &nbsp;101</p>
<p>3.1. The five phases of economy how everything</p>
<p>accelerates at the same time &nbsp;101</p>
<p>3.2. The expected impact on just about everything &nbsp;&nbsp;&nbsp;&nbsp;105</p>
<p>Chapter 4. Taking Up Complexity &nbsp;&nbsp;&nbsp;&nbsp;109</p>
<p>4.1. Taking into account complex models &nbsp;&nbsp;&nbsp;109</p>
<p>4.1.1. A brief overview of the approach called complexity &nbsp;109</p>
<p>4.1.2. Another (bio–inspired) vision of the world: universality&nbsp;&nbsp; &nbsp;112</p>
<p>4.1.3. How to address complexity in this universal world? &nbsp;115</p>
<p>4.1.4. The usefulness of this book &nbsp;&nbsp;116</p>
<p>4.2. Economy and management of risks &nbsp;&nbsp;&nbsp;&nbsp;117</p>
<p>4.2.1. Important challenges to raise &nbsp;&nbsp;&nbsp;&nbsp;117</p>
<p>4.2.2. Adapted vocabulary that it is useful to adopt &nbsp;&nbsp;&nbsp;&nbsp;118</p>
<p>4.2.3. What do we mean by dynamic pricing? &nbsp;119</p>
<p>Part 3. 121</p>
<p>Chapter 5. Tackling Complexity with a Methodology &nbsp;&nbsp;123</p>
<p>5.1. Any methodology must first enrich the systemic interrelationships &nbsp;123</p>
<p>5.1.1. The innovation economy: the dynamic management of innovation &nbsp;&nbsp;124</p>
<p>5.1.2. A basic mechanism of efficient innovation &nbsp;&nbsp;&nbsp;&nbsp;125</p>
<p>5.1.3. The benefits of such a shift mechanism &nbsp;126</p>
<p>5.2. Towards a transdisciplinary co–economy &nbsp;&nbsp;126</p>
<p>Chapter 6. Management and Control of Complex Systems&nbsp; &nbsp;129</p>
<p>6.1. Introduction &nbsp;&nbsp;129</p>
<p>6.2. Complex systems: the alternatives &nbsp;&nbsp;&nbsp;&nbsp;132</p>
<p>6.2.1. Notions of sociability in agent communities &nbsp;&nbsp;&nbsp;&nbsp;132</p>
<p>6.2.2. The evolutionary principles of complex systems &nbsp;&nbsp;134</p>
<p>6.3. Control principles of production systems &nbsp;&nbsp;135</p>
<p>6.3.1. Introduction&nbsp;&nbsp; &nbsp;135</p>
<p>6.3.2. Control: by scheduling or by configuration? &nbsp;&nbsp;&nbsp;&nbsp;136</p>
<p>6.3.3. The tools used in monitoring and control &nbsp;140</p>
<p>6.4. PABADIS: an example of decentralized control &nbsp;&nbsp;&nbsp;&nbsp;141</p>
<p>6.4.1. Introduction&nbsp;&nbsp; &nbsp;141</p>
<p>6.4.2. Context and objectives of the PABADIS project &nbsp;&nbsp;142</p>
<p>6.4.3. Conceptual overview of PABADIS &nbsp;&nbsp;142</p>
<p>6.4.4. Principle of adopted convergence: the inverse solution&nbsp;&nbsp; &nbsp;144</p>
<p>6.4.5. Implementation&nbsp; &nbsp;145</p>
<p>6.5. Generalization of the concepts and mechanisms &nbsp;&nbsp;&nbsp;&nbsp;146</p>
<p>6.5.1. Introduction&nbsp;&nbsp; &nbsp;146</p>
<p>6.5.2. Allocation of resources: the agents in complex production systems&nbsp;&nbsp; &nbsp;147</p>
<p>6.5.3. Allocation of resources: the negotiation protocols &nbsp;&nbsp;147</p>
<p>6.5.4. Optimization of the resource allocation process &nbsp;&nbsp;&nbsp;148</p>
<p>6.6. A basic mechanism of control the auction &nbsp;150</p>
<p>6.6.1. Introduction&nbsp;&nbsp; &nbsp;150</p>
<p>6.6.2. The mechanism of the auction &nbsp;&nbsp;&nbsp;&nbsp;151</p>
<p>6.6.3. Comparative review of the types of auctions &nbsp;&nbsp;&nbsp;&nbsp;153</p>
<p>6.6.4. Findings on the interest of the auction mechanism &nbsp;&nbsp;155</p>
<p>6.7. The control of self–organized systems &nbsp;&nbsp;&nbsp;156</p>
<p>6.7.1. Introduction&nbsp;&nbsp; &nbsp;156</p>
<p>6.7.2. The types and mechanisms of self–organization &nbsp;&nbsp;&nbsp;157</p>
<p>6.7.3. Towards a dynamic integrated model: Cellular Automata (CA)&nbsp; &nbsp;160</p>
<p>6.7.4. Self–organization: forms and configurations obtained &nbsp;165</p>
<p>6.7.5. Conclusion and implementation of the ACCA concept, a major model&nbsp; &nbsp;167</p>
<p>Chapter 7. Platforms for Taking up Complexity &nbsp;&nbsp;&nbsp;169</p>
<p>7.1. The VFDCS: a platform for implementation &nbsp;169</p>
<p>7.1.1. Controlling the phenomena of self–organization &nbsp;&nbsp;&nbsp;171</p>
<p>7.1.2. Methodology for implementation and the validation of concepts&nbsp; &nbsp;172</p>
<p>7.2. The application of VFDCS: the auction market &nbsp;&nbsp;&nbsp;&nbsp;174</p>
<p>7.2.1. The concept of the Container in the auction market &nbsp;176</p>
<p>7.2.2. Feedbacks and results 176</p>
<p>7.2.3. Discussion &nbsp;178</p>
<p>7.3. The application of VFDCS: the virtual supply chain &nbsp;&nbsp;179</p>
<p>7.3.1. Introduction&nbsp;&nbsp; &nbsp;179</p>
<p>7.3.2. Architecture of the virtual supply chain &nbsp;181</p>
<p>7.3.3. Results and comments 184</p>
<p>7.3.4. Conclusion &nbsp;185</p>
<p>7.3.5. Enhancement of the multi–agent platform &nbsp;&nbsp;&nbsp;&nbsp;186</p>
<p>7.4. General method for the control of systems &nbsp;186</p>
<p>7.4.1. Introduction&nbsp;&nbsp; &nbsp;186</p>
<p>7.4.2. Reminders and definitions &nbsp;&nbsp;187</p>
<p>7.4.3. Analytical approach to consistency &nbsp;&nbsp;188</p>
<p>7.4.4. Methods for the analysis and monitoring of performances &nbsp;189</p>
<p>7.4.5. Critical analysis of the convergence of configurations &nbsp;192</p>
<p>7.5. Conclusions and prospects 194</p>
<p>7.5.1. Synthesis &nbsp;194</p>
<p>7.5.2. Discussion &nbsp;195</p>
<p>7.5.3. Comparison of approaches, tools and applications &nbsp;&nbsp;197</p>
<p>7.5.4. Results &nbsp;&nbsp;199</p>
<p>Part 4. 201</p>
<p>Introduction to Part 4&nbsp; &nbsp;203</p>
<p>Chapter 8. Applying Intrinsic Complexity:</p>
<p>The Uberization of the Economy &nbsp;&nbsp;&nbsp;&nbsp;207</p>
<p>8.1. Preamble &nbsp;&nbsp;207</p>
<p>8.2. The context: new opportunities and new consumption needs &nbsp;207</p>
<p>8.3. The domains that are studied in this chapter &nbsp;208</p>
<p>8.4. Concepts, definitions and remainders &nbsp;&nbsp;&nbsp;209</p>
<p>8.4.1. Uberization&nbsp;&nbsp; &nbsp;209</p>
<p>8.4.2. Digitalization of the economy &nbsp;&nbsp;&nbsp;&nbsp;210</p>
<p>8.4.3. Collaborative consumption (CC) &nbsp;&nbsp;&nbsp;211</p>
<p>8.4.4. Model generalization: the sharing economy &nbsp;&nbsp;&nbsp;&nbsp;211</p>
<p>8.4.5. Participatory financing 211</p>
<p>8.5. The business model and key elements &nbsp;&nbsp;&nbsp;213</p>
<p>8.5.1. Practicing networks &nbsp;213</p>
<p>8.5.2. Positive and negative impacts of network applications &nbsp;&nbsp;214</p>
<p>8.5.3. The problem of producer consumers and consumer producers&nbsp;&nbsp; &nbsp;215</p>
<p>8.5.4. Underlying mechanisms: some differences with the usual economic systems 216</p>
<p>8.5.5. A form of social hypocrisy? &nbsp;&nbsp;217</p>
<p>8.5.6. Generalization: the management rules for P2P &nbsp;&nbsp;&nbsp;219</p>
<p>8.6. The problem of property and resource allocation. &nbsp;&nbsp;&nbsp;220</p>
<p>8.6.1. The growing role of platforms &nbsp;&nbsp;&nbsp;&nbsp;220</p>
<p>8.6.2. The prisoner s dilemma 223</p>
<p>8.6.3. Games theory: an introduction &nbsp;&nbsp;&nbsp;&nbsp;224</p>
<p>8.6.4. Nonlinear models in game theory &nbsp;&nbsp;&nbsp;224</p>
<p>8.7. The uberization approach in context &nbsp;&nbsp;&nbsp;226</p>
<p>8.7.1. Simplexification.&nbsp; &nbsp;227</p>
<p>8.7.2. Increasing complexity: the influence of cognitive approaches&nbsp;&nbsp; &nbsp;227</p>
<p>8.8. Generalization: the complexity of allocation problems &nbsp;&nbsp;230</p>
<p>8.9. Conclusion &nbsp;&nbsp;234</p>
<p>Chapter 9. Computer–assisted Production Management&nbsp;&nbsp; &nbsp;235</p>
<p>9.1. Introduction and reminders 235</p>
<p>9.2. Intercommunication networks &nbsp;&nbsp;236</p>
<p>9.2.1. Notions of complexity in networks &nbsp;&nbsp;236</p>
<p>x Smart Decisions in Complex Systems</p>
<p>9.2.2. A few concepts of parallelism &nbsp;&nbsp;&nbsp;&nbsp;237</p>
<p>9.2.3. Elements of parallelism and associated architectures &nbsp;237</p>
<p>9.2.4. Transposition into industrial or social applications &nbsp;&nbsp;239</p>
<p>9.3. Communication network topologies &nbsp;&nbsp;&nbsp;240</p>
<p>9.3.1. Some characteristics of different network topologies &nbsp;241</p>
<p>9.3.2. Construction of a hypercube &nbsp;&nbsp;&nbsp;&nbsp;242</p>
<p>9.3.3. Notions of symmetry: cutting a hypercube &nbsp;&nbsp;&nbsp;&nbsp;243</p>
<p>9.3.4. The shortest path between two processors &nbsp;&nbsp;&nbsp;&nbsp;244</p>
<p>9.4. A few important properties 244</p>
<p>9.5. Analysis of new concepts and methods in manufacturing sciences: instabilities, responsiveness and flexibility &nbsp;&nbsp;&nbsp;&nbsp;246</p>
<p>9.5.1. General approach: planning and scheduling &nbsp;&nbsp;&nbsp;&nbsp;247</p>
<p>9.5.2. Illustration in management systems &nbsp;&nbsp;247</p>
<p>9.5.3. Problems and remarks 250</p>
<p>9.5.4. Improvements in planning and scheduling &nbsp;&nbsp;&nbsp;&nbsp;251</p>
<p>9.5.5. Improvements in configuration/reconfiguration &nbsp;&nbsp;&nbsp;252</p>
<p>9.5.6. Global improvements through simulation &nbsp;&nbsp;&nbsp;&nbsp;253</p>
<p>9.5.7. Inverse modeling and simulation &nbsp;&nbsp;&nbsp;254</p>
<p>9.6. New concepts for managing complex systems &nbsp;&nbsp;&nbsp;&nbsp;256</p>
<p>9.6.1. Traditional approach &nbsp;257</p>
<p>9.6.2. Recent improvements in the management of systems &nbsp;260</p>
<p>9.7. The change of conduct &nbsp;264</p>
<p>9.8. Improvements in manufacturing: process balancing &nbsp;&nbsp;&nbsp;266</p>
<p>9.9. Conclusion: main action principles in complex environments &nbsp;&nbsp;267</p>
<p>Chapter 10. Complexity and Cognitive Robotics &nbsp;&nbsp;&nbsp;271</p>
<p>10.1. Introduction &nbsp;271</p>
<p>10.2. The new industrial revolution &nbsp;&nbsp;272</p>
<p>10.3. The factory of the future: trend or revolution? &nbsp;&nbsp;&nbsp;&nbsp;272</p>
<p>10.4. Inputs for the factory of the future and their impact on the industry s professions &nbsp;275</p>
<p>10.5. Conditions for success &nbsp;276</p>
<p>10.6. The data sciences&nbsp;&nbsp; &nbsp;277</p>
<p>10.6.1. Introduction to the characteristics of Big Data &nbsp;&nbsp;277</p>
<p>10.6.2. The problem of Big Data &nbsp;&nbsp;277</p>
<p>10.6.3. A new profession: the data scientist &nbsp;&nbsp;279</p>
<p>10.6.4. Some ask, how will this be possible? &nbsp;279</p>
<p>10.6.5. The field of large numbers &nbsp;&nbsp;280</p>
<p>10.7. A few technologies in data sciences &nbsp;&nbsp;&nbsp;281</p>
<p>10.7.1. The steps of reasoning based on the experience of the inductive approach and on the verification of hypotheses &nbsp;&nbsp;281</p>
<p>10.7.2. The Lasso method 281</p>
<p>10.7.3. Kernel regression methods &nbsp;&nbsp;282</p>
<p>10.7.4. The random forests &nbsp;283</p>
<p>10.7.5. Neural networks&nbsp; &nbsp;284</p>
<p>10.7.6. Comments on clustering and graph partitioning issues 286</p>
<p>10.7.7. Cognitive informatics cognitivism &nbsp;&nbsp;286</p>
<p>10.8. Mechanisms of conventional cognitive engineering &nbsp;&nbsp;288</p>
<p>10.9. The new mechanisms of engineering &nbsp;&nbsp;&nbsp;289</p>
<p>10.9.1. Transduction&nbsp;&nbsp; &nbsp;289</p>
<p>10.9.2. Reasoning by constructed analogies &nbsp;&nbsp;290</p>
<p>10.10. The study of links and relationships in large databases &nbsp;290</p>
<p>10.10.1. Comment&nbsp;&nbsp; &nbsp;291</p>
<p>10.11. Application of cognitive robotics: the Watson platform &nbsp;291</p>
<p>10.11.1. Applications&nbsp; &nbsp;292</p>
<p>10.12. The impossibilities and unpredictabilities of complexity&nbsp;&nbsp; &nbsp;293</p>
<p>10.13. Current strategies of digitalization &nbsp;&nbsp;&nbsp;295</p>
<p>10.13.1. Reference examples and discussion &nbsp;296</p>
<p>10.13.2. GNOSIS &nbsp;298</p>
<p>10.13.3. Data is Centric &nbsp;299</p>
<p>10.14. Conclusion: a maximum risk economy &nbsp;&nbsp;300</p>
<p>Bibliography &nbsp;&nbsp;303</p>
<p>Index &nbsp;327</p>