CONTENTS -- Introduction Carlos Gershenson, Diederik Aerts and Bruce Edmonds -- References -- Restricted Complexity, General Complexity Edgar Morin -- 1. The three principles of the rejection of complexity by 'classical science' -- 2. Complexity: A first breach: irreversibility -- 3. Interaction Order/Disorder/Organization -- 4. Chaos -- 5. The emergence of the notion of complexity -- 6. Generalized complexity -- 7. System: It should be conceived that "any system is complex" -- 8. Emergence of the notion of emergence -- 9. The complexity of organization -- 10. The self-eco-organization -- 11. The relationship between local and global -- 12. Heraclitus: "live of death, die of life" -- 13. On non-trivial machines -- 14. To complexify the notion of chaos -- 15. The need of contextualization -- 16. The hologrammatic and dialogical principles -- 17. For the sciences, a certain number of consequences -- 18. Two scientific revolutions introduced complexity de facto -- 19. The insertion of science in History -- 20. The link between science and philosophy -- 21. Second epistemological rupture with restricted complexity -- 22. The principle of ecology of action -- 23. Creating "Institutes of fundamental culture" -- 24. I conclude: generalized complexity integrates restricted complexity -- 25. We should even apprehend the possibilities of metamorphosis -- Complexity Science as an Aspect of the Complexity of Science Don C. Mikulecky -- 1. INTRODUCTION -- 1.1. The largest Model -- 1.2. Why is the whole more than the sum of its parts? -- 1.3. Causality and information: Science of method and science of content -- 1.4. Which is generic, physics or biology? -- 1.5. Analytic vs. synthetic models -- 1.6. Fragmentability -- 1.7. Computability -- 2. SCIENCE AS A COMPLEX SYSTEM -- 3. COMPLEXITY AS AN ATTRIBUTE OF NATURE.

3.1. Hard Science is built on Cartesian Reductionism -- 3.2. The Newtonian paradigm is the modern manifestation of hard science -- 3.3. Complexity is the result of the failure of the Newtonian Paradigm to be generic -- 3.4. The way science is done: The modeling relation -- 3.5. Complex systems and simple systems are disjoint categories that are related by the modeling relation -- 4. THERMODYNAMIC REASONING AS A TRANSITION TO COMPLEXITY SCIENCE -- 4.1. Classical or "equilibrium" thermodynamics and its limits -- 4.2. Dissipation, friction, and irreversibility -- 4.3. Preserving the paradigm involved considering friction, irreversibility and dissipation -- 4.4. Framing the question in science:" Don't think about the whole system" -- 4.5. Reductionism needs a particular kind of mathematics to accomplish its goals -- 4.6. Topological reasoning in thermodynamics leads to powerful results -- 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? -- References -- On the Importance of a Certain Slowness Paul Cilliers -- 1. Introduction -- 2. Living in the Present -- 3. Complex Systems, Temporality and Memory -- 4. Integrity, Identity and Reflection -- References -- Simplicity is Not Truth-Indicative Bruce Edmonds -- 1. Introduction -- 2. Elaboration -- 3. A Priori Arguments -- 4. Some Evidence from Machine Learning -- 5. Special Cases -- 6. Versions of "Simplicity" -- 7. An Example - Curve fitting by parameterisation -- 8. Concluding Plea -- Acknowledgements -- References -- Can the Whole be More than the Computation of the Parts? A Reflection on Emergence Camilo Olaya -- 1. Introduction -- 2. The Emergent Question -- 3. Explanations of Uniformity -- 4. Addressing Novelty -- 5. And Individuality -- 6. Looking Ahead -- Acknowledgments -- References.

Why Diachronically Emergent Properties Must Also Be Salient Cyrille Imbert -- 1. Preliminaries -- 2. Statement of the problem (Pb) -- 2.1. Two ways to face (Pb) -- 2.1.1. Is nominal emergence enough to solve (Pb)? -- 2.1.2. Is there more to diachronical emergence than mere computational irreducibility? -- 2.2. How to complete the simulation requirement? -- 2.2.1. The criterion must not rely o n intrinsic features of the property but must be contextual (Cl) -- 2.2.2. The criterion must be local, that is to say must rely on preceding microstates only (C2) -- 2.2.3. The criterion must be contextually absolute (C3) -- 3. Salience -- 3.1. Preliminary definitions -- 3.2. Towards a definition of salience -- 3.3. How to avoid ad hoc descriptive functions? -- 3.4. Requirements about describing EMOs -- 4. Conclusion -- Acknowledgements -- References -- On the Relativity of Recognising the Products of Emergence and the Nature of the Hierarchy of Physical Matter Kurt A . Richardson -- 1. Introduction -- 2. A Theory of Everything -- 3. So, What is Emergence? -- 3.1. Complex cellular automata and patterns -- 4. The Hierarchy of Reality: Nested versus Convoluted -- 4.1. Non-composite secondary structures -- 4.2. Does level of abstraction correspond to degree of realism ? -- 5. Multiple Filters and Horizontal Ontologies -- 5.1. A note on intrinsic emergence -- 6. Summary and Conclusions -- 7. Appendix A: On the Reasonableness of Assuming that the Universe is, at Some Level, Well-Described as a Cellular Automaton -- References -- Truth in Complex Adaptive Systems Models should be Based on Proof by Constructive Verification David Shipworth -- 1. The epistemology of complex adaptive systems -- 1.1. Formal systems -- 1.2. Mathematical Realism vs. Intuitionism -- 2. Characteristics of CAS theory and practice -- 2.1. The epistemology of computer experimentation.

2.2. The ontological rejection of uni-directional reductionist causality -- 3. Conclusion: Truth in complex adaptive systems models should be based on proof by constructive verification -- References -- Complexity as an Epistemic Revolution: Considerations on the New Science in the Context of Western Intellectual History Damian Popolo -- 1. Introduction -- 2. The Modern Episteme -- 3. Continental Philosophy and the Collapse of the Modern Episteme -- 4. Complexity and the Modern Episteme -- 5. Conclusion -- References -- Metaphors and Method: Epistemological Considerations on Complexity Science Rodrigo Zeidan and Maria Fonseca -- 1. Introduction -- 2. The Metaphorical Level -- 3. Empirical Level - The Method -- 4. The Role of Metaphor -- 5. Final Comments -- References -- Some Problems for an Ontology of Complexity Michael McGuire -- 1. Ontology -- 2. What should ontology do for complexity theory? -- 3. What ought complexity theory to ask of ontology? -- 4. Some 'ontologies' of complexity -- 5. Nominalism and Complexity -- 6. Problems with the alternatives to nominalism -- 7. Problems with the Order/Disorder Distinction -- 8. Further, More General Objections -- 9. Patterns -- 10. Patterns and Complexity -- 11. The problems with patterns -- References -- How to Love the Bomb - Trying to Solve the Prisoner's Dilemma with Evolutionary Game Theory Vasco Castela -- 1. How to love the bomb - the MAD strategy -- 2. The problem -- 3. The Prisoner's Dilemma -- 4. Naturalistic accounts of the emergence of altruism -- 5. Complex Systems and Evolutionary Game Theory -- 6. A solution to the Prisoner's Dilemma? -- 7. The role of morality -- 8. Discussion -- 9. Conclusions -- Acknowledgments -- References -- Physical Complexity and Cognitive Evolution Peter Jedlicka -- 1. Introduction -- 2. Kolmogorov - Chaitin complexity and biology -- 3. Physical complexity.

4. Physical complexity and the concept of cognitive biology -- 5. Self-referential cognition - a 'Big Bang' of complexity in cognitive evolution? -- Acknowledgements -- References -- Informational Dynamic Systems: Autonomy, Information, Function Walter Riofrio -- 1. Introduction -- 2. Some preliminary reflections -- 3. The Informational Dynamic Systems -- 3.1. Organization of the Informational Dynamic System -- 3.2. Information and Function Emergence -- References -- Grasping the Complexity of Living Systems Through Integrative Levels and Hierarchies J. M. Siqueiros and Jon Umerez -- 1. Introduction. Complexity: an elusive idea -- 2. Sciences of objects and systems, some simple, some complex -- 2.1. Some methodological and conceptual conclusions -- 3. Biological complexity: hierarchies -- 3.1. Organicism -- 3.2. Theory of Integrative Levels -- 3.3. Pattee and recent work (Pattee on hierarchies & the concept of constraint) -- 4. Conclusion -- Aknowledgements -- References -- Simulation as Formal and Generative Social Science: The Very Idea Nuno David, Jaime Simio Sachman and Helder Coelho -- 1. Introduction -- 2. An Ontological Confusion -- 2.1. Against Genemtive Sufficiency of Growing Artificial Societies From the Bottom Up -- 2.2. 'Generative' from a Philosophy of Computer Science Perspective -- 2.3. Refutation of Formal Deduction through Execution -- 3. The Role of Programming Languages -- 4. A Different Idea of Simulation -- Acknowledgements -- References -- A Compromise Between Reductionism and Non-Reductionism Eray Ozkural -- 1. Introduction -- 2. Information theory and complexity -- 2.1. Basic definitions -- 2.2. Physical complexity -- 2.3. Adequacy of algorithmic complexity -- 2.4. Shortcomings of algorithmic complexity -- 2.5. Applications and related work -- 3. Non-reductionism and physicalism -- 3.1. Non-reductionism in philosophy of mind.

3.2. Arguments from ignorance.