Worldviews, Science and Us: Philosophy and Complexity

Cover......Page 1
Half Title......Page 2
Title Page......Page 4
Copyright......Page 5
CONTENTS......Page 6
Introduction Carlos Gershenson, Diederik Aerts and Bruce Edmonds......Page 8
References......Page 11
1. The three principles of the rejection of complexity by \'classical science\'......Page 12
2. Complexity: A first breach: irreversibility......Page 13
3. Interaction Order/Disorder/Organization......Page 14
4. Chaos......Page 15
5. The emergence of the notion of complexity......Page 16
6. Generalized complexity......Page 17
7. System: It should be conceived that \'any system is complex\'......Page 18
8. Emergence of the notion of emergence......Page 19
10. The self-eco-organization......Page 20
11. The relationship between local and global......Page 22
12. Heraclitus: \'live of death, die of life\'......Page 23
14. To complexify the notion of chaos......Page 24
15. The need of contextualization......Page 25
17. For the sciences, a certain number of consequences......Page 27
18. Two scientific revolutions introduced complexity de facto......Page 28
19. The insertion of science in History......Page 29
20. The link between science and philosophy......Page 30
22. The principle of ecology of action......Page 32
23. Creating \'Institutes of fundamental culture\'......Page 33
24. I conclude: generalized complexity integrates restricted complexity......Page 34
25. We should even apprehend the possibilities of metamorphosis......Page 35
1. INTRODUCTION......Page 37
1.1. The largest Model......Page 38
1.2. Why is the whole more than the sum of its parts?......Page 39
1.3. Causality and information: Science of method and science of content......Page 40
1.5. Analytic vs. synthetic models......Page 41
2. SCIENCE AS A COMPLEX SYSTEM......Page 42
3.1. Hard Science is built on Cartesian Reductionism......Page 44
3.2. The Newtonian paradigm is the modern manifestation of hard science......Page 45
3.4. The way science is done: The modeling relation......Page 46
3.5. Complex systems and simple systems are disjoint categories that are related by the modeling relation......Page 47
4. THERMODYNAMIC REASONING AS A TRANSITION TO COMPLEXITY SCIENCE......Page 48
4.1. Classical or \'equilibrium\' thermodynamics and its limits......Page 49
4.2. Dissipation, friction, and irreversibility......Page 51
4.3. Preserving the paradigm involved considering friction, irreversibility and dissipation......Page 53
4.4. Framing the question in science: \"Don\'t think about the whole system\"......Page 54
4.5. Reductionism needs a particular kind of mathematics to accomplish its goals......Page 55
4.6. Topological reasoning in thermodynamics leads to powerful results......Page 56
5. Will science extend to the modeling of complex reality or will it be restricted to the limited domain of the largest model formalism it clings to?......Page 57
References......Page 58
1. Introduction......Page 60
2. Living in the Present......Page 62
3. Complex Systems, Temporality and Memory......Page 64
4. Integrity, Identity and Reflection......Page 67
References......Page 70
1. Introduction......Page 72
2. Elaboration......Page 74
3. A Priori Arguments......Page 76
4. Some Evidence from Machine Learning......Page 78
5. Special Cases......Page 79
6. Versions of \'Simplicity\'......Page 81
7. An Example - Curve fitting by parameterisation......Page 82
References......Page 85
1. Introduction......Page 88
2. The Emergent Question......Page 89
3. Explanations of Uniformity......Page 94
4. Addressing Novelty......Page 97
5. And Individuality......Page 99
6. Looking Ahead......Page 100
References......Page 102
Why Diachronically Emergent Properties Must Also Be Salient Cyrille Imbert......Page 106
1. Preliminaries......Page 107
2.1.1. Is nominal emergence enough to solve (Pb)?......Page 110
2.1.2. Is there more to diachronical emergence than mere computational irreducibility?......Page 111
2.2.1. The criterion must not rely o n intrinsic features of the property but must be contextual (Cl)......Page 113
2.2.3. The criterion must be contextually absolute (C3)......Page 114
3. Salience......Page 115
3.1. Preliminary definitions......Page 116
3.2. Towards a definition of salience......Page 118
3.3. How to avoid ad hoc descriptive functions?......Page 119
3.4. Requirements about describing EMOs......Page 120
References......Page 122
2. A Theory of Everything......Page 124
3. So, What is Emergence?......Page 125
3.1. Complex cellular automata and patterns......Page 126
4. The Hierarchy of Reality: Nested versus Convoluted......Page 131
4.1. Non-composite secondary structures......Page 132
5. Multiple Filters and Horizontal Ontologies......Page 135
5.1. A note on intrinsic emergence......Page 138
6. Summary and Conclusions......Page 140
7. Appendix A: On the Reasonableness of Assuming that the Universe is, at Some Level, Well-Described as a Cellular Automaton......Page 141
References......Page 145
1.1. Formal systems......Page 148
1.2. Mathematical Realism vs. Intuitionism......Page 154
2.1. The epistemology of computer experimentation......Page 156
2.2. The ontological rejection of uni-directional reductionist causality......Page 158
3. Conclusion: Truth in complex adaptive systems models should be based on proof by constructive verification......Page 159
References......Page 161
1. Introduction......Page 163
2. The Modern Episteme......Page 165
3. Continental Philosophy and the Collapse of the Modern Episteme......Page 169
4. Complexity and the Modern Episteme......Page 174
5. Conclusion......Page 177
References......Page 178
1. Introduction......Page 180
2. The Metaphorical Level......Page 182
3. Empirical Level - The Method......Page 184
4. The Role of Metaphor......Page 185
5. Final Comments......Page 186
References......Page 187
Some Problems for an Ontology of Complexity Michael McGuire......Page 188
1. Ontology......Page 189
2. What should ontology do for complexity theory?......Page 190
3. What ought complexity theory to ask of ontology?......Page 191
4. Some \'ontologies\' of complexity......Page 192
5. Nominalism and Complexity......Page 195
6. Problems with the alternatives to nominalism......Page 197
7. Problems with the Order/Disorder Distinction......Page 199
8. Further, More General Objections......Page 201
9. Patterns......Page 202
10. Patterns and Complexity......Page 204
11. The problems with patterns......Page 205
References......Page 207
1. How to love the bomb - the MAD strategy......Page 210
2. The problem......Page 212
3. The Prisoner\'s Dilemma......Page 213
4. Naturalistic accounts of the emergence of altruism......Page 214
5. Complex Systems and Evolutionary Game Theory......Page 216
6. A solution to the Prisoner\'s Dilemma?......Page 219
8. Discussion......Page 221
9. Conclusions......Page 225
References......Page 226
1. Introduction......Page 228
2. Kolmogorov - Chaitin complexity and biology......Page 229
3. Physical complexity......Page 230
4. Physical complexity and the concept of cognitive biology......Page 232
5. Self-referential cognition - a \'Big Bang\' of complexity in cognitive evolution?......Page 234
References......Page 236
1. Introduction......Page 239
2. Some preliminary reflections......Page 241
3. The Informational Dynamic Systems......Page 242
3.1. Organization of the Informational Dynamic System......Page 245
3.2. Information and Function Emergence......Page 248
References......Page 253
1. Introduction. Complexity: an elusive idea......Page 257
2. Sciences of objects and systems, some simple, some complex......Page 260
2.1. Some methodological and conceptual conclusions......Page 262
3. Biological complexity: hierarchies......Page 263
3.1. Organicism......Page 264
3.2. Theory of Integrative Levels......Page 266
3.3. Pattee and recent work (Pattee on hierarchies & the concept of constraint)......Page 267
Aknowledgements......Page 269
References......Page 270
1. Introduction......Page 273
2.1. Against Genemtive Sufficiency of Growing Artificial Societies From the Bottom Up......Page 277
2.2. \'Generative\' from a Philosophy of Computer Science Perspective......Page 278
2.3. Refutation of Formal Deduction through Execution......Page 281
3. The Role of Programming Languages......Page 285
4. A Different Idea of Simulation......Page 288
References......Page 290
1. Introduction......Page 292
2.1. Basic definitions......Page 293
2.2. Physical complexity......Page 294
2.4. Shortcomings of algorithmic complexity......Page 295
2.5. Applications and related work......Page 296
3.1. Non-reductionism in philosophy of mind......Page 297
3.2. Arguments from ignorance......Page 298
4. Mild versus extreme non-reductionism......Page 299
4.2. Consequences of irreducibility......Page 300
4.3. The need for abstract talk......Page 301
4.4. Algorithmic complexity and emergence......Page 302
5. Conclusion......Page 304
References......Page 305
The Complexity of Information-Processing Tasks in Vision John Symons......Page 307
1. Recognizing Complexity......Page 309
2. How Marr saw vision......Page 311
3. Information processors vs. resonators......Page 312
4. Representation and Information Processing Complexity......Page 315
5. Conclusion......Page 318
Acknowledgements......Page 320
References......Page 321
1. Introduction......Page 322
2. Modeling the Brain......Page 329
3.1. Determining the power of a neural network by the complexity of its weights......Page 332
3.2. Extracting information from the weights......Page 333
3.3. Other sources of computational power: spikes and operations......Page 335
4. Discussion and Conclusion......Page 340
References......Page 341
1. What Would Mathematics without Culture Look Like?......Page 345
2. Cognitive Processes Involved in Learning and Performing Algebra......Page 347
3. Cultural Transmission of Algebra in Context......Page 349
3.1. Chinese Algebra......Page 350
3.2. Islamic Algebra......Page 351
3.3. European Algebra......Page 352
4. The Extended Mind and the Autonomy of Mathematical Thought......Page 354
5. Conclusion......Page 355
References......Page 356
Back Cover......Page 359