**Title**

*UNDERSTANDING NATURAL PHENOMENA: Self-Organization and Emergence in Complex Systems*

**Author**

Vinod Wadhawan

**Book details**

**Paperback:**514 pages

**Publisher:**CreateSpace Independent Publishing Platform; 2 edition (September 13, 2017)

**Language:**English

**ISBN-10:**1548527939

**ISBN-13:**978-1548527938

**Product Dimensions:**6.7 x 1.3 x 9.6 inches

**Shipping Weight:**2.2 pounds

Can be ordered directly from the CreateSpace eStore: https://www.createspace.com/7308929

Also available at:

https://www.amazon.com/Understanding-Natural-Phenomena-Self-Organization-Emergence/dp/1548527939/ref=sr_1_1?s=books&ie=UTF8&qid=1499318009&sr=1-1&keywords=vinod+wadhawan

and

http://www.amazon.in/Understanding-Natural-Phenomena-Self-organization-Emergence/dp/1548527939/ref=sr_1_1?s=books&ie=UTF8&qid=1500484507&sr=1-1&keywords=vinod+wadhawan

**Legend for the front cover**

A flower is a work of art, but there is
no artist involved. The flower evolved from lesser things which, in turn,
evolved from still lesser things, and so on, all the way down. For example, the
symmetry of a flower is the end result of a long succession of spontaneous
processes and events, as also of some simple ‘local rules’ in operation, all
constrained, even aided, by the infallible second law of thermodynamics for
‘open’ systems. In fact, the second law is the mother of all organizing
principles, leading to the enormous amounts of cumulative self-organization,
structure, symmetry, and ‘emergence’ we see in Nature.

**About the book**

Science
is all about trying to understand natural phenomena under the strict discipline
imposed by the celebrated scientific method. Practically all the systems we
encounter in Nature are dynamical systems, meaning that they evolve with time.
Among them there are the ‘simple’ or ‘simplifiable’ systems, which can be
handled by traditional, reductionistic science; and then there are 'complex’
systems, for which nonreductionistic approaches have to be attempted for
understanding their evolution. In this book the author makes a case that a good
way to understand a large number of natural phenomena, both simple and complex,
is to focus on their self-organization and emergence aspects. Self-organization
and emergence are rampant in Nature and, given enough time, their cumulative
effects can be so mind-boggling that many people have great difficulty
believing that there is no designer involved in the emergence of all the
structure and order we see around us. But it is really quite simple to
understand how and why we get so much ‘order for free’. It all happens because,
as ordained by the infallible second law of thermodynamics, all
‘thermodynamically open’ systems in our ever-expanding and cooling (and
therefore gradient-creating) universe constantly tend to move towards
equilibrium and stability, often ending up in ordered configurations. In other words,
order emerges because Nature tends to find efficient ways to annul gradients of
all types.

This book will help you acquire a good understanding of the essential features of many natural phenomena, via the complexity-science route. It has four parts: (1) Complexity Basics; (2) Pre-Human Evolution of Complexity; (3) Humans and the Evolution of Complexity; and (4) Appendices. The author gives centrestage to the second law of thermodynamics for ‘open’ systems, which he describes as ‘the mother of all organizing principles’. He also highlights a somewhat unconventional statement of this law: ‘Nature abhors gradients’.

The book is written at two levels, one of which hardly uses any mathematical equations; the mathematical treatment of some relevant topics has been pushed to the last part of the book, in the form of ten appendices. Therefore the book should be accessible to a large readership. It is a general-science book written in a reader-friendly language, but without any dumbing down of the narrative.

This book will help you acquire a good understanding of the essential features of many natural phenomena, via the complexity-science route. It has four parts: (1) Complexity Basics; (2) Pre-Human Evolution of Complexity; (3) Humans and the Evolution of Complexity; and (4) Appendices. The author gives centrestage to the second law of thermodynamics for ‘open’ systems, which he describes as ‘the mother of all organizing principles’. He also highlights a somewhat unconventional statement of this law: ‘Nature abhors gradients’.

The book is written at two levels, one of which hardly uses any mathematical equations; the mathematical treatment of some relevant topics has been pushed to the last part of the book, in the form of ten appendices. Therefore the book should be accessible to a large readership. It is a general-science book written in a reader-friendly language, but without any dumbing down of the narrative.

**Preface**

I am a
scientist and I take pride in the fact that we humans have invented and
perfected the all-important

*scientific method*for investigating natural phenomena. Wanting to understand natural phenomena is an instinctive urge in all of us. In this book I make a case that taking the complexity-science route for satisfying this urge can be a richly rewarding experience. Complexity science enables us (fully or partially) to find answers to even the most fundamental questions we may ask about ourselves and about our universe. We call them*the**Big Questions*: How did our universe emerge out of ‘nothing’ at a certain point in time; or is it that it has been there always? Why and how has structure arisen in our universe: galaxies, stars, planets, life forms? How did life emerge out of nonlife? How does intelligence emerge out of nonintelligence? These are difficult questions. But, as Mark Twain is said to have said, ‘there is something fascinating about science. One gets such wholesale of conjecture out of such a trifling investment of fact’. As you will see in this book, the Big Questions, as also many others, can be answered with a good amount of credibility by using just the following ‘trifling investment of facts’:
1.

*Gradients tend to be obliterated spontaneously*. Concentration gradients, temperature gradients, pressure gradients, etc. all tend to decrease spontaneously, till a state of equilibrium is reached, after which the gradients cannot fall any further. This is actually nothing but a nonstatistical-mechanical version of the*second law of thermodynamics*. [Why do gradients arise at all, at a cosmic level? The original cause of all gradients in the cosmos is the continual expansion and cooling of our universe. At the local (terrestrial) level, the energy impinging on our ecosphere from the Sun is the main factor creating gradients.]
2.

*It requires energy to prevent a gradient from annulling itself, or to create a new gradient*. A refrigerator works on this principle, as also so many other devices.
3.

*Left to themselves, things go from a state of less disorder to a state of more disorder, spontaneously.*This is the more familiar version of the second law of thermodynamics. Examples abound. Molecules in a gas occupy a larger volume spontaneously if the larger volume is made available to them; but there is practically no way they would occupy the smaller volume again, on their own.
4.

*If a system is not left to itself, i.e., if it is not an isolated system and can therefore exchange energy and/or matter with its surroundings, then a state of lower disorder*. [This is in keeping with the second law of thermodynamics, as generalized to cover ‘thermodynamically open’ systems also.] Growth of a crystal from a fluid is an example. A crystal has a remarkably high degree of order and design, even though there is no designer involved. To borrow a phrase from Stuart Kauffman, this is ‘order for free’.__can__sometimes arise locally
5.

*If a sustained input of energy drives a system far away from equilibrium, the system may develop a structure or tendencies which enable it to dissipate energy more and more*This is called__efficiently__.*dissipation-driven adaptive organization*. England (2013) has shown that all dynamical evolution is more likely to lead to structures and systems which get better and better at absorbing and dissipating energy from the environment.
6.

*The total energy of the universe is conserved.*This is known as the energy-conservation principle. Since energy and mass are interconvertible, the term ‘energy’ used here really means ‘mass plus energy’.
7.

*Natural phenomena are governed by the laws of quantum mechanics.*Classical mechanics, though adequate for understanding many day-to-day or ‘macroscopic’ phenomena, is only a special, limiting, case of quantum mechanics.
8.

*There is an uncertainty principle in quantum mechanics, one version of which says that the energy-conservation principle*The larger the violation of energy conservation, the smaller this duration is.__can__be violated, though only for a very small, well-specified duration.
9.

*It can be understood fully in terms of the second law of thermodynamics that in a system of interacting entities, entirely new (unexpected) behaviour or properties can arise if the interactions are appropriate and strong enough.*‘More is different’ (Anderson 1972). The technical term for this occurrence is*emergence*. Complexity science is mostly about self-organization and emergence, and we shall encounter many examples of them in this book. To mention a couple of them here: the emergence of life out of nonlife; and the emergence of human intelligence in a system of nonintelligent entities, namely the neurons. Interestingly, the second law of thermodynamics is itself an emergent law. The motion of a molecule is governed by classical or ‘Newtonian’ mechanics, which has time-reversal symmetry, meaning that if you could somehow reverse the direction of time, the Newtonian equations of motion would still hold. And yet, when you put a large number of these molecules together, there are interactions among them and there emerges a*direction*of time: Time increases in the direction in which overall disorder increases. As I shall discuss later in the book, even the causality principle is an emergent principle.
10.

*The dynamics of evolution of a complex system of interacting entities is mostly through the operation of ‘local rules’*. Chua (1998) has introduced the important notion of cellular nonlinear networks (CNNs), and enunciated a*local-activity dogma*. According to it, in order for a ‘nonconservative’ system or model to exhibit any form of complexity, the associated CNN parameters must be such that that either the cells or their couplings are*locally active*.
11.

*The most adaptable are the most likely to survive and propagate.*Any species, if it is not to become extinct, must be able to survive and propagate, in an environment in which there is always some intra-species and/or inter-species competition because different individuals may all have to fight for the same limited resources like food or space. The fittest individuals or groups for this task (i.e., the most*adaptable*ones) stand a greater chance of winning this game and, as a result, the population gets better and better (more adapted) at survival and propagation in the prevailing conditions: the more adaptable or ‘fitter’ ones are not only more likely to survive, but also stand a greater chance to pass on their genes to the next generation.
It is
remarkable that an enormous number and variety of natural phenomena can be understood
in terms of just these few ‘commonsense’ facts, by adopting the
complexity-science approach. Complexity science helps us understand, to a small
or large extent, even those natural phenomena which fall outside the scope of
conventional reductionistic science.

What is
complexity science, and how is its operational space different from that of
conventional science? Let us begin by answering the question: What does the
phrase ‘system under investigation’ mean in conventional science? Strictly
speaking, since everything interacts with everything else, the entire cosmos is
one big single system. But such an approach cannot take us very far because it
is neither tractable nor useful. So, depending on our interest, we define a
subsystem which is a ‘

*quasi-isolated system*’. A quasi-isolated system is an imaginary construct, such that what is outside it can be, to a good approximation, treated as an unchanging (usually large) ‘background’, or ‘heat bath’ etc. This approach is so common in conventional science that we just say ‘system’ when what we really mean is a carefully identified quasi-isolated system. An example from rocket science will illustrate the point. For predicting the initial trajectory of a rocket, we can assume safely that a truck moving an adequate distance away from the launching site will not affect the trajectory*significantly*. Conventional science deals mostly with such ‘simple’ or ‘simplifiable’ systems. Complexity science, by contrast, deals with systems which must be treated in their totality; for them it is mostly not possible to identify a ‘quasi-isolated subpart’.
By
definition, a complex system is one which comprises of a large number of
‘members’, ‘elements’ or ‘agents’, which interact substantially with one
another and with the environment, and which have the potential to generate
qualitatively new collective behaviour. That is, there can be an

*emergence*of new (unexpected) spatial, temporal, or functional structures or patterns. Different complex systems have different ‘degrees of complexity’, and the amount of information needed to describe the structure and function of a system is one of the measures of that degree of complexity (Wadhawan 2010).
‘Complexity’
is something we associate with a complex system (defined above). It is a
technical term, and does not mean the same thing as ‘complicatedness’.

The idea of
writing this book took shape when I was working on my book

*Smart Structures: Blurring the Distinction between the Living and the Nonliving*(Wadhawan 2007). Naturally, there was extensive exposure to concepts from complexity science. Like the subject of smart structures, complexity science also cuts across various disciplines, and highlights the basic unity of all science. The uneasy feeling grew in me that, in spite of the fact that complexity is so pervasive and important, it is not introduced as a well-defined subject even to science students. They are all taught, say, thermodynamics and quantum mechanics routinely, but not complexity science. Even among research workers, although a large number are working on one complex system or another (and not just in physics or chemistry, but also in biology, brain science, computational science, economics, etc.), not many have learnt about the basics of complexity science in a coherent manner at an early stage of their career. I have tried to write a book on complexity that takes this subject to the classroom at a fairly introductory but comprehensive level. There is no dumbing down of facts, even at the cost of appearing ‘too technical’ at times.
Here are
some examples of complex systems: beehives; ant colonies; self-organized
supramolecular assemblies; ecosystems; spin-glasses and other complex
materials; stock markets; economies of nations; the world economy; the global
weather pattern. The origin and evolution of life on Earth was itself a series
of emergent phenomena that occurred in highly complex systems. Evolution of
complexity is generally a one-way traffic: The new emergent features may (in
principle) be deducible from, but are not reducible to, those operating at the
next lower level of complexity. Reductionism stands discounted.

As I said
earlier, emergent behaviour is a hallmark of complex systems. Human
intelligence is also an emergent property: Thoughts, feelings, and purpose
result from the interactions among the neurons. Similarly, even memories are
emergent phenomena, arising out of the interactions among the large number of
‘unmemory-like’ fragments of information stored in the brain.

What goes on
in a complex system is essentially as follows: There is a large number of
interacting agents, which may be viewed as forming a

*network*. In the network-theory jargon, the agents are the ‘nodes’ of the network, and a line joining any two nodes (i.e., an ‘edge’) represents the interaction between that pair of agents. Any interaction amounts to communication or exchange of information. The action or behaviour of each agent is determined by what it ‘sees’ others doing, and its actions, in turn, determine what the other agents may do. Further, the term*game-playing*is used for this mutual interaction in the case of those complex systems in which the agents are ‘thinking’ organisms (particularly humans). Therefore a partial list of topics covered in this book is: information theory; network theory; cellular automata; game theory.
Exchange of
information in complex systems, controlled like other macroscopic phenomena by
the second law of thermodynamics, leads to self-organization and emergence. In
particular, biological evolution is a natural and inevitable consequence of
such ongoing processes, an additional factor for them being the cumulative
effects of

*mutations*and*natural selection*. This book has chapters on evolution of complexity of all types: cosmic, chemical, biological, artificial, cultural.
Networked or
‘webbed’ systems have the all-important

*nonlinearity*feature. In fact, nonlinear response, in conjunction with substantial departure from equilibrium, is the crux of complex behaviour. There are many types of nonlinear systems. The most important for our purposes in this book are those in which, although the output (*y*) is indeed proportional to the input (*x*), the proportionality factor (*m*) is not independent of the input; i.e.,*m*is not a constant factor, but rather varies with what*x*is. For a linear system we have*y = m x*, with m having a fixed value, not varying with*x*. But for a nonlinear system, the equation becomes*y*=*m*(*x*)*x*; now*m*is not a constant. This has far-reaching consequences for the (always networked) complex system. In particular, its future progression of events is very sensitive to conditions at any particular point of time (the so-called ‘initial conditions’). This sensitivity to initial conditions is also the hallmark of*chaotic systems*. In fact, there is a well-justified viewpoint that it is impossible to discuss several types of complex systems without bringing in concepts from chaos theory. And, what is more, complex systems tend to evolve to a configuration wherein they can operate near the so-called*edge of chaos*(neither too much order, nor too much chaos). There is a chapter on chaos which elaborates on these things.
Inanimate
systems can also be complex. Whirlpools and whirlwinds are familiar examples of
dynamic nonbiological complex systems. Even static physical systems like some
nanocomposites may exhibit properties that cannot always be deduced from those
of the constituents of the composite. A particularly fascinating class of
complex materials are the so-called

*multiferroics*. A multiferroic is actually a ferroic crystalline material (a ‘natural’ composite) which just refuses to be homogeneous over macroscopic length scales, so that the same crystal may be, say, ferroelectric in some part, and ferromagnetic in another. In a multiferroic, two or all three of the electric, magnetic and elastic interactions compete in a delicately balanced manner, and even a very minor local factor can tilt the balance in favour of one or the other. This class of materials offers great scope for basic research and for device applications, particularly in smart structures.
The current
concern about ecological conservation and global warming points to the need for
a good understanding of complex systems, particularly their holistic nature.
Mother Earth is a single, highly complex, system, now increasingly referred to
as

*the System Earth*.
A better
understanding of complexity may well become a matter of life and death for the
human race. And the subject of complexity science is still at the periphery of
science. It has not yet become mainstream, in the sense that it is not taught
routinely even at the college level. That cannot go on.

There are
already a substantial number of great books on complexity science, and I have
drawn on them. But I believe that this book is student-friendly and
teacher-friendly, and it brings home the all-pervasive nature of the subject.
Here are its salient features:

1. It
provides a comprehensive update on the subject.

2. It can
serve as introductory or supplementary reading for an undergraduate or graduate
course on any branch of complexity science.

3.
Practically all the mathematical treatment of the subject has been pushed to
the appendices at the end of the book, so the main text can be comprehended
even by those who are not too comfortable with equations. This is important
because a large fraction of the educated public must get the hang of the nature
of complexity, so that we can successfully meet the challenges posed to our
very survival as a species.

4. Both
among scientists and nonscientists there is a large proportion of people who
are insufficiently trained about the explaining power of complexity science
when it comes to some of the deepest puzzles of Nature and, hopefully, this
book would help remedy the situation to some extent.

5. The book
has a certain all-under-one-roof character. The topics covered are so many and
so diverse that it would be well-nigh impossible for a reader, specializing in
a particular branch of complexity science, not to get exposed to what is going on
in the rest of complexity science! This is important, because using the
insights gained in one complex system for trying to understand another complex
system is the hallmark of complexity science.

6. A proper
understanding of what complexity science has already achieved will also help
discredit many of the claims of mystics, supernaturalists, and
pseudoscientists.

Bengaluru

September, 2017

**Contents**

**Preface**

**I. Complexity Basics**

**1. Overview 1**

1.1 Preamble 3

1.2 A whirlpool as an
example of self-organization 5

1.3 Spontaneous pattern
formation: the Bénard instability 6

1.4 Recent history of
investigations in complexity science 8

1.5 Organization of the
book 8

**2. The Philosophical and Computational Underpinnings of Complexity Science 9**

2.1 The scientific
method for understanding natural phenomena 9

2.2 Reductionism and its
inadequacy for dealing with complexity 12

2.3 The Laplace demon 13

2.4 Holism 15

2.5 Emergence 16

2.6 Scientific
determinism, effective theories 17

2.7 Free will 18

2.8 Actions, reactions,
interactions, causality 21

2.9 The nature of
reality 23

**3. The Second Law of Thermodynamics 25**

3.1 The second law for
isolated systems 25

3.2 Entropy 26

3.3 The second law for
open systems 27

3.4 Nucleation and
growth of a crystal 29

3.5 The second law is an
emergent law 32

3.6 Emergence, weak and
strong 33

3.7 Nature abhors
gradients 33

3.8 Systems not in
equilibrium 34

3.9 Thermodynamics of
small systems 35

**4. Dynamical Evolution 37**

4.1 Dynamical systems 37

4.2 Phase-space
trajectories 37

4.3 Attractors in phase
space 38

4.4 Nonlinear dynamical
systems 40

4.5 Equilibrium, stable
and unstable 40

4.6 Dissipative
structures and processes 42

4.7 Bifurcations in
phase space 43

4.8 Self-organization
and order in dissipative structures 44

**5.Relativity Theory and Quantum Mechanics 47**

5.1.Special theory of
relativity 47

5.2 General theory of
relativity 49

5.3 Quantum mechanics 52

5.4 Summing over
multiple histories 55

**6.The Nature of Information 57**

6.1 Russell’s paradox 57

6.2 Hilbert’s formal
axiomatic approach to mathematics 58

6.3 Gödel’s
incompleteness theorem 59

6.4 Turing’s halting
problem 60

6.5 Elementary
information theory 63

6.6 Entropy means
unavailable or missing information 65

6.7 Algorithmic
information theory 66

6.8 Algorithmic
probability and Ockham’s razor 69

6.9 Algorithmic
information content and effective complexity 70

6.10 Classification of
problems in terms of computational complexity70

6.11 ‘Irreducible
complexity’ deconstructed 71

**7.Darwinian Evolution, Complex Adaptive Systems, Sociobiology 75**

7.1 Darwinian evolution
75

7.2 Complex adaptive
systems 77

7.3 The inevitability of
emergence of life on Earth 79

7.4.Sociobiology,
altruism, morality, group selection 81

**8. Symmetry is Supreme 83**

8.1 Of socks and shoes
83

8.2 Connection between
symmetry and conservation laws 83

8.3 Why so much
symmetry? 84

8.4 Growth of a crystal
as an ordering process 85

8.5 Broken symmetry 86

8.6 Symmetry aspects of
phase transitions 88

8.7 Latent symmetry 89

8.8 Latent symmetry and
the phenomenon of emergence in complex systems 90

8.9 Broken symmetry and
complexity 91

8.10 Symmetry of complex
networks 92

**9. The Standard Model of Particle Physics 95**

9.1 The four fundamental
interactions 95

9.2 Bosons and fermions
96

9.3 The standard model
and the Higgs mechanism 98

**10. Cosmology Basics 101**

10.1 The ultimate causes
of all cosmic order and structure 101

10.2 The Big Bang and
its aftermath 102

10.3 Dark matter and
dark energy 105

10.4 Cosmic inflation
108

10.5 Supersymmetry,
string theories, M-theory 109

10.6 Has modern
cosmology got it all wrong? 111

**11. Uncertainty, Complexity, and the Arrow of Time 117**

11.1 Irreversible
processes, and not entropy, determine the arrow of time 117

11.2 Irreversible
processes

*can*lead to order 117
11.3 The arrow of time
and the early universe 118

11.4 When did time
begin? 119

11.5 Uncertainty and
complex adaptive systems 120

**12. The Cosmic Evolution of Complexity 123**

12.1 Our cosmic history
123

12.2 We are star stuff
124

**13. Why Are the Laws of Nature What They Are? 127**

13.1 The laws of Nature
in our universe 127

13.2 The anthropic
principle 128

**14. The Universe is a Quantum Computer 131**

14.1 Quantum computation
131

14.2 Quantum
entanglement 132

14.3 The universe
regarded as a quantum computer 133

**15. Chaos, Fractals, and Complexity 135**

15.1 Nonlinear dynamics
135

15.2 Extreme sensitivity
to initial conditions 136

15.3 Chaotic rhythms of
population sizes 137

15.4 Fractal nature of
the strange attractor 139

15.5 Chaos and
complexity 141

**16. Cellular Automata as Models of Complex Systems 143**

16.1 Cellular automata
143

16.2 Conway’s Game of
Life 143

16.3 Self-reproducing
automata 145

16.4 The four Wolfram
classes of cellular automata 146

16.5 Universal cellular
automata 147

**17. Wolfram’s ‘New Kind of Science’ 149**

17.1 Introduction 149

17.2 Wolfram’s principle
of computational equivalence (PCE) 150

17.3 The PCE
and the rampant occurrence of complexity 151

17.4 Why does the
universe run the way it does? 152

17.5 Criticism of
Wolfram’s NKS 153

**18. Swarm Intelligence 157**

18.1 Emergence of swarm
intelligence in a beehive 157

18.2 Ant logic 159

18.3 Positive and
negative feedback in complex systems 160

**19. Nonadaptive Complex Systems 163**

19.1 Composite materials
163

19.2 Ferroic materials
163

19.3 Multiferroics 164

19.4 Spin glasses 165

19.5 Relaxor
ferroelectrics 166

19.6 Relaxor
ferroelectrics as vivisystems 167

**20. Self-Organized Criticality, Power Laws 169**

20.1 The sandpile
experiment 169

20.2 Power-law behaviour
and complexity 170

20.3 Robust and
nonrobust criticality 173

**21. Characteristics of Complex Systems 175**

**II. Pre-Human Evolution of Complexity**

**22. Evolution of Structure and Order in the Cosmos 183**

22.1 The three eras in
the cosmic evolution of complexity 183

22.2 Chaisson’s
parameter for quantifying the degree of complexity 183

22.3 Cosmic evolution of
information 184

22.4

**Why so much terrestrial complexity? 186****23. The Primary and Secondary Chemical Bonds 187**

23.1 The primary
chemical bonds 187

23.2 The secondary
chemical bonds 189

23.3 The hydrogen bond
and the hydrophobic interaction 190

**24. Cell Biology Basics 193**

**25. Evolution of Chemical Complexity 197**

25.1 Of locks and keys
in the world of molecular self-assembly 197

25.2 Self-organization
of matter 199

25.3 Emergence of
autocatalytic sets of molecules 202

25.4 Positive feedback,
pattern formation, emergent phenomena 204

25.5 Pattern formation:
the BZ reaction 205

**26. What is Life? 207**

26.1 Schrödinger and
life 207

26.2 Koshland’s ‘seven
pillars of life’ 209

**27. Models for the Origins of Life 211**

27.1 The early work 211

27.2 The RNA-world model
for the origin of life 213

27.3 Dyson’s
proteins-first model for the origins of life 215

27.4 Why was evolution
extremely fast for the earliest life? 218

**28. Genetic Regulatory Networks and Cell Differentiation 219**

28.1 Circuits in genetic
networks 220

28.2 Kauffman’s work on
genetic regulatory networks 221

**29. Ideas on the Origins of Species: From Darwin to Margulis 223**

29.1 Darwinism and
neo-Darwinism 223

29.2 Biological
symbiosis and evolution 225

29.3 What is a species
227

29.4 Horizontal gene
transfer in the earliest life forms 228

29.5 Epigenetics 229

**30. Coevolution of Species 231**

30.1 Punctuated
equilibrium in the coevolution of species 231

30.2 Evolutionarily
stable strategies 232

30.3 Of hawks and doves
in the logic of animal conflicts 234

30.4 Evolutionary arms
races and the life-dinner principle 236

**31. The Various Energy Regimes in the Evolution of Our Ecosphere 241**

31.1 The thermophilic
energy regime 242

31.2 The phototrophic
energy regime 244

31.3 The aerobic energy
regime 245

**III. Humans and the Evolution of Complexity**

**32. Evolution of Niele’s Energy Staircase After the Emergence of Humans 249**

32.1 The pyrocultural
energy regime 249

32.2 The agrocultural
energy regime 251

32.3 The carbocultural
energy regime 252

32.4 The green-valley
approach to System Earth 253

32.5 The imperial
approach to System Earth 254

32.6 A nucleocultural
energy regime? 256

32.7 A possible
‘heliocultural’ energy regime 258

**33. Computational Intelligence 261**

33.1 Introduction 261

33.2 Fuzzy logic 262

33.3 Neural networks,
real and artificial 263

33.4 Genetic algorithms
265

33.5 Genetic
programming: Evolution of computer programs 267

33.6
Artificial life 271

**34. Adaptation and Learning in Complex Adaptive Systems 273**

34.1 Holland’s model for
adaptation and learning 273

34.2 The bucket brigade
in Holland’s algorithm 274

34.3
Langton’s work on adaptive computation 276

34.4
The edge-of-chaos existence of complex adaptive systems 278

**35. Smart Structures 281**

35.1 The three main
components of a smart structure 281

35.2 Reconfigurable
computers and machines that can evolve 283

**36. Robots and Their Dependence on Computer Power 287**

36.1 Behaviour-based
robotics 287

36.2 Evolutionary
robotics 288

36.3 Evolution of computer
power per unit cost 290

**37. Machine Intelligence 295**

37.1 Artificial
distributed intelligence 295

37.2 Evolution of
machine intelligence 296

37.3 The future of
intelligence and the status of humans 298

**38. Evolution of Language 303**

**39. Memes and Their Evolution 307**

**40. Evolution of the Human Brain, and the Nature of Our Neocortex 311**

40.1 Evolution of the
brain 312

40.2 The human neocortex
313

40.3 The history of
intelligence 315

**41. Minsky’s and Hawkins’ Models for how Our Brain Functions 319**

41.1 Marvin Minsky’s
‘Society of Mind’ 319

41.2 Can we make
decisions without involving emotions? 320

41.3 Hawkins’ model for
intelligence and consciousness 323

**42. Inside the Human Brain 325**

42.1 Probing the human
Brain 325

42.2 Peering into the
human brain 327

**43. Kurzweil’s Pattern-Recognition Theory of Mind 331**

**44. The Knowledge Era and Complexity Science 337**

44.1 The wide-ranging
applications of complexity science 337

44.2 Econophysics 338

44.3 Application of
complexity-science ideas in management science 341

44.4 Cultural evolution
and complexity transitions 343

44.5 Complexity
leadership theory 345

44.6 Complexity science
in everyday life 345

**45. Epilogue 347**

**IV. Appendices**

**A1. Equilibrium Thermodynamics and Statistical Mechanics 357**

A1.1 Equilibrium
thermodynamics 357

A1.2 Statistical
mechanics 360

A1.3 The ergodicity
hypothesis 360

A1.4 The partition
function 361

A1.5 Tsallis
thermodynamics of small systems 361

**A2. Probability Theory 365**

A2.1 The notion of
probability 365

A2.2 Multivariate
probabilities 365

A2.3 Determinism and predictability
367

**A3. Information and Uncertainty 369**

A3.1 Information theory
369

A3.2 Shannon’s formula
for a numerical measure of information 370

A3.3 Shannon entropy and
thermodynamic entropy 371

A3.4 Uncertainty 372

A3.5 Algorithmic
information theory 373

**A4. Thermodynamics and Information 375**

A4.1 Entropy and
information 375

A4.2 Kolmogorov-Sinai
entropy 376

A4.3 Mutual information
and redundancy of information 377

**A5. Systems Far from Equilibrium 379**

A5.1 Emergence of
complexity in systems far from equilibrium 379

A5.2 Nonequilibrium
classical dynamics 380

A5.3 When does the
Newtonian description break down? 383

A5.4 Generalization of
Newtonian dynamics 384

A5.5 Pitchfork
bifurcation 386

A5.6 Extension of
Newton’s laws 386

**A6. Quantum Theory and Particle Physics 389**

A6.1 Introduction 389

A6.2 The Heisenberg
uncertainty principle 389

A6.3 The Schrödinger
equation 390

A6.4 The Copenhagen
interpretation 391

A6.5 Time asymmetry 391

A6.6 Multiple universes
391

A6.7 Feynman’s
sum-over-histories formulation 392

A6.8 Quantum Darwinism
393

A6.9 Gell-Mann’s
coarse-graining interpretation 393

A6.10 Poincaré
resonances and quantum theory 394

A6.11 Model-dependent
realism, intelligence, existence 396

A6.12 The principle of
conservation of quantum information 397

A6.13 Particle physics
398

**A7. Theory of Phase Transitions and Critical Phenomena 401**

A7.1 A typical phase
transition 401

A7.2 Liberal definitions
of phase transitions 401

A7.3 Instabilities can
cause phase transitions 402

A7.4 Order parameter of
a phase transition 403

A7.5 The response
function corresponding to the order parameter 404

A7.6 Phase transitions
near thermodynamic equilibrium 404

A7.7 The Landau theory
of phase transitions 405

A7.8 Spontaneous
breaking of symmetry 407

A7.9 Field-induced phase
transitions 407

A7.10 Ferroic phase
transitions 408

A7.11 Prototype symmetry
409

A7.12 Critical phenomena
409

A7.13 Universality
classes and critical exponents 410

**A8. Chaos Theory 413**

A8.1 The logistic
equation 413

A8.2 Lyapunov exponents
416

A8.3 Divergence of
neighbouring trajectories 417

A8.4 Chaotic attractors
419

**A9. Network Theory and Complexity 421**

A9.1 Graphs 421

A9.2 Networks 425

A9.3 The
travelling-salesman problem 426

A9.4 Random networks 427

A9.5 Percolation
transitions in random networks 428

A9.6 Small-world
networks 429

A9.7 Scale-free networks
431

A9.8 Evolution of
complex networks 432

A9.9 Emergence of
symmetry in complex networks 433

A9.10 Chua’s cellular
nonlinear networks as a paradigm for emergence and complexity 435

**A10. Game Theory 439**

A10.1 Introduction 439

A10.2 Dual or two-player
games 442

A10.3 Noncooperative
games 449

A10.4 Nash equilibrium
450

A10.5 Cooperative games
450

**Bibliography 453**

**Index 481**

**Acknowledgements 491**

**About the Author 492**

----------------------------------------------------------------

**NOTE ADDED ON 13th September 2017**

The second edition of
this book was published today. A number of corrections and other improvements
have been incorporated. The font size has been reduced by 10%. New information
has been added, and some less relevant material has been removed. Accordingly,
the list of contents etc. underwent some minor changes, which have been
incorporated in this blog post.