# Teaching & Researching Big History: Exploring a New Scholarly Field

Leonid E Grinin, David Baker, Esther Quaedackers, Andrey Korotayev

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Leonid E Grinin, David Baker, Esther Quaedackers, Andrey Korotayev (Dir.). Teaching & Researching Big History: Exploring a New Scholarly Field. Uchitel, pp.368, 2014, Grinin, Leonid, 978-5-7057-4027-7. hprints-01861224

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Teaching & Researching

**BIG HISTORY:**

EXPLORING A NEW SCHOLARLY FIELD
<figure><img src="image_1.png" /></figure>
INTERNATIONAL BIG HISTORY ASSOCIATION

RUSSIAN ACADEMY OF SCIENCES
INSTITUTE OF ORIENTAL STUDIES

The Eurasian Center for Big History and System Forecasting

TEACHING & RESEARCHING
BIG HISTORY:
EXPLORING
A NEW SCHOLARLY FIELD

Edited by

Leonid Grinin, David Baker,
Esther Quaedackers, and Andrey Korotayev

<figure><img src="image_2.png" /></figure>

'Uchitel'
Publishing House
Volgograd
Editorial Council:

Cynthia Stokes Brown

Ji-Hyung Cho

David Christian

Barry Rodrigue

**Teaching & Researching Big History: Exploring a New Scholarly Field /**
*Edited by Leonid E. Grinin, David Baker, Esther Quaedackers, and Andrey V. Korotayev.* – Volgograd: ‘Uchitel’ Publishing House, 2014. – 368 pp.

According to the working definition of the International Big History Association, ‘Big History seeks to understand the integrated history of the Cosmos, Earth, Life and Humanity, using the best available empirical evidence and scholarly methods’. In recent years Big History has been developing very fast indeed. Big History courses are taught in the schools and universities of several dozen countries. Hundreds of researchers are involved in studying and teaching Big History. The unique approach of Big History, the interdisciplinary genre of history that deals with the grand narrative of 13.8 billion years, has opened up a vast amount of research agendas. Big History brings together constantly updated information from the scientific disciplines and merges it with the contemplative realms of philosophy and the humanities. It also provides a connection between the past, present, and future. Big History is a colossal and extremely heterogeneous field of research encompassing all the forms of existence and all timescales. Unsurprisingly, Big History may be presented in very different aspects and facets. In this volume the Big History is presented and discussed in three different ways. In its first part, Big History is explored in terms of methodology, theories of knowledge, as well as showcasing the personal approach of scholars to Big History. The second section comprises such articles that could clarify Big History’s main trends and laws. The third part of this book explores the nature of teaching Big History as well as profiling a number of educational methods.

This volume will be useful both for those who study interdisciplinary macroproblems and for specialists working in focused directions, as well as for those who are interested in evolutionary issues of Astrophysics, Geology, Biology, History, Anthropology, Linguistics and other areas of study.

'Uchitel' Publishing House
143 Kirova St.,
400079 Volgograd,
Russia

ISBN 978-5-7057-4027-7

© 'Uchitel' Publishing House, 2014
# Contents

<table><tr><td>Introduction. Big History's Big Potential</td><td>7</td></tr></table>

## Section 1. Understanding and Explaining Big History

<table><tr><td>David Christian</td><td>Swimming Upstream: Universal Darwinism and Human History</td><td>19</td></tr><tr><td>David Baker</td><td>Standing on the Shoulders of Giants: Collective Learning as a Key Concept in Big History</td><td>41</td></tr><tr><td>Lowell Gustafson</td><td>From Particles to Politics</td><td>65</td></tr><tr><td>Esther Quaedackers</td><td>To See the World in a Building: A Little Big History of Tiananmen</td><td>90</td></tr><tr><td>Sun Yue</td><td>Chinese Traditions and Big History</td><td>112</td></tr><tr><td>Ken Gilbert</td><td>The Universal Breakthroughs of Big History: Developing a Unified Theory</td><td>122</td></tr><tr><td>Ekaterina Sazhienko</td><td>The Future of Global Civilization: Commentary of Big Historians</td><td>147</td></tr></table>

## Section 2. Big History's Phases, Regularities, and Dimensions

<table><tr><td>Leonid E. Grinin</td><td>The Star-Galaxy Era of Big History in the Light of Universal Evolutionary Principles</td><td>163</td></tr><tr><td>Andrey V. Korotayev,<br/>Alexander V. Markov</td><td>Mathematical Modeling of Biological and Social Phases of Big History</td><td>188</td></tr><tr><td>Ken Baskin</td><td>The Dynamics of Evolution: What Complexity Theory Suggests for Big History's Approach to Biological and Cultural Evolution</td><td>220</td></tr></table>
<table><tr><td>Abel A. Alves</td><td>The Animals of the Spanish Empire: Humans and Other Animals in Big History</td><td>248</td></tr><tr><td>Craig G. Benjamin</td><td>Big History, Collective Learning and the Silk Roads</td><td>265</td></tr><tr><td>Barry Rodrigue</td><td>Retrofitting the Future</td><td>276</td></tr><tr><td>Joseph Voros</td><td>Galactic-Scale Macro-engineering: Looking for Signs of Other Intelligent Species, as an Exercise in Hope for Our Own</td><td>283</td></tr></table>

Section 3. Teaching Big History

<table><tr><td>Michael Duffy, D'Neil Duffy</td><td>Big History and Elementary Education</td><td>305</td></tr><tr><td>Tracy Sullivan</td><td>Big History and the Secondary Classroom: A Twenty-First Century Approach to Interdisciplinarity?</td><td>317</td></tr><tr><td>Cynthia Stokes Brown</td><td>Constructing a Survey Big History Course</td><td>328</td></tr><tr><td>John Fowler</td><td>Cosmology, Mythology, and the Timeline of Light</td><td>336</td></tr><tr><td>Jonathon Cleland Host</td><td>Big History Beads: A Flexible Pedagogical Method</td><td>347</td></tr><tr><td>Contributors to the volume</td><td></td><td>363</td></tr></table>
# Introduction

## Big History's Big Potential

Leonid E. Grinin, David Baker,
Esther Quaedackers, and Andrey V. Korotayev

Big History has been developing very fast indeed. We are currently observing a 'Cambrian explosion' in terms of its popularity and diffusion. Big History courses are taught in the schools and universities of several dozen countries, including China, Korea, the Netherlands, the USA, India, Russia, Japan, Australia, Great Britain, Germany, and many more. The International Big History Association (IBHA) is gaining momentum in its projects and membership. Conferences are beginning to be held regularly (this edited volume has been prepared on the basis of the proceedings of the International Big History Association Inaugural Conference [see below for details]). Hundreds of researchers are involved in studying and teaching Big History.

What is Big History? And why is it becoming so popular? According to the working definition of the IBHA, 'Big History seeks to understand the integrated history of the Cosmos, Earth, Life and Humanity, using the best available empirical evidence and scholarly methods'.

The need to see this process of development holistically, in its origins and growing complexity, is fundamental to what drives not only science but also the human imagination. This shared vision of the grand narrative is one of the most effective ways to conceptualize and integrate our growing knowledge of the Universe, society, and human thought. Moreover, without using 'mega-paradigms' like Big History, scientists working in different fields may run the risk of losing sight of how each other's tireless work connects and contributes to their own.

Scientific specialization and the immense amounts of information contained in the various 'silos' of academia can hinder our capacity for inclusiveness, but, paradoxically, it also amplifies the need for it. Many scientists would like a more integrated vision that sees beyond their meticulous and complicated fields of specialization. One can see the growth of such interest in the framework of individual disciplines, as well as in interdisciplinary research. Yet, while interdisciplinarity is not
a new idea, many disciplines can run the disappointing tendency of on-
ly paying lip-service to it. This is not possible in Big History. In a disci-
pline that *starts* by weaving together all the disciplines into a single nar-
rative, interdisciplinary work is not only possible, it is essential.
A unification of disciplines, a deep symbiosis of academic cells, will
open up research areas that are vital to the development of the twenty-
first century thought and culture. As has been mentioned on a number
of occasions, the rapidly globalizing world needs global knowledge that
explains a unified global system (see Grinin, Korotayev, Carneiro, and
Spier 2011; Grinin and Korotayev 2009). Indeed, globalization itself be-
comes a vehicle for Big History. The very existence of the International
Big History Association is proof of that.

Big History ideas did not appear out of nowhere. They have deep
roots in human spirituality, philosophy, and science. In the nineteenth
and twentieth centuries, there was an explosive growth of scientific
knowledge accompanied by a deep differentiation of disciplines. This
made borders between scholars and scientists much more rigid, while
research specialization grew by an order of magnitude. As Erwin
Schrödinger justly noted: '[I]t has become next to impossible for a single
mind fully to command more than a small specialized portion of it'.
However, he continued, there is 'no other escape from this dilemma
(lest our true aim be lost forever) than that some of us should venture to
embark on a synthesis of facts and theories' (Schrödinger 1944: 1). As
disintegration peaked in the twentieth century, such undertakings were
not mentioned as often as they ought to have been. When an interdisci-
plinary synthesis was mentioned at all, it was seen as a lofty goal, the
barest whisper of a dream, rather than an approachable reality.

A very different picture appears if we look further back in the his-
tory of human thought. From the very moment of their emergence, grand
unified theories of existence tended to become global. Even the Abra-
hamic theological tradition, that was dominant in the western half of the
Afroeurasian world-system in the Late Ancient and Medieval periods,
contains a sort of proto-Big History. It presents a unified vision of
the Universe's origin, development, and future. In that grand narrative,
the Universe has a single point of creation and it develops according to
a divine plan. Similarly, classical Indian religious philosophy loosely
resembles the principle of the unity of the world through the idea of
reincarnation, in a Hindu approximation of the First Law of Thermody-
namics. Even the delusions of astrologers and alchemists contained the
idea of universal interconnectedness (stars and planets affect human
fates; everything can be transformed into everything else). This is only a fragment of the pre-modern ideas that contained elements of Big History thinking. Many interesting insights on the properties of the Universe can be found in pre-scientific worldviews generated by various human civilizations.

Ancient philosophy even aspired to find the single principle cause for everything that exists.<fnref n="1" /> This was done in a very insightful way in the works of the ancient Greeks, who were especially interested in the origins and nature of the Universe. Note that even while Greek (and, more generally, classical) philosophy concentrated on ethical or aesthetic issues, it was still dominated by the idea of the single law of *Logos* that governed the whole Universe, with many different interpretations of it provided by various thinkers. This was reinforced by the concept of a 'cosmic circulation' that also influenced human society. Medieval philosophy inherited the Greek tradition 'to comprehend the universe on the basis of archetypal principles ... as well as the inclination to detect clarifying universals in the chaos of the life' (Tarnas 1991: 3–5).

The transition from the geocentric (Ptolemaic) to the heliocentric (Copernican) perspective took many centuries notwithstanding all the brilliant conjectures of Giordano Bruno (1548–1600). Discoveries by Johannes Kepler (1571–1630), Galileo Galilei (1564–1642), and Isaac Newton (1643–1727) produced a majestic vision of the Universe. For the first time in history, a more advanced form of Big History thinking was produced – not by the speculations of philosophers or theologians but on the basis of corroborated facts and mathematically formulated laws of Nature. 'Mechanicism' became the dominant paradigm in the western scientific thought (including the social sciences). Thus the formation of a unified scientific worldview was consolidated. 'Natural philosophy', the precursor term for science, investigated everything from the highly cosmological to the deeply sociological and continued to preserve its dominant position in the eighteenth century: the age of the Enlightenment (see Barg 1987; Grinin 2012 for more details).

However, new ideas stressing historical variability soon emerged. Those ideas and discoveries led to a crisis of the dominant scientific paradigm. In geology, Georges-Louis Leclerc, Comte de Buffon, systematized all the known empirical data and analyzed a number of important theo-

<footnote n="1">¹ In particular, in the classical Indian philosophy one finds the belief in the 'eternal moral order' of the Universe as well as ideas of the collossality of the world space and time, infinity of the Universe comprising millions of such worlds as our Earth (see, e.g., Chatterjee and Datta 1954).</footnote>
retical issues of the development of the Earth and its surface. He also pro-
duced a few insights that turned out to be important for the development
of the theory of biological evolution. The hypothesis of the emergence of
the Solar System from a gas nebula was first spelled out by philosopher
Immanuel Kant and later by mathematician and astronomer Pierre-
Simon Laplace in one of the notes to his multivolume *Mécanique Céleste*
(1799-1825).

Some of the philosophical roots of evolutionary ideas are very old in-
deed, and scientifically based evolutionary ideas first emerged in the sev-
enteenth and eighteenth centuries. But the idea of universal evolution
only became really influential in the nineteenth century. The first major
evolutionary theory in biology was produced by Jean-Baptiste Lamarck
(1744-1829), who advocated change via acquired traits. Another no less
evolutionary theory was formulated in geology by Charles Lyell (1797-
1875) who, in his *Principles of Geology* (1830-1833), refuted the theory of
catastrophism.

It is no coincidence that the first narratives beginning to resemble
modern Big Histories first emerged around that time. The first real con-
certed and conscious attempt to unify the story of the physical processes
of the Universe to the dynamics of human society was made by Alexan-
der von Humboldt (1769-1859), a Prussian natural philosopher, who set
out to write *Kosmos* (1845-1859), but died before he could complete it.
Also, Robert Chambers anonymously published the *Vestiges of the Natu-
ral History of Creation* in 1844. His book began with the inception of the
Universe in a fiery mist and ended with a history of humanity.

In the second half of the nineteenth century, the concept of evolu-
tion by natural selection as pioneered by Charles Darwin (1859) and
Alfred Russel Wallace (1858) merged with the idea of social progress
espoused by Herbert Spencer (1857, 1862, 1896) and became a major in-
fluence on western thought. The idea of evolution/progress as a transi-
tion from less to more complex systems dramatically transformed the
human worldview.<fnref n="2" /> It became known that stars and planets, including
the Sun and the Earth, are objects that have their origin, history, and
end. There was a great deal of indication that revolutionary changes in
astronomy were forthcoming.

Two discoveries produced the most important contribution to the
emergence of Big History. First, the interpretation of the redshift by

<footnote n="2">² Note that although Spencer paid more attention to biological and social evolution, he treated evolution as a universal process taking place at all possible levels – from micro-organisms to galaxies.</footnote>
Edwin Hubble in the 1920s demonstrated that the Universe is not static and eternal, but is in a general state of expansion, as if it began with a primordial 'explosion'. By the 1940s, interacting teams of physicists and astronomers from around the world speculated on the existence of left-over radiation from this event – cosmic microwave background radiation. This radiation was detected in 1964 by Arno Penzias and Robert Wilson and provides the most convincing observational evidence for the explosive beginning of our Universe, which in the late 1940s George Gampow and Fred Hoyle called the 'Big Bang'. The simple epithet became useful for the theory's supporters. Moreover, the emergence of *historical* evidence for a point of origin of the Universe established a sense of chronology and transformed astrophysics into a historical science. The door firmly swung open for scholars of all shades to produce a universal history, called, to use our own simple epithet, 'Big History'.

By the last decades of the twentieth century, it became clear that the natural sciences contained a clear narrative from the Big Bang to modern day and this unity began to find expression in an increasing number of written works. For the first time it was actually possible for the mainstream to grasp the entire chronology.<fnref n="3" /> This began the process of thinking about both natural and human history as part of the unified whole. In 1980, astrophysicist Eric Jantsch wrote *The Self-Organizing Universe* (Jantsch 1980), now sadly out of print, which tied together all universal entities into a collection of processes. It constitutes the first modern unifying Big History. Jantsch did a credible job of examining human history as an extension of cosmic evolution and as just one of many structures operating beyond thermodynamic equilibrium. Jantsch's work constitutes the first attempt to find a common strand or dynamic that streamlines, unites, and underwrites the entire grand narrative. It is thus possible to explore history from the Big Bang to modern day without being weighed down by the scale of the chronology.

Around the same time American-based astrophysicists, geologists, and biologists such as Preston Cloud, Siegfried Kutter, George Field, and Eric Chaisson began writing and teaching courses about the cosmic story. Then, at the end of the 1980s, history and psychology professors like David Christian in Sydney, John Mears in Dallas, and Akop Nazaretyan in Moscow<fnref n="4" /> began to craft grand narratives that incorporated

<footnote n="3">³ A phenomenon best discussed in David Christian's 'The Evolutionary Epic and the Chronometric Revolution' (2009).</footnote>

<footnote n="4">⁴ For more details on the Russian Big History tradition see Nazaretyan 2011.</footnote>
the human story more seamlessly into a larger universal narrative. Fred Spier did the same at Amsterdam and Eindhoven. From here, a Cambrian-style explosion of courses and works has occurred.<fnref n="5" />

Eric Chaisson's *Cosmic Evolution* (2001) defines the unifying theme of Big History as the rise of complexity. Chaisson even proposed a way of objectively measuring this trend. Free energy rate density is the energy per second that flows through an amount of mass. In this way Chaisson empirically established that complexity has been rising in the Universe for 13.8 billion years. The theme of rising complexity was incorporated into David Christian's *Maps of Time* (2004) which further employed it in the human tale. The book also coincided with John and William McNeill's *The Human Web* (2003) and went back further to the beginning of time, for which William McNeill (somewhat superlatively and, one hopes, humorously) compared himself to John the Baptist and David Christian to Jesus of Nazareth for historicizing the natural sciences. Fred Spier, most recently in his book, *Big History and the Future of Humanity* (2010), has emphasized the Goldilocks principle, and how the rise of complexity occurs when conditions like temperature, pressure, and radiation are 'just right' for the rise of complexity to occur. Spier asserts that the rise of complexity combined with energy flows and the Goldilocks principle form the beginnings of an overarching theory of Big History.

The unique approach of Big History, the interdisciplinary genre of history that deals with the grand narrative of 13.8 billion years, has opened up vast research agendas. Or, to engage an evolutionary metaphor, it has triggered a scholarly speciation event where hundreds of new niches have opened up waiting to be filled. The ecological terrain is vast and the numbers that currently populate it are few. The research comes in a variety of forms. We, big historians, must collaborate very closely to pursue this vibrant new field. Our world is immensely diverse and unlimited in its manifestations. However, fundamentally it is one world – that is why it is so important to study those fundamentals.

Hence the International Big History Association was formed on 20 August 2010, at the Geological Observatory at Coldigioco in Italy.

<footnote n="5">⁵ For recent survey of size and of the field see Rodrigue, Stasko 2009; and the canon of seminal works includes but is not confined to Fred Spier's *The Structure of Big History: From the Big Bang until Today* (1996), David Christian's *Maps of Time: An Introduction to Big History* (2004), Eric Chaisson's *Epic of Evolution: Seven Ages of the Cosmos* (2006), Cynthia Stokes Brown's *Big History: From the Big Bang to the Present* (2007), and *Evolution: A Big History Perspective* (Grinin, Korotayev, and Rodrigue 2011).</footnote>
Subsequently, there was some tireless work involved in arranging the first conference in Grand Rapids, Michigan, in August 2012. Anyone who attended the first conference could not help but feel a little encouraged. We established a fraternity of researchers and educators from every corner of the globe. Numerous presentations were given on a diverse range of projects and we were given demonstrations of Chronozoom and the Big History Project. There is, however, a long road ahead of us as a discipline. One of the most important tasks of big historians in the coming years is to prove that Big History can sustain a wide number of empirically rigorous and truly interdisciplinary research projects. These conference proceedings are a sample made by the IBHA Publications Committee of the excellent work done on the many dynamics of the grand narrative and the best methods of teaching it.

## STRUCTURE AND SECTIONS

Big History brings together constantly updated information from the scientific disciplines and merges it with the contemplative realms of philosophy and the humanities. It also provides a connection between the past, present, and future. Big History is a colossal and extremely heterogeneous field of research encompassing all the forms of existence and all timescales. Unsurprisingly, Big History may be presented in very different aspects and facets. One way of dividing it is 1) methodology and the theory of knowledge, 2) ontological aspects, and 3) pedagogy. This volume is consequently structured in the following way:

- **Section 1. Understanding and Explaining Big History** in which Big History is explored in terms of methodology, theories of knowledge, as well as showcasing the personal approach of scholars to Big History.

- **Section 2. Big History's Phases, Regularities, and Dimensions** is connected with ontological aspects. A mental dissection of the whole into its parts is one of the most important tools of scientific cognition.

- **Section 3. Teaching Big History** explores the nature of teaching Big History as well as profiling a number of educational methods.

The first section of the volume stresses the unity of Big History. The second section comprises the articles that could clarify Big History's main trends and laws. The third section shows how that scholarly knowledge is transformed to the benefit of future generations. Naturally, in a field as interwoven as Big History, there is some overlap in the ideas and arguments contained in all three of these sections.
**1. Understanding and Explaining Big History**

David Christian's *Swimming Upstream: Universal Darwinism and Human History* shows how the patterns in cosmic, quantum, and biological evolution are connected to cultural evolution, especially in relation to his concept of Collective Learning. David Baker's *Standing on the Shoulders of Giants: Collective Learning as a Key Concept in Big History* presents research on the evolutionary history of Collective Learning in hominines, its role in the history of agrarian civilizations, and explains how this form of Universal Darwinism is deeply connected to the wider rise of complexity in the Universe. Lowell Gustafson's highly entertaining *From Particles to Politics* bestows a new perspective on the entire grand narrative through the lens of a political scientist and with the use of political metaphors for a variety of physical processes, showing the 'body politic' of everything from atoms to apes. Esther Quaedackers' *To See the World in a Building: A Little Big History of Tiananmen* explores how the history of one single thing can reflect back the many physical processes of Big History and how Little Big Histories can be used as a fertile research agenda for scholars of any discipline. Esther Quaedackers invented Little Big Histories in 2007 and the concept has since been adopted by the Big History Project and also forms the basis for each episode of H2's *Big History* series. Sun Yue's *Chinese Traditions and Big History* outlines some of the challenges for Big History in the world's most populous nation and also compares some of the key features of cosmic evolution to strikingly similar ones found in traditional Chinese philosophy. Ken Gilbert's *The Universal Breakthroughs of Big History: Developing an Unified Theory* explores how the concept of 'thresholds', as seen through a Gouldian framework, could potentially lead to an overarching theory of Big History that unites cosmology, biology, and human history. Ekaterina Sazhienko's *Future of Global Civilization: Commentary of Big Historians* compiles data from interviews with various people connected to Big History about the prospects for humanity and the future of complexity. The work touches on opinions of big historians about many areas of the grand narrative and uses them to take on the brave, if idealistic, task of figuring out what should be done to address the most crippling problems of the twenty-first century.

**2. Big History's Phases, Regularities, and Dimensions**

Leonid Grinin's *The Star-Galaxy Era of Big History in the Light of Universal Evolutionary Principles* is an in-depth view of how Universal Darwinism operates in the stelliferous section of the grand narrative. A startling
number of similarities occur at this level, resembling both biological and cultural evolution and governing the life and death of stars – without which further evolutionary processes and the rise of complexity would be impossible. Andrey Korotayev and Alexander Markov's *Mathematical Modeling of Biological and Social Phases of Big History* explores how macropatterns of evolution are similar at both the biological and social phases, and goes even further to explain how these processes can be charted and effectively described by mathematical models. Ken Baskin's *The Dynamics of Evolution: What Complexity Theory Suggests for Big History's Approach to Biological and Cultural Evolution* examines complexity dynamics through the lens of cultural evolution and punctuated equilibrium. Abel Alves' *The Animals of the Spanish Empire: Humans and Other Animals in Big History* is a historian's take on the similarities between animal and human behavior, contrasting the realms of biology and human history, that also tests the hypothesis that humans are 'chimpanzees who would be ants'. It is a remarkable take on conventionally human history and a fresh insight into our relationship with nature. Craig Benjamin's *Big History, Collective Learning and the Silk Roads* explores how in the era of agrarian civilizations human societies across Africa, Europe, and Asia into the largest of the 'world systems'. Barry Rodrigue's *Retrofitting the Future* takes an archaeological look at how technologies devised by humans in the earliest agrarian villages and states can inform our own technological development today. Joseph Voros' brilliant *Galactic-Scale Macro-engineering: Looking for Signs of Other Intelligent Species, as an Exercise in Hope for Our Own* deals with the most daunting of all Big History periods: the future. A respected physicist and futurist, the author looks at possible avenues for the further rise of culturally-generated complexity, how civilizations could harness the power of stars and even galaxies, and the telltale signs that such large scale complexity would exhibit in the night's sky.

### 3. Teaching Big History

Michael and D'Neil Duffy's *Big History and Elementary Education* discusses methods on how Big History can be extended from university and high school curricula to be taught to elementary students, particularly in a Montessori framework. They have devised a course progression through which young minds can travel through all the thresholds of the grand narrative. Tracy Sullivan's *Big History and the Secondary Classroom:*
*A Twenty-First Century Approach to Interdisciplinarity?* involves the experience the author has had teaching Big History to high school students and also developing the Big History Project. She uses her knowledge to explore pedagogical questions about how modern educators all over the world will define and improve their curricula in the rest of this century. Cynthia Stokes Brown's *Constructing a Survey Big History Course* takes her experiences in teaching Big History at a university level and gives some careful and direct advice to university lecturers who are considering setting up a course of their own. John Fowler's *Cosmology, Mythology, and the Timeline of Light* explores how to best capture the imaginations of 11 and 12 year old elementary students to impart on them an all-encompassing knowledge of the long story of existence from the Big Bang to modern day. Lastly, Jonathon Cleland Host's *Big History Beads: A Flexible Pedagogical Method* demonstrates a fun way that students of many ages can further reinforce their education of Big History by some simple but clever mnemonic devices.

## THE FUTURE OF THE IBHA

Big History has already come a long way, and these proceedings are but a small sliver of proof that this new field already has minds churning with a thousand different ideas about how we understand and interpret the Universe. It is our hope that further work will be done in the near future, on a mounting scale, with an ever-widening network of collaborators. As a young discipline, we have enjoyed advantages in our early years that other young disciplines do not. The historical study of the Universe has a highly interdisciplinary and mind-blowing quality to its founding principles that embraces and inspires scholars from every background. Scientists, historians, philosophers, and more, can find a place in our ranks. And many who have heard of Big History have eagerly jumped at the chance to do so. We also benefit from the whole-hearted support of prestigious and well-respected public figures like Bill Gates and Walter Alvarez.

We also enjoy the advantage of timing. At no point was a discipline that explored the connections between the natural and social sciences more relevant than now. At no time was a discipline that told the inhabitants of all nations across the globe their common story more important than in an age when travel is swift and communications are instantaneous. And never before in human history have we been so conscious of our potential in the cosmic story of rising complexity and so conscious of the perils threatening to reduce that potential to ruins and
ashes. The timing is not a coincidence. We are currently in the middle of a cultural revolution unprecedented in the times of all our forbearers. We should not be surprised that people from many different backgrounds and nations should at once have risen up and called for a grand historical epic that unites us all.

But each member of the IBHA needs to get the word out about Big History. Too few people have yet heard of this genre, much less the full story of humanity, life, and the Universe. We need to foster a network of researchers from the sciences and humanities. Physicists, geologists, biologists, historians, philosophers, and more, need to be encouraged to pursue interdisciplinary research projects in Big History. We need support, positions, and funding for graduate students who will be the researchers and educators of Big History in the future. We need to establish an academic journal to provide incentive for more scientists and scholars to spend their time doing Big History research. We need to create large, funded, research hubs in America, Europe, Australia, and anywhere else that a university will take us, to bring together people from various disciplines to work jointly on the questions of cosmic evolution. Much depends on the reader of this volume to do his or her part in these early days, so that the words 'Big History' will one day immediately leap to mind when people talk of the cultural legacy of the twenty-first century.

## Acknowledgements

The editors would like to express the deep gratitude to Elena Emanova and Kseniya Ukhova for their invaluable assistance in the process of preparation of this volume.

## References

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**Brown C. S. 2007. Big History: From the Big Bang to the Present.** New York and London: The New Press.

**Chaisson E. 2001. Cosmic Evolution: The Rise of Complexity in Nature.** Cambridge: Harvard University Press.

**Chaisson E. 2006. Epic of Evolution: Seven Ages of the Cosmos.** New York: Columbia University Press.

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# I. UNDERSTANDING AND EXPLAINING BIG HISTORY

## 1

### Swimming Upstream: Universal Darwinism and Human History*

David Christian

**Abstract**

This essay discusses Universal Darwinism: the idea that Darwinian mechanisms can explain interesting evolutionary change in many different domains, in both the Humanities and the Natural Sciences. The idea should appeal to big historians because it links research into evolutionary change at many different scales. But the detailed workings of Universal Darwinism vary as it drives different vehicles, just as internal combustion engines differ in chain-saws, motor cycles and airplane engines. To extend Darwin's ideas beyond the biological realm, we must disentangle the biological version of the Darwinian mechanism from several other forms. I will focus particularly on Universal Darwinism as a form of learning, a way of accumulating information. This will make it easier to make the adjustments needed to explore Darwinian mechanisms in human history.

**Keywords:** Universal Darwinism, collective learning, information, Big History.

Countlessness of livestories have netherfallen by this plage, flick as flowflakes, litters from aloft, like a waast wizard all of whirlworlds. Now are all tombed to the mound, isges to isges, erde from erde.

Finnegans Wake, Ch. 1

James Joyce's strange masterpiece, *Finnegans Wake*, is fractal. You can read it at many different scales, but you always have the eerie feeling that you are hearing a story you have already heard somewhere else. A mathematician might say the stories are 'self-similar'. You may think

<footnote>* My thanks to David Baker, Billy Grassie, Nick Doumanis, Ji-Hyung Cho, and Seohyung Kim for reading suggestions and comments on earlier versions of this paper.</footnote>
you are reading about the wake for a drunken bricklayer who fell to his
death from a ladder; but you are actually reading about the fall of hu-
manity and the expulsion from Paradise; and then again the story is real-
ly about Dublin and the many rises and falls of that city's history, people
and landscapes. Something similar happens in the emerging discipline
of Big History (see Christian 2004, 2010). Big History surveys the past at
the scales of cosmology, physics, geology, biology and human history.
Each discipline tells its own story, but as you get to know the stories,
they start to overlap, and we begin to see each discipline refracted in the
others. Like *Finnegans Wake*, Big History is 'self-similar'. And like *Finne-
gans Wake*, Big History derives much of its power from the synergies
that arise when you glimpse unexpected connections across different
scales and domains.

This paper explores one of these fractal phenomena: 'Universal Darwinism'. In biology, the Darwinian paradigm describes a distinctive form of evolutionary change that generates adaptive change through repeated copying of selected variants. Universal Darwinism is the idea that similar mechanisms may also work in many other domains. If so, do they always work as they do in biology? Or can we distinguish between a core machinery and the modifications needed to drive it in different environments?

Universal Darwinism

Richard Dawkins coined the phrase ‘Universal Darwinism’ in an essay
published in 1983. If we find life beyond this earth, he argued, it will
surely evolve by ‘the principles of Darwinism’ (Dawkins 1983: 403).
But there will also be differences. For example, the replicators may not
be genes. Dawkins suggested that human culture might offer an ex-
ample in the ‘meme’, an idea or cultural artifact such as a song or fash-
ion that varies, that replicates through imitation, that travels in sound
or images, and colonizes human minds when selected from a popula-
tion of rival artifacts (on meme theory see Blackmore 1999). More gen-
erally, he suggested that, ‘Whenever conditions arise in which a new
kind of replicator *can* make copies of itself, the new replicators *will*
tend to take over, and start a new kind of evolution of their own’
(Dawkins 2006: 193–194).

Universal Darwinism treats natural selection as one member of a
family of evolutionary machines that generate adaptive change through
repetitive, algorithmic processes. Always we see variation, selection and
replication. Some variations are selected, then copied and preserved
with slight modifications, after which the process repeats again and
again.
Here is a description of the basic machinery by a physicist, Lee Smolin,

To apply natural selection to a population, there must be:

* a space of parameters for each entity, such as the genes or the phenotypes;

* a mechanism of reproduction;

* a mechanism for those parameters to change, but slightly, from parent to child;

* differentiation, in that reproductive success strongly depends on the parameters (Smolin 2005: 34).

And here, to illustrate slight variations in our understanding of the basic machinery, is a description by a psychologist, Susan Blackmore:

Darwin's argument requires three main features: variation, selection and retention (or heredity). That is, first there must be variation so that not all creatures are identical. Second, there must be an environment in which not all the creatures can survive and some varieties do better than others. Third, there must be some process by which offspring inherit characteristics from their parents. If all these three are in place then any characteristics that are positively useful for survival in that environment must tend to increase (Blackmore 1999: 10–11).

Repeated many times, these simple rules yield interesting evolutionary change. Variation creates diversity, but by selecting some variations over others you steer diversification in a particular direction. You ensure that surviving variations will fit the environment that selected them, so they will be 'adapted'. In this way, the Darwinian machinery steers change away from the random mush ordained by entropy and the second law of thermodynamics. And if by chance some selected variants are slightly more complex than others, then we have, in Universal Darwinism, a way of increasing complexity. Indeed, Lee Smolin argues that natural selection provides the *only* scientific way to explain how complexity can increase against the tide of entropy (Smolin 2005: 34). (As I write this paper, I watch myself selecting some ideas, words and metaphors, and rejecting others; and I know that eventually the paper itself will have to take its chances in a competitive world populated by many other academic papers.)

So powerfully does the Darwinian machinery steer biological change that many find it hard to avoid imagining that there must be a designer. Surely, organs as beautifully designed as wings or brains must
have been, well, designed! Yet natural selection needs no cosmic project manager. This is what Daniel Dennett called 'Darwin's Dangerous Idea': operating without purpose, the Darwinian algorithm creates the appearance of purposefulness (Dennett 1995). From camels to chameleons, species fit their environments so precisely that they seem to transcend the laws of entropy. Yet they need no teleology and no driver. Darwin's ideas threatened theism because they explained the appearance of direction without needing a divine director (Ibid.).

Even in Darwin's time, some wondered if the same machinery could work outside the domain of biology. In a section on language in Chapter 3 of *The Descent of Man*, Darwin wondered if languages evolved like living organisms. After all, he noted, languages vary, they are reproduced, and their components – words, grammatical forms and even particular languages – are subject to selection for their 'inherent virtue'. Darwin concluded that, 'The survival or preservation of certain favoured words in the struggle for existence is natural selection' (Darwin 1989: 95). Darwin's friend, Thomas H. Huxley, suggested that there might be evolutionary competition between different bodily organs, while William James extended the idea of evolution to learning in general (Plotkin 1994: 61–64).

But it was in biology that Darwin's ideas really triumphed. In the 1930s and 1940s, several lines of research converged in the 'neo-Darwinian synthesis', which fixed several weaknesses in Darwin's original theory. For example, Darwin assumed that inheritance was blended, an idea that threatened to eliminate successful variations by driving all variation towards a mean; Darwin also feared that natural selection worked too slowly to generate today's biodiversity, particularly on a planet he believed to be less than 100 million years old. The neo-Darwinian synthesis used the work of Gregor Mendel to show that inheritance works not by blending but by copying discrete alleles. August Weismann showed the importance of distinguishing between phenotype and genotype, between characteristics acquired during an organism's lifetime, and those inherited through the germ line, which ruled out intentional or 'Lamarckian' forms of evolution; and this suggested that genetic mutations had to be random rather than purposeful. Finally, population geneticists such as Ronald A. Fisher and John B. S. Haldane proved mathematically that successful genes could spread fast enough to generate all the variety we see today, and geologists showed that the earth was almost 50 times older than Darwin had supposed (Mesoudi 2011: 40–51). Just as James Watt's modified steam engine made it industry's standard prime mover, so the neo-Darwinian synthesis turned Darwinism
into biology's standard explanation for biological change. The discovery of DNA and the evolution of genetic research consolidated Darwinism's paradigm role within biology.

Paradoxically, the success of the neo-Darwinian synthesis inhibited its use in other fields by creating the impression that all Darwinian machines had to be neo-Darwinian. Replicators had to be particulate; they had to be distinct from the entities in which they were tested (phenotypes or bodies); and variation had to arise randomly. Outside of biology, the neo-Darwinian model worked much less well than it did within biology. Historians and social scientists resisted Darwinian models for another reason: applied carelessly or too rigidly, they seemed to encourage Social Darwinism. The idea of Social Darwinism attracted scholarly attention after the publication of Richard Hofstadter's, *Social Darwinism in American Thought*, in 1944 (Hofstadter 1944). For Hofstadter, Social Darwinism's primary meaning was 'biologically derived social speculation'; but others associated it more closely with racist theories, though even Hofstadter had warned that '[Darwinism] was a neutral instrument, capable of supporting opposite ideologies' (Leonard 2009: 41–48). These fears helped preserve the gulf between the humanities and the natural sciences that Charles P. Snow bemoaned more than 50 years ago (Snow 1959).

In the late twentieth century, scholars in several fields returned to modified Darwinian models of change. They found them at work in immunology, in economics, in the history of science and technology, and even in cosmology, where Lee Smolin has proposed a theory of 'cosmological natural selection' (Smolin 1998; Nelson 2006; Campbell 2011). In Smolin's model, new universes are born in black holes. Information about how to construct universes resides in basic physical parameters, such as the power of gravity. Reproduction generates variation because daughter universes may inherit slightly different parameters. Variations are 'selected' and preserved because they will survive only if they generate universes complex enough to form black holes and reproduce. So cosmological natural selection does not generate a random mix of universes, but only those universes with just the parameters needed to create complexity. Our own existence proves that some universes will be complex enough to yield planetary systems, and life and creatures like us. Here we have a Darwinian explanation for the existence of a universe such as ours whose parameters seem exquisitely tuned for complexity.

Wojciech Zurek and his colleagues at the Los Alamos National Laboratory have even detected Darwinian mechanisms in quantum physics (Campbell 2011: 89ff.). When a quantum system interacts with an-
other system, perhaps by being measured in a lab, just one of its many
possible outcomes is selected and launched into the world, in the proc-
ess known as 'decoherence'. We have variability of the initial possibili-
ties, a selection from those possibilities, and a copying of the selected
possibilities from the quantum to the non-quantum domain. 'This Dar-
winian process allows a quantum system to probe its environment
searching for and selecting the optimal low entropy states from all those
available, thus allowing greater complexity to be discovered and sur-
vive' (Ibid.: 154). (The author of this paper makes no claim to under-
stand these processes except in the most superficial way. The point is
that Darwinian mechanisms may be at work even at the quantum level.)

Darwinian ideas have also returned to the humanities and social
sciences, attracting the attention of anthropologists, linguists, psycholo-
gists, game theorists and some economists, political scientists and histo-
rians of technology (see Mesoudi 2011 on cultural evolution; Fitch 2010
on language origins and Nelson 2007: 74 on Darwinian models in other
fields). Such explorations may get easier because the neo-Darwinian
synthesis is loosening its grip within the core territory of biology. When
the human genome was deciphered in 2003, it turned out that humans
have far fewer genes for the manufacture of proteins than had been ex-
pected, little more than 20,000, fewer than in the rice genome. This dis-
covery reminded biologists and geneticists that DNA is not a lone auto-
crat; it rules through a huge biochemical bureaucracy, whose agents
often manage their ruler, as civil servants manage politicians. Mecha-
nisms within cells control how and when the information in DNA is
expressed, and occasionally they even alter DNA itself, if only to repair
it. Even more striking, some of these changes seem to be heritable.
Through this modest backdoor, Lamarckian inheritance is creeping back
into biological thought. In a recent survey of these changes, Jablonka
and Lamb write that 'there is more to heredity than genes; some heredi-
tary variations are nonrandom in origin; some acquired information is
inherited; evolutionary change can result from instruction as well as
selection' (Jablonka and Lamb 2005: 1).

These debates within biology may help us stand back from the bio-
logical form of the Darwinian machinery and see how different variants
work in other realms, including human history.

Information and Universal Darwinism

Darwinian machines run on information: they replicate patterns, and
that means replicating *information* about those patterns. So to under-
stand their general properties, we need the idea of information. But in-
formation is a mysterious and ghostly substance that sometimes ap-
pears to float above reality, so we must define it carefully (accessible
surveys include Floridi 2010; Gleick 2011; Lloyd 2007; Seife 2007).

The idea of information presupposes the existence of differences that matter. To an antelope it matters if the animal behind the tree is a tiger or another antelope. Information reduces uncertainty by selecting one of several possible realities. This is why Donald MacKay described information as 'a distinction that makes a difference' (Floridi 2010: 23). A difference matters if other entities can detect and react to it. They may be able to detect it directly; but if not, they can often detect it indirectly, by secondary differences that correlate with the initial difference. This is where information steps in. When two differences are correlated, the second can carry a message from the first to any receiver able to interpret the message. In this way, causal chains carry potential information, whether or not there is a mind at the end of the chain. An antelope may detect a nearby lion by its shadow, and that should remove uncertainty about the danger. Run! But an electron can also be said to detect and react to a proton through its electric charge. Inserting a conscious entity into the chain simply adds one more link. It may add uncertainty, but all links do that. In this way information can travel along causal chains because we infer differences that are hard to detect from others that are easier to detect. Information is embedded in chains of cause and effect. '[It] is not a disembodied abstract entity; it is always tied to a physical representation. It is represented by an engraving on a stone tablet, a spin, a charge, a hole in a punched card, a mark on paper, or some other equivalent' (Rolf Landauer, cited in Seife 2007: 86).

When information travels through long causal chains, it can lose
precision. The second, and third and fourth differences are not, after all,
the same as the first. So we can judge a message by how well it represents
the original difference. Faulty genes trick cells into making cancer cells,
and an antelope can take a trick of the light for a tiger's shadow. But some
chains transmit information more efficiently than others. As a general
rule, digital or particulate information carriers detect differences better
than continuous or analogue carriers, because they *have* to discriminate.
That is why DNA employs genes, languages use words, and computers
prefer on/off switches. Effective transmission systems can partition the
smoothest of changes.

We can also judge a transmission system by the amount of informa-
tion it carries. Claude Shannon, the founder of 'Information theory',
showed that information increases precision by reducing uncertainty
(Floridi 2010: 37ff.). You can measure the amount of information in
a message by the number of alternative realities it excludes. 'There is a tiger behind the bush' is helpful advice; it reduces uncertainty. But if a friend adds that the tiger is hungry and in a bad mood, that should eliminate any doubts you had about running away. If, from all the possible things that might have happened, a message selects a tiny, not-easily-predicted sub-set, then it eliminates a vast number of other possibilities and a huge amount of uncertainty. Each rung on a molecule of DNA can exclude three out of four possible futures; so the entire molecule, with billions of rungs, can exclude a near infinity of possible creatures. It tells you how to build just one, say, an armadillo. Not an amoeba, or an archaeopteryx, but an armadillo. In information theory, 'the amount of information conveyed by [a] message increases as the amount of uncertainty as to what message actually will be produced becomes greater' (Pierce 1980, Kindle edition, location 461).

We have seen that information does not need minds. However, words like 'meaning' make sense only when the causal chain *does* include a mind. Only then can we describe information as *semantic*. And when the information is complex it makes sense to call it knowledge. Luciano Floridi writes,

> Knowledge and information are members of the same conceptual family. What the former enjoys and the latter lacks ... is the web of mutual relations that allow one part of it to account for another. Shatter that, and you are left with a pile of truths or a random list of bits of information that cannot help to make sense of the reality they seek to address. Build or reconstruct that network of relations, and information starts providing that overall view of the world which we associate with the best of our epistemic efforts (Floridi 2010: 51).

We needed this digression on information because Universal Darwinism builds complexity by accumulating, storing and disseminating information about how to make things that work. Darwinian machines generate unexpected outcomes, like armadillos or human brains, because they accumulate information that is *not* entropic mush. So wherever they are at work, unexpected things happen – whether in the immune system or in DNA, or in human history or entire universes (Blackmore 1999: 15). Darwinian machines learn (a classic summary is Campbell 1960: 380). This is why Karl Popper described the growth of knowledge as: 'the result of a process closely resembling what Darwin called "natural selection", that is, *the natural selection of hypotheses*: our knowledge consists, at every moment, of those hypotheses which have shown their (comparative) fitness by surviving so far in their struggle for existence' (Plotkin 1994: 69).
## Three Darwinian Learning Machines

Seeing Darwinian machines as learning machines will help us understand how they may shape human history. On this planet, living organisms learn in three distinct ways. All are Darwinian, but they use different variants of the same basic engine.

**Genetic Learning and Natural Selection.** The first variant is natural selection. Biologists have studied this engine for a long time and they understand it well. It explains how molecules of DNA accumulate adaptively significant information. DNA codes information about how to manufacture proteins using four nitrogenous 'bases': Adenine, Thymine, Guanine and Cytosine. Differences in the order of the letters really matter. Exchange one A for a T in the code for a protein with 146 different amino acids and you get sickle cell anemia. DNA stores information that is rich because it is specific, impossible to generate randomly, and therefore it is unexpected. Over time, billions of new genetic recipes for building proteins and whole organisms accumulated in the world's stock of DNA to generate the species we see today.

Generation by generation, packets of DNA are sieved as their products enter the world. Mutations, copying errors and recombination during reproduction create random variations in genes and in the organisms they give rise to, so that slight modifications on the original instructions are continually being tested. Only those packages that produce viable organisms will survive and reproduce. Much of the information they contain tells cells how to choose the tiny number of biochemical pathways that resist entropy. For example, it may include recipes for enzymes that steer biochemical reactions along rare but efficient pathways, or that help export entropy outside the organism (Campbell 2011: 102). In each generation, that information can be updated. This explains why living organisms have an uncanny ability to track changing environments.

DNA preserves information because it acts like a ratchet (on the 'ratchet effect' in human history, see Tomasello 1999). Mechanical ratchets allow a gear-wheel to turn in only one direction because the 'pawl' catches on the cogs and prevents the wheel from turning backwards. By only copying information that works, DNA ensures that the gear wheel of evolution normally turns in the direction that accumulates viable variations. Without an information ratchet, the wheel of evolution could turn in either direction, viable variations would survive no better than any others, and biological change would drift with the flow of entropy. That is why it makes sense to suppose that life itself began with DNA or
its predecessor, RNA. Before the evolution of DNA or RNA, parts of the Darwinian machine already existed: there was plenty of variation within pre-biotic chemistry, and variations could be selected for their greater stability. But only after DNA evolved (possibly preceded by RNA) could successful variations be locked in place so that genetic information could accumulate. With DNA preventing any backsliding, life was off and running.

To summarize key features of genetic learning: information accumulates as it is locked into the biochemical structures of DNA molecules. Most variations arise randomly during reproduction. Variations survive only if the DNA molecules they inhabit are copied. Genes are particulate, but when working together, they can create the impression of a 'blending' of characteristics. Because most variation arises during reproduction, genetic learning is non-Lamarckian; it does not preserve 'acquired variations', variations generated during an individual's lifetime. Random variations are tested, one by one, surviving only if they create organisms that fit their environment. These are the rules of the neo-Darwinian synthesis.

**Individual Learning.** The other two forms of learning have been studied less closely than the genetic machine, and we do not understand them as well.

I will call the second machine ‘individual learning’. It works not across species or organisms but within the neurological system of a single individual. It is at work in species as varied as cephalopods, crows and chimpanzees. It works even in simple organisms, which can learn to detect and react to gradients of light or warmth or acidity. But individual learning is most impressive in animals with brains. Imagine our antelope glimpsing a lion near a waterhole. Was that really a lion? Should it make for another waterhole? With no guidance, it might have to choose randomly, as young animals often do. It will soon find out if its gamble succeeded. But intelligent animals also have better ways of choosing. They accumulate memories of past experiences associated with pain, fear, anxiety or with a sense of pleasure and ease. If any of those memories are similar to what is happening right now, they may provide guidance. Trying out possibilities in memory is less dangerous than trying them out in the real world, and the accompanying sensations, installed over time by genetic learning, will provide better than random criteria for repeating or avoiding particular experiences. Alasdair MacIntyre reports that if a young cat catches a shrew, it will eat it as if it were a mouse. It will then become violently ill, which is an unpleasant experience. But it has learnt a difference that matters and from
now on it will avoid shrews (MacIntyre 2001: 37). A memory that
should help the cat survive has outcompeted a memory that once
caused it misery.

Put more generally, an intelligent organism undergoes experiences
that carry information about the outside world, if they can be stored and
interpreted. Memory provides an information ratchet as it encodes ex-
periences in neurological networks. It accumulates useful information
within an individual's lifetime. Faced with an important choice, the or-
ganism can refer to its memory bank and look for experiences that had
happy or unhappy outcomes. As it replays memories with their associ-
ated experiences of pleasure or pain or fear or comfort, it learns to make
better choices. Significant memories are selected by being reinforced
(through repetition or association with other strong experiences), while
memories that are not reinforced will fade away (Campbell 2011: 119-
120). The criteria for selection – repeated reinforcement or strong asso-
ciation with experiences of pain or pleasure – will have been built into
the organism by genetic learning, which teaches you to cherish parents
and shun predators. Here we have the complete Darwinian cast: varied
experiences that are encoded in memories, only some of which are se-
lected for preservation.

So individual learning is a Darwinian machine. But it does not work
quite like the machinery of the neo-Darwinian synthesis. Its arena is the
individual brain, rather than the outer world. Individual learning pre-
serves useful memories acquired during an individual's lifetime, but
those memories can also change; unlike genes, memories are not fixed
from the moment of their birth. So individual learning can be Lamarck-
ian. It contains no simple analogue to the neo-Darwinian separation of
genotype (which does not change during an individual's lifetime) and
phenotype (which can change within a lifetime). Variation arises mainly
from the diversity of individual life experiences, though some may arise
from mistakes in coding or assessing those experiences. In individual
learning, the primary information carriers are neurological networks, and
memories, their psychological correlate. Both are more diffuse and vari-
able than genes and subject to constant minor changes as they join or
separate from other networks and memories. Selection occurs through
reinforcement rather than reproduction, as networks are selected for their
strength and connectedness, which depend on the number and strength
of the synapses from which they are constructed. Networks that are re-
inforced strongly because they are repeated often ('*that* waterhole is
safe') or are particularly shocking ('nearly got caught that time!'), will
survive, while the rest will dwindle and fade. The criteria for selection
do not reside in the outer environment, but are built into the organism
by genetic learning. But selection is not purely mechanical. Sometimes it
demands a judgment call ‘*that* waterhole is safe but the water does not
taste as good, Hmmm’. At this point we may conclude that animals
ponder alternatives before selecting consciously and with intent. Selec-
tion is beginning to look purposeful.

So here we have a Darwinian machine that lacks the bells and whistles of the neo-Darwinian synthesis but can still generate new, non-random and significant information. It also sports some glossy new features. It is very fast; it can accumulate new information in seconds, while genetic learning gets to test new variations just once in a lifetime. Individual learning is also specific; instead of producing generic adaptive rules for millions of individuals, it tells a particular individual how to live in a particular time and niche. But individual learning is also ephemeral; it cannot survive outside the arena of the individual brain. A lifetime of learning evaporates on the death of each individual, so every generation starts from scratch. Individual learning is Sisyphean; it cannot accumulate information at time scales larger than a lifetime, so it does not lead to a long-term change. That is why it cannot generate what we humans call ‘history’; change at scales larger than a single lifetime.

Darwinian Machines in Human History: Collective Learning

Our third Darwinian machine *does* generate long-term change. I call it
‘collective learning’, and it seems to be unique to our species, *Homo sapi-
ens* (for brief discussions see Christian 2004, 2012).

Collective learning happens when you join individual learning to a suf-
ficiently powerful system of communication. It depends on the ability of
individual learners to share what they have learned with others, and to
do so in such volume and with such precision that new information ac-
cumulates at the level of the community and even the species. As Merlin
Donald writes, ‘The key to understanding the human intellect is not so
much the design of the individual brain as the synergy of many brains’
(Donald 2001: xiii).

Collective learning uses a new and more powerful information
ratchet. Unlike individual learning, it stores information in many minds
over many generations, so that information can outlive the individuals
who created it. If a fraction of that information improves how individu-
als exploit their environments, collective learning will tend to increase
the ecological power of whole communities. Like all animals, humans
exploit their environments to extract the energy and resources they need
to survive; but only humans keep discovering and sharing new ways of
exploiting their environment, so that over time they can extract more
and more energy and resources. Our ecological creativity explains why
humans are the only species that has a history of long-term changes in
behaviours, social structures and ecological adaptations. Like individual
learning, collective learning also works much faster than genetic learn-
ing. That is why, within just a few hundred thousand years we have
become more powerful than any single species in the 3.8 billion year
history of life on earth, so powerful that some geologists argue we have
entered a new geological epoch, the 'Anthropocene' (see Steffen et al.
2007).

By sharing ideas, information, gossip and beliefs, collective learning
creates human 'culture', which Mesoudi defines broadly as 'information
that is acquired from other individuals via social transmission mecha-
nisms such as imitation, teaching, or language' (Mesoudi 2011: 2-3; for
a similar definition see Distin 2011: 11). Of course, humans are not alone
in having 'culture' in this sense. Songbirds, chimps and whales all share
information. The difference is in the degree of sharing, but that small
difference really matters. Animal languages lack an efficient informa-
tion ratchet, so in the animal versions of 'telephone', information leaks
away within a few exchanges and has to be constantly relearned. This is
why knowledge accumulation has little impact on any species except
ours, and that is why no other species has a history of long-term change
over many generations. Alex Mesoudi sums up a broad consensus
among those who study animal culture:

Although numerous species exhibit one-to-one social learning
and regional cultural traditions, no species other than humans
appears to exhibit cumulative culture, where increasingly ef-
fective modifications are gradually accumulated over succes-
sive generations. This might therefore be described as the de-
fining characteristic of human culture (Mesoudi 2011: 203).

There is a narrow but critical threshold between individual and col-
lective learning. To appreciate its significance, imagine pouring water
into a bathtub with no plug. A trickle of water will deposit a thin film at
the bottom of the bathtub. But the level will not rise because water leaks
away as fast as it pours in. Increase the flow and the water level will rise
and settle at a new level. (We see something like this in species such as
*Homo erectus*, or in some species of primates.) Increase the flow just a bit
more and suddenly the level starts rising and keeps rising as water en-
ters faster than it leaves. You have crossed a critical threshold beyond
which there appears a new type of change because now the water level
will keep rising without limit (until it overflows the bathtub).
How did our ancestors cross the threshold to collective learning? We do not really know, though we have plenty of suggestions. Many changes led our ancestors towards the threshold of collective learning (for recent discussions, see Tattersall 2012; and Fitch 2010). They included larger brains; insight into the thinking of others (a 'theory of mind'); some ability to cooperate; the ability to control vocalizations and interpret the vocalizations of others; the use of fire to cook and pre-digest food, which, as Richard Wrangham points out, gave access to the high quality foodstuffs needed to grow brains. Many other species share some of these qualities and abilities (Tomasello 1999, 2009; Wrangham 2009; MacIntyre 2001: chs 3, 4). So, as Richerson and Boyd put it, we can imagine several species gathering at the barrier before collective learning, until eventually one broke through (Richerson and Boyd 2005: 139). Our own history suggests that the lucky species would then deny passage to its rivals: 'humans were the first species to chance on some deviant path around this constraint [the difficulty that culture works only within a community of skilled social learners], and then we have pre-empted most of the niches requiring culture, inhibiting the evolution of any competitors' (Boyd and Richerson 2005: 16). Since humans broke through, our closest hominine relatives, from Neanderthals to Denisovans, have perished and our closest surviving relatives, the chimps and gorillas are approaching extinction. Even if several related species arrived almost simultaneously at the barrier to collective learning, there was apparently room for only one species to sneak past it.

But the speed of the change – we, humans, began our climb to world domination less than 500,000 years ago, a mere second in paleontological time – suggests that a single push shoved us through. Perhaps, it was a glitzy new neurological gadget, some form of Chomsky's 'grammar' module, or a new form of the FOXP2 gene that pushed us through. Or perhaps, as Terrence Deacon has argued, it was symbolic language (Deacon 1998). Some have argued for a slower transition. But, as a recent article argues, even if human language evolved 500,000 years ago, in evolutionary terms, that is a 'flash in the pan', implying that 'language abilities were relatively rapidly cobbled together from pre-adapted cognitive and neurophysiological structures' (Dediu and Levinson 2013: 10). Whatever the explanation, we should expect to find a single, critical change, because it defies reason to suppose that all the necessary pre-adaptations could have converged simultaneously on a single point in paleontological time. As Michael Tomasello writes: 'This scenario [of a single switch] solves our time problem because it posits one and only one biological adaptation – which could have happened at any time in human evolution, including quite recently' (Tomasello 1999: 7).
Suddenly, humans began to communicate not just in semantic fragments ('Tiger!'), but in organized and contextualized strings of information ('Yup, it's got the same markings as the one that got Fred, and it's behind the same bush!'). They began to use large, coherent packets of symbolic information, words like 'family' or 'gods' that compressed a world of experience into a few sounds, and linked those sounds into precise relationships using grammar (Deacon 1998). Human language locked up cultural information as tightly as DNA molecules locked up genetic information. As Tomasello puts it, 'The process of cumulative cultural evolution requires ... faithful social transmission that can work as a ratchet to prevent slippage backward – so that the newly invented artefact or practice preserves its new and improved form at least somewhat faithfully until a further modification or improvement comes along' (Tomasello 1999: 5). That is why some anthropologists describe cultural accumulation as 'cultural ratcheting' (Pringle 2013).

Once the switch for collective learning was thrown, our ancestors could start building new knowledge, community by community, accumulating local knowledge stores that steered each group in different directions to generate the astonishing cultural variety unique to humans. At the same time, our inner world was transformed as ideas washed from mind to mind. We do not just learn collectively; we experience collectively. The anthropologist, Clifford Geertz, described this realm as, 'that intersubjective world of common understandings into which all human individuals are born, in which they pursue their separate careers, and which they leave persisting behind them after they die' (Geertz 2000: 92). A simple thought experiment illustrates the power of this mental sharing. Look inside your head and do a quick census of everything that is there. (It takes just a few seconds.) Then ask the question: how much of that stuff would be there if you had never had a conversation with another human? Most will agree that the correct answer is: 'Very little'. And that 'very little', mostly produced by individual learning, hints at the inner world of chimps. While chimps learn alone or in ones and twos, humans learn within teams of millions that include the living and the dead.

When did our ancestors cross the threshold to collective learning? In paleontological time, the crossing took an instant, but in human time it was probably smeared out over tens of thousands of years (a paradox captured in the title of McBrearty and Brooks 2000, 'The Revolution that Wasn't'). And even when the engine of collective learning spluttered into action, it took time to pick up speed. So we cannot easily judge when human history began. But we do know what to look for. We should look for sustained evidence of humans adding ideas to ideas to form
new ideas. We should look for sustained innovation and ever-increasing
cultural diversity. We should look for new and more diverse tools, and
signs that humans were exploiting many new niches. And if, as Ter-
rence Deacon and others have suggested, the breakthrough was the ac-
quisition of symbolic language, then we should also look for evidence of
symbolic thinking in art, body painting or signing (Deacon 1998).

The first speakers of a fully human language may not have belonged
to groups normally classified within our own species, though they were
surely very similar to us (Dediu and Levinson 2013). If they did belong
to our species, we can date human history to at least 200,000 years ago,
because that is the date of the oldest skull generally assigned to *Homo
sapiens*. It was found in Omo, in Ethiopia in the 1960s (Tattersall 2012: 186).

But what we really need is evidence of new behaviours. In a com-
prehensive survey of African evidence from the Middle Stone Age, pub-
lished in 2000, Sally McBrearty and Alison Brooks found hints of collec-
tive learning from as early as 250,000 years ago (McBrearty and Brooks
2000; and for a brief update see Pringle 2013). The Acheulian stone
technologies associated with *Homo ergaster* were replaced by new, more
delicate and more varied stone tools, some of which may have been
hafted. The new tools are associated with species that few anthropolo-
gists would classify as *Homo sapiens*, so the technological speed up may
have preceded our own species. By 150,000 years ago, when members of
our species were surely around, McBrearty and Brooks find hints that
some groups were using shellfish and exchanging resources over long
distances. We also see evidence of regional cultural variations. Ecologi-
cal migrations are important because they show a species with enough
technological creativity to move further and further from its evolution-
ary niche. Early in our history, new knowledge counted most at the
edge of a population's range, where people faced the dangers and op-
portunities of testing new plants or animals. Before 100,000 BCE, we
have tantalizing hints that some humans had entered deserts and forests
(McBrearty and Brooks 2000: 493-494). After 60,000 such evidence mul-
tiplies; humans appear in Europe, in Australia and then in Ice-Age Sibe-
ria and, by at least 15,000 years ago, in the Americas.

Language leaves no direct traces, but archaeologists have found
many hints of symbolic thinking. More than 260,000 years ago, early
humans near Twin Rivers in modern Zambia used hematite (red iron
oxide), possibly to paint their bodies (Stringer 2012: 129). Later evidence
is less equivocal (for a good survey see Pettit 2005; on Blombos cave see
Henshilwood et al. 2011). At Pinnacle Point in South Africa, in sites dat-
ed to about 160,000 years ago, we find the earliest evidence for the use
of shellfish, along with signs of composite tools and lots of hematite, of
a particularly brilliant red, which points to symbolic uses (Stringer 2012: 129). By 115,000 years ago, similar evidence turns up in modern Israel, where, in Skhul cave, archaeologists have found evidence of symbolic burials. But the best evidence of all for rich symbolic activity comes from the marvellous South African site of Blombos cave, whose remains date from almost 100,000 years ago. Here, Chris Henshilwood and his team have found delicate stone tools, seashell beads, and lumps of ochre carved with wavy lines that could almost be an early form of writing (Ibid.: 129–130).

Evidence for early signs of collective learning will surely come into
sharper focus, but in the meantime, these hints suggest that if human his-
tory began with collective learning then something had cranked up the mo-
tor certainly by 100,000 years ago, perhaps, as early as 250,000 years
ago and possibly 500,000 years ago (Dediu and Levinson 2013).

Collective Learning as a form of Universal Darwinism

Collective learning launched and sustained our species on its astonish-
ing journey towards planetary domination. If this argument is right, it
seems that some form of Universal Darwinism has driven human his-
tory. We see *variation* in the ideas and information of different human
societies, from their technologies to their religious rituals, from their art
and clothing to their cuisine and entertainment. Individuals and whole
societies *select* some variants and reject others. And selected variations
are *preserved* as they flow between minds.

But in detail, collective learning works differently from genetic
learning and individual learning, and any Darwinian accounts of hu-
man history must take these differences into account. As Alex Mesoudi
writes,

...many of the details of biological evolution that have been
worked out by biologists since [*The Origin of the Species*], such as
particulate inheritance (the existence of discrete particles of in-
heritance, genes), blind variation (new genetic variation is not
generated to solve a specific adaptive problem), or Weismann's
barrier (the separation of genotypes and phenotypes such that
changes acquired in an organism's lifetime are not directly
transmitted to offspring), may not apply to cultural evolution
(Mesoudi 2011: x).

Why does collective learning work so much faster than genetic
learning? In part because it builds on the machinery of individual learn-
ing, which works with neurological impulses rather than entire organ-
isms. A genetic mutation must wait a generation before it effects
change; a suddenly triggered memory can have you swerving in a sec-
ond. Collective learning also copies fast. It can transmit new ideas on the fly, as they evolve, and can broadcast them to many brains at once because it works with sound waves (in speech) or light waves (in signalling and imitation). Like genetic learning, collective learning is auto-catalytic, so it has generated better ways of storing and transmitting information, from writing to printing to the telegraph and internet. Auto-catalysis explains why collective learning generates not just change, but *accelerating* change. Finally, collective learning, like individual learning, builds on acquired as well as inherited variations. While genetic learning gropes randomly in the dark, collective learning can probe more purposefully.

How do variation, selection and reproduction work in collective learning?

In collective learning, as in genetic learning, some variation is blind, arising from mutation and drift; but these variations arise from misunderstandings or simple blurring of meaning rather than from biochemical glitches. Much more important is another source of variation: deliberate innovation. Richerson and Boyd call this 'guided variation' (see the taxonomy of cultural evolutionary forces in Richerson and Boyd 2005: 69). Individuals deliberately add what they have learnt to the common pool of knowledge, or tweak and modify existing ideas. A little more salt in the soup, or tautness in the bowstring, or even a separate boiler for the steam engine. Moment by moment, and often with a sense of purpose, individual learning adds new information to a shared pool of knowledge, whereas genetic learning receives its variations at random.

Selection, too, can be conscious and purposeful in collective learning. Richerson and Boyd describe purposeful selection as 'biased transmission'. We select using 'content-based' biases when we choose an idea or cultural variant on its merits, for its beauty or precision, perhaps. Other forms of selection are deliberate but less thoughtful. In a conformist or lazy mood, we often choose the most accessible idea or behaviour, or we choose ideas or behaviours associated with admired role-models. In the taxonomy of Richerson and Boyd these are called 'frequency-based biases' or 'model-based biases'. Either way, selection is trickier in collective learning because cultural variations are fuzzier than genes, though often, when we choose one word or another or vote for one political party rather than another, we chop up the cultural flow.

Reproduction is fuzzier and more complex than in genetic learning. Ideas have many parents. They can also replicate in their thousands at religious festivals or political rallies or through mass media. Most important of all, in collective learning reproduction is less tightly bound to the reproductive success of particular individuals than in genetic learning. This is why humans often select variations that are *not* adaptive
under the rules of genetic learning. For example, they may choose to
have fewer children than possible, thereby reducing their reproductive
success (Richerson and Boyd 2005: ch. 5). This makes no sense under the
rules of genetic evolution, which measure success by the number of genes
passed on to the next generation. Even worse, humans sometimes risk
their lives for others who are not even close kin. Genetic reproduction can
just make sense of sacrifices on behalf of close kin (who do, after all, share
genes with you). But it cannot explain sacrifices on behalf of strangers or
people you may never have met. Collective learning can explain such be-
haviour, because collective learners live within shared flows of ideas, in-
formation and motivation that create a sense of shared meaning and pur-
pose, and magnify the importance of reciprocity. We inherit ideas and
values from dead strangers and living teachers as well as from parents
and grandparents, and we cannot always distinguish clearly between
the two types of inheritance. So collective learning allows behaviours
that, from the perspective of genetic learning, seem like errors, such as
the choice of a group of ducklings to treat Konrad Lorenz as their
mother. Symbolic thinking blurs the line between genetic and imagined
kinship. And where meanings are shared so, too, are their emotional
charges. Flags and national anthems can motivate us as powerfully as
family, particularly if cultural differences sharpen our sense of shared
community. Richerson and Boyd have shown that in such environments
models predict the rapid spread of altruistic behaviours. This is particu-
larly true where cultural selection is 'conformist', where people choose
values because they are normal within their community (Ibid.: ch. 6).

In short, a sense of shared meaning blurs the distinction between
individual and group success. In collective learning, the viability of ide-
as (and sometimes of the humans who carry them) depends as much on
the reproductive success of entire groups as on that of individuals. So
where collective learning is at work, group selection may be as impor-
tant as individual selection, because with the flourishing of human cul-
ture, genes are no longer the primary shapers of behavioural change.
Group mechanisms including shared cultural norms and social struc-
tures clearly play a profound role in explaining human behaviour. So
we should not be surprised to find that humans collaborate so effec-
tively in bands, tribes and nations as well as in families. Though the
idea of group selection is fiercely contested at present (for two different
positions see Pinker 2013 and Wilson 2007), something like group selec-
tion is surely at work in the evolution of human culture.

Finally, and most mysteriously, collective learning generates an en-
tirely new form of change, cultural change. Like information, cultural
change often seems to inhabit a limbo between the physical and mental
worlds. John Searle, who has spent much of his career trying to explain cultural phenomena, argues that the cultural realm arises from 'shared intentionality', or the shared sense of meaning created by collective learning (not his term) (Searle 2010: 3–8 and passim). 'Shared intentionality' explains why only humans can assign conventional meanings or functions to people and objects. It matters if they agree to call a piece of paper a twenty-dollar bill. The agreement creates rights, obligations and possibilities; it motivates behaviours that go well beyond our sense of individual wants or needs. Searle argues that such agreements are the foundation of all social relations and institutions. They are what make human societies different.

**Conclusion: Different Versions of the Darwinian Machine**

Wherever we see change swimming against the flow of entropy, we should suspect that a Darwinian machine is at work. Human history represents a spectacular example of this kind of change, so we should expect to find a Darwinian machine lurking somewhere within the discipline. Most historians have rejected this possibility, partly from fear of Social Darwinism, partly because the neo-Darwinian synthesis fit human history so poorly. But as we have seen, Darwinian machines come in different versions. A clearer appreciation of these differences may encourage historians, too, to explore the possibility that Darwinian mechanisms of some kind can help us explain the remarkable trajectory of human history. But they may also help us see human history itself as part of a much larger story of increasing complexity, most of which (perhaps all of which) was driven by Darwinian mechanisms of some kind.

*'Mutt. – Ore you astoneaged, jute you?*

*Jute. – Oye am thonthorstrok, thing mud'* (Finnegans Wake, Ch. 1).

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# Standing on the Shoulders of Giants: Collective Learning as a Key Concept in Big History

David Baker

## Abstract

One of the key concepts for the human part of the grand narrative is known as 'collective learning'. It is a very prominent broad trend that sweeps across all human history. Collective learning to a certain degree distinguishes us as a species; it got us out of Africa and the foraging lifestyle of the Palaeolithic, and underpinned demographic cycles and human progress for over 250,000 years. The present article looks at collective learning as a concept, its evolution within hominine species, as well as its role in human demography and the two great revolutions in human history: agriculture and industry. The paper then goes on to explain the connection of collective learning to Jared Diamond's 'Tasmanian Effect'. Collective learning also played a key role in the two 'Great Divergences' of the past two thousand years. One is industry and the rise of the West, described to great effect by Kenneth Pommeranz, the other is the less well known: the burst of demography and innovation in Song China at the turn of the second millennium AD. Finally, the paper concludes with insights into how collective learning forges a strong connection between human history and cosmology, geology, and biology, through what is widely recognized as one of the 'unifying themes' of Big History – the rise of complexity in the Universe.

**Keywords:** complexity, collective learning, demographic cycles, evolution, accumulation.

When I arrived in Sydney in 2010 to start my PhD in Big History, my original topic was long-term patterns in Malthusian cycles. However, it was only a few weeks before I noticed the strong connection between population dynamics, the rise of complexity that is central to Big History's grand narrative, and a concept known as cultural evolution, which is the transmission of cultural ideas, beliefs, and attitudes through an algorithm of variation and selection very similar to the evolution of genes in biology. Cultural ideas evolve and adapt far faster than genetics and this permits a much more rapid increase in complexity. Cultural evolution is, of course, one of many manifestations of the
‘Darwinian algorithm’ that is observed in cosmology, geology, biology, and even quantum physics, that seems to play a role in rising complexity (Baker 2011a, 2013, 2014; Christian in this volume). My dissertation has explored the Darwinian connection among these differing physical processes and I have explored them in a few other articles, but in this article I would like to focus on an aspect of cultural evolution that is crucial to human progress and the upper end of the immense complexity the Universe has generated so far.

Collective learning is an ability to accumulate more innovation with each passing generation than is lost by the next. It has allowed humans to exploit our ecological niches with increasing efficiency and allowed us to largely harness the energy flows of the planet and the Sun. Through foraging, agriculture, and heavy industry collective learning has raised the carrying capacity of the population, allowing for more potential innovators, who in turn raise the carrying capacity, thus creating even more innovation. Gradually, over 250,000 years of humanity, the population has risen and we have generated increasingly complex societies and have developed the capacity to harness an enormous amount of energy. In terms of the wider rise of complexity and in processes of Universal Darwinism, collective learning is the summit of the process, and I say the next two words with emphasis, *thus far*.

The historian's view of all human history is no longer vague or boundless with a chaotic tangle of periods and research areas. Collective learning gives a clear and definite shape to the whole picture as well as an underlying theme. This is revolutionary not only for Big History, but for areas of conventional human history as well. The idea has its uses within archaeology, agrarian history, and within the study of the industrial era – not to mention our anxiety-fraught examination of the loom-ing trials of the twenty-first century. For the concept of collective learn-ing we are deeply indebted to David Christian for expounding it in his own works, and also anthropologists like Peter Richerson, Robert Bet-tinger, Michelle Kline, and Robert Boyd, for developing it mathematically and, in one case of a recent paper to the Royal Society, with a strong de-gree of empiricism (Christian 2005: 146–148; Richerson, Boyd, and Bet-tinger 2009: 211–235; Kline and Boyd 2010: 2559–2564).

In natural ecology, all organisms are slaves to some form of S-curve that restricts the amount of resources available to an individual and a species, enabling them to survive and reproduce. When the carrying capacity of a biological population is reached, the population undergoes
strain, decline, and recovery. While potentially destructive to life-forms, it does have the merit of spurring along evolution by natural selection. Thomas Malthus' *Essay on the Principle of Population* (1798) illustrated how the human population growth always tended to exceed the resources capable of supporting its burgeoning numbers. Darwin read it in 1838 and extrapolated it to other organisms whereby species overbreed, compete, and change over time to possess the traits that are best able to extract resources from their environment and perpetuate their survival. It was an epiphany for him. At last, he said, 'I have finally got a theory with which to work' (Darwin 1887: 82). It also applies to human history. In his recent book, big historian Fred Spier identifies the unifying theme of our long story:

> If we want to prevent our bodily complexity as well as all the complexity that we have created from descending into chaos, we must keep harvesting matter and energy flows on a regular basis. *This is the bottom line of human history.* I will therefore argue that during most, if not all, of human history, the quest for sufficient matter and energy to survive and reproduce... has been the overriding theme (Spier 2010: 116; emphasis added).

Until a few million years ago there was nothing on Earth to indicate that anything else besides the *mêlée* of genetic evolution, with its constant generation and annihilation of diversity, would arise. It appeared the short, ignorant, and terrifying existence of beasts of the field was the highest level of complexity of which the planet was capable. Biology seemed like the finest manifestation of the Darwinian algorithm that gradually produced more and more complexity, with the annihilation of useful DNA mutations and the selection of useful ones. However, like stellar evolution builds on quantum Darwinism, like mineral evolution is an extension of stellar evolution, biological evolution soon spawned another Darwinian process. There emerged the groundswell of collective learning, the concept that a species' learning accumulates in ways over several generations that enhances their ability for survival. If harvesting energy to maintain our complexity is the bottom line of human history, then collective learning and its ability to raise the carrying capacity is without question the shape. That shape looks something like this.
<figure><img src="image_3.png" /><figcaption>Fig. 1.</figcaption></figure>

Source: Richerson *et al.* 2009: 219.

**I. Collective Learning in the Palaeolithic**

What precise ability enables collective learning? How did it evolve? What selection pressures made it spring into being? This engages with a much larger and much older debate over the nature of human uniqueness – something to which a refined version of collective learning can contribute. These ideas are universal grammar *à la* Noam Chomsky vs. symbolic reference *à la* Terrence Deacon, the emergent thought vs. the computational model of the mind, the role of imitation and mimicry in the evolution of language, and the debate over group selection in humans that raged over a recent book by Edward O. Wilson and the counterblast of Steven Pinker (Wilson 2012; Pinker 2012). While the importance of collective learning and technological accumulation to human history has been clearly identified, it is much less clear what trait or a set of traits enabled it in the first place. A number of theories exist and they all seem to revolve around the gradual and the sudden. Chomsky argues against gradualism and considers universal grammar an all or nothing proposition that somehow flickered into being (Chomsky 2002: 80). Pinker argues for a more gradual evolution of a computational model of the mind similar to the evolution of the eyes (Pinker 1997: 21). Deacon argues for the appearance of symbolic reference as a sudden occurrence (Deacon 1997: 328–355). Dunbar claims that enhanced communication abilities and technological accumulation were the gradual result of selection pressures on complex interaction and coordination due to increasing group size and inter-group connectivity (Dunbar 1996: 3–17, 56–58, 62–64, 77; 2004: 28–29, 71–72, 125–126; 2010: 22–33). Finally, Corballis places ges ticulation as the fundamental form of social learning with speech being the ultimate form – thus being a change of degree and not of kind (Cor
ballis 2002: 41–65). Whatever the skill that allowed humans to accumu-
late more innovation with one generation than was lost by the next, it
needs to have a clear explanation about how it evolved in real terms
without recourse to metaphor and with identifiable selection pres-
sures – whether sudden or gradual.

These questions tie into the next issue: the threshold after which collec-
tive learning became possible. Where is it drawn? Is it the result of a grad-
ual evolution over several species or a sudden jump? If we knew what
ability, origin, and selection pressures caused collective learning, we
might be able to better answer that question. For now it is a big blank
spot on the map. Do we draw the line at humans? And if so, how do we
treat the nascent elements of collective learning in our evolutionary
family? David Christian often gives the example of the Pumphouse
Gang baboons, where a skilled hunter dies and information eventually
degrades, vanishes, and the range of the species does not expand. He
also gives a nod to what he calls the ‘sporadic learning’ in apes and in
*Homo habilis* and *Homo ergaster/erectus* (Christian 2005: 146). But if we
place the threshold where more knowledge is accumulated with each
generation than is lost by the next, we are confronted with questions
about the significance of situations where knowledge neither degrades
nor accumulates – it is simply preserved. For example, termite fishing,
rock hammers, leaf sponges, branch levers, and banana leaf umbrellas
are passed on by social learning, not instinct, and not sporadically, in
certain populations of chimpanzees, and are withheld from others out-
side that cultural network (Pinker 1997: 198–199). They are sustained
and passed on, usually from mother to offspring, and are not reinvented
every generation. Here is a tremendous ability, however weak, probably
possessed by our last common ancestor. This ought to tell us something
about the nascent elements of collective learning. But, on the other
hand, if this learning does not accumulate, but is only preserved, per-
haps, it can conceivably be dismissed, if we wish to maintain a sudden
threshold with humanity and not a gradualist account.

Similarly, the stagnant nature of stone tools 2.6–1.8 million years
ago may potentially be dismissed as a ‘sporadic learning’, simply pre-
serving knowledge but not accumulating it. Around 1.8 million years
ago, however, the assertion grows more tenuous. Stone tool manufacture is
less haphazard, with deliberate shapes being constructed that are passed on
culturally. *Homo ergaster/erectus* also migrated into different environments
in Asia, no mean feat, and there is evidence of a demographic boom in Af-
rica that may have driven the migration. A demographic boom also indi-
cates an enhanced ability to exploit niches in the ecosystem. There is also
evidence of increased brain size and sociality (Stringer 2011: 25–26; Tattersall 2012: 123–124). All of these things are staple arguments for collective learning in *Homo sapiens* and the profound impact they had on the Palaeolithic world. There is no reason why the same arguments could not apply to *Homo ergaster/erectus*, albeit on a lesser scale. But this is a difference of scale, not a difference of kind.

Nevertheless, the jury is still out on whether there was any technological accumulation. When *Homo ergaster/erectus* first arrived on the scene 1.8 million years ago, they were making tools that had not changed significantly since *Homo habilis*. However, 1.78 million years ago we begin to observe rare and crude new forms of teardrop hand-axes in Kenya (Tattersall 2012: 105). But for about 200,000 years we see, for the most part, no major widespread improvements in the stone tools of *Homo ergaster/erectus*. This remained the case in most migratory regions. The tools were functional. The object was to get a flake edge. No aesthetics were involved. But in Africa 1.5 million years ago, where *Homo ergaster* populations were at their densest, the hand-axes first made 1.78 million years ago rapidly became common. What is more, they improve in quality, shaped with a flat edge into multipurpose picks, cleavers, and other kinds of implements (Tattersall 2008: 125–127). This has been considered by some archaeologists as the first clear sign of tinkering, accumulation, and improvement of technology, if only a much weaker form of collective learning among *Homo ergaster/erectus* than *Homo sapiens*, who are the real champions at it.

Still, the assertion that *Homo ergaster/erectus* had crossed the threshold into mild collective learning can still be reasonably disputed and dismissed if the case is only based on such limited evidence. This argument is less feasible for the hominines of the last million years. *Homo antecessor*, *Homo heidelbergensis*, and the Neanderthals presided over the systematised and regular use of fire in hearths (790,000 years ago), the earliest wooden spears (400,000 years ago), the earliest use of composite tools (400,000 years ago), the first evidence of intricately constructed shelters (350–400,000 years ago), and the first prepared core tools (300,000 years ago) all before *Homo sapiens* was ever heard of (Goren-Inbar et al. 2004: 725–727; Tattersall 2008: 125). *Homo heidelbergensis* became the first pan-Old World hominine (600,000 years ago), showing signs of technological improvement, with the earliest specimens using simpler tools than later ones, and even evidence of pigments at Terra Amata, a site in Europe 350,000 years ago (Oakley 1981: 205–211). The Neanderthals adapted to climes that made clothing and other cultural innovations necessary for insulation and warmth. There is also limited evidence for use of pigments (Stringer 2011: 163–165). They used complex tool manufacture,
with prepared stone cores, producing a variety of implements, sharp
points, scrapers, teardrop hand-axes, wood handles, with deliberate use
of good stone materials, and an endless supply of variations and signs of
improvement over time (Tattersall 2012: 166–173; 2008: 150–158).

Now, bearing in mind that *Homo sapiens*, without question, is by far
the most talented at collective learning, there is very little doubt that these
hominine innovations accumulated over several generations, did not fade
away, improved in quality down the chronology, and yielded a certain
degree of ecological success and extensification into new environments.
Interestingly enough this happened in several hominine species for
which there has yet to be found clear evidence of symbolic thought and
complex language, two things that are sometimes (and probably incor-
rectly) attributed as the *cause* of collective learning rather than more ef-
ficient vehicles for it. All this raises severe questions about the threshold
that must be addressed. It also bleeds into questions about human
uniqueness and why it is so important for some people to draw an iron-
clad boundary between us and our evolutionary family that distinguishes
us in essential kind. This sort of essentialism is alien to many forms of
evolution. It would be a rash statement indeed to say that if *Homo sapiens*
had never existed and had never out-competed other hominines, that
these same hominines would not have possessed collective learning or
attained some degree of cultural complexity. Much more work, at any
rate, would be required before one could make such a statement. As it is,
it appears a more gradual evolution of collective learning occurred over
several hominine species.

The question of a 'Palaeolithic revolution' is another point of con-
tention. Did *Homo sapiens* undergo a biological change c. 50,000 years
ago and does this explain the explosion of technological complexity that
appears in the fossil record? Or did collective learning and population
density achieve a point of saturation allowing for a faster pace of learn-
ing? Or did this complexity arrive in Africa prior to 100,000 years ago as
McBrearty and Brooks have suggested (McBrearty and Brooks 2000:
453–563)? If the latter, it is probably the result of collective learning
maintaining a faster rate of accumulation in denser African populations
than disparate migrant ones. Collective learning may have also played
a role in the Out-of-Africa migrations themselves. Recent DNA studies
have shown exponential human population growth in Africa preceded
our most successful migration out of that continent c. 60,000 years ago
(Atkinson, Gray, and Drummond 2009: 367–373). This coincides with
evidence of an increase in the complexity of technology around
the same time (Mellars 2006: 9381–9386). It is possible that there is a cor-
relation between migration and population growth that may be ex-
plained by the gradual rise of collective learning. If such a connection
exists for the ecological success of humans, it might also be applied to
the prior migrations of *Homo ergaster/erectus*, *Homo heidelbergensis*, and
the Neanderthals. The human correlation is also reinforced by genetic
studies by Powell, Thomas, and Shennan that show population density
in Africa may have reached a critical mass to allow more consistent
technological accumulation without as many periods of loss (Powell,
Shennan, and Thomas 2009: 1298–1301).

Decline in population and collective learning can also lead to a Tas-
manian Effect, where technology disappears or undergoes simplifica-
tion. Jared Diamond coined the term for the extreme disappearance of
technology in Tasmania (Diamond 1978: 185–186). Kline and Boyd re-
cently established a similar case in Oceania, where technology declined
in groups that were isolated or lost density (Kline and Boyd 2010: 2559–
2564). My own work has unearthed a similar occurrence of technologi-
cal disappearance and simplification in the extreme and sustained
population decline of isolated parts of post-Roman Western Europe in
the fifth and sixth centuries (Baker 2011b: 217–251). Finally, Zenobia
Jacobs, Bert Roberts, Hilary Deacon, and Lyn Wadley established two
Palaeolithic Tasmanian Effects in Africa, at Still Bay 72,000 years ago
and Howieson's Poort 64,000 years ago (Jacobs et al. 2008: 733–735; Wad-
ley et al. 2009: 9590–9594). All are cases where technology disappears or
is simplified in areas that suffered isolation and population decline –
a phenomenon deemed more likely in the Palaeolithic due to lower
populations and lower connectivity. It might explain why collective
learning took tens of thousands of years to get off the ground, relatively
speaking, before the explosion of agriculture.

## II. Accumulation of Innovations from Foraging to Agriculture

Culture evolves through an accumulation of small variations. Those
ideas that are successful or useful, in whatever way, are selected and
spread throughout a society. Every invention of technology or break-
through in practice, like in agriculture, comes from a series of small im-
provements contributed by a long dynasty of innovators. The single
innovation of a genius might be of revolutionary magnitude and reper-
cussions, but would have been impossible without the hundreds of tiny
innovations made by the hundreds of generations that came before it.
Newton said he stood on the shoulders of giants. It might be fairer to
say that every ordinary person stands on the shoulders of other ordi-
nary people – some with more than ordinary perceptiveness and abso-
lutely extraordinary timing. Our technologies, our institutions, our lan-
guages are far too elaborate for even the most gifted of geniuses to cre-
ate from scratch. Human beings have a tremendous capacity for lan-
guage. We can share information with great precision, accumulating
a pool of knowledge that all people may use. The knowledge an indi-
vidual contributes to that pool can long survive his death. If our popula-
tions are large and well-connected enough, more information is ac-
quired by each passing generation than is lost by the next. It can be
accessed and improved by countless generations.

From the origins of collective learning in the Palaeolithic, it is clear
that from the rising carrying capacity and increase in cultural variants
and innovations, that collective learning has great bearing on the his-
torical narratives. Nowhere is this more relevant than the discussion of
population cycles. The inception of the current arc of complexity is eas-
ily spotted. Around 74,000 years ago there was a catastrophic eruption
at mount Toba, on the island of Sumatra, part of what is now Indonesia.
It was worse than anything in recorded history. The eruption drastically
lowered temperatures on Earth for several years (Rampino and Self
1992: 50-52). Genetic studies show that the resultant decline in flora and
fauna upon which humans could predate had reduced the population to
near extinction. It is likely that in the aftermath of a period of starvation,
on the entire face of the Earth there were scarcely more than 10,000 (and
perhaps as few as 1000) human souls, which, as an aside, is what makes
our long history of racism so abhorrent and absurd, particularly those
ideological impulses inspired by Darwinism (Williams et al. 2009: 295-
314; Rampino and Ambrose 2000: 78-80; Ambrose 1998: 623-651). Here is
a low watermark for the current trend of human population dynamics.
Evidently the starvation did not last long. In approximately the same
amount of time that separates us from the dawn of agriculture, the hu-
man species had recovered and c. 60,000 years ago migrated out of Af-
rica across the world. By 30,000 years ago, the foraging human popula-
tion had risen to half a million. By 10,000 years ago, the innovation of
hunter-gatherer bands had allowed them access to almost every envi-
ronment on Earth, from Eurasia to Australia to the Americas. We must
remember that the carrying capacity for a foraging band is quite low and
they need a vast area to supply relatively small numbers. Nevertheless,
by the dawn of agriculture the ranks of our species had swelled to six mil-
lion people, approaching the full capacity for supporting hunter-gatherers
of which the entire surface of the Earth is capable (Livi-Bacci 1992: 31).
Innovations began to mount up. The earliest recorded evidence for herd-
ing goats and sheep in Southwest Asia is from 11-12,000 years ago, and
one thousand years later, we have evidence for the farming of wheat,
barley, emmer, lentils, and pigs. By 8,000 years ago, East Asia had be-
gun using millets and gourds, and the Americas had domesticated llamas and maize. By 6,000 years ago, Southwest Asia had domesticated dates and the grapevine, while East Asia had domesticated water chestnuts, mulberries, water buffalo, and that mainstay of all Asian crops – rice (Roberts 1998: 136). All of a sudden, much larger numbers could be supported over a much smaller land area. The agrarian civilizations brought about a greater degree of connectivity, faster population growth, and a new rapid pace for innovation. Suddenly there were a lot more minds to generate ideas and a lot less space between those minds in order to conference. Agricultural efficiency gradually improved and practices slowly spread to new regions. From the upper limits of the carrying capacity for foragers, the population increased nearly tenfold by 3000 BC to 50 million people, and it took only another 2000 years to increase this number to 120 million (Biraben 1979: 13–25). But there was a problem. The tinkering of ideas in cultural evolution is random, after all. For nearly 10,000 years, the growth in the carrying capacity of agriculture was sluggish while population growth was exponential, and so there was a series of miniature waves of population collapse and recovery throughout the period of agrarian civilizations. From there came the advent of industry which has raised the carrying capacity and enhanced collective learning by leaps and bounds.

<figure><img src="image_4.png" /><figcaption>Fig. 2. The asterisk (*) marks a period of severe population decline where collective learning is lost</figcaption></figure>

Bear in mind that each innocuous-looking downturn on the graph represents a period of intense starvation, suffering, and death. Every few centuries an agrarian civilization overshot its carrying capacity and count-
less famines, instability, poverty, and plagues ravaging a malnourished landscape, resulted. Each droop of the line represents the death of millions. Sometimes population loss would be so significant that it adversely affected the onward march of collective learning, as the asterisk simulates. If collective learning is lost, the carrying capacity falls, and the smaller group of innovators has to make up lost ground. This reversal of the process is known as the Tasmanian Effect.

### III. Collective Learning Undermined and Overthrown

When a catastrophe strikes and a population is reduced and isolated, the accumulation of knowledge slows down and a population's ability to retain information is weakened. The most extreme example of this is from Tasmania, which possessed many technologies shared by their Australian relatives to the north, but whose skills and technologies gradually disappeared after Tasmania was cut off from Australia c. 10,000 years ago. Jarred Diamond famously observed that when the Europeans first visited Tasmania in the seventeenth century, the native population was small, isolated, and lacked many of the tools and methods that the aboriginal Australians on the mainland possessed. The Tasmanians could not produce fire in hearths, they did not have boomerangs, shields, spears, no bone tools, no specialized stone tools, no compound tools like an axe head mounted on a handle, no woodworking, no sewing of clothes despite Tasmania's cold weather, and even though they lived on the sea coast, they had no technology for catching and eating fish (Diamond 1978: 185–186). Diamond hypothesized that this was caused by the loss of the land bridge between Australia and Tasmania c. 10,000 years ago. A subsequent recent study of Tasmania's archaeological and ethno-historical evidence has borne out the same result (Henrich 2004: 197–218). The Tasmanians upon European contact had lost a great deal of technology that was enjoyed not only by their neighbours across the Bass Strait but also by most groups of *Homo sapiens* in the Palaeolithic. Humans probably arrived in Tasmania from Australia 34,000 years ago, across a land bridge, and were indeed cut off 12,000–10,000 years ago by the rising sea (Jones 1995: 423–446). The archaeological evidence shows that at the time of migration, the Tasmanians were producing bone tools, cold-weather clothing, fishhooks, hafted tools, fishing spears, barbed spears, fish/eel traps, nets, and boomerangs, and continued to do so even after the island was cut off by the rising seas. These tools gradually declined in frequency, variety, and quality between 8,000 and 3,000 years ago before completely disappearing from the archaeological record (Henrich 2004: 198). Thereafter, to hunt and fight, the Tasmani-
ans used one-piece spears, rocks, and throwing clubs, and their entire toolkit consisted of 24 items, as opposed to the hundreds of tools possessed by the Australians to the north (Ryan 1981). Bone tools are on the Tasmanian record from at least 18,000 years ago, just as they were in Australian records and also enjoyed by Palaeolithic man in Africa from 89,000 years ago (Webb and Allen 1990: 75-78). The archaeological record also shows that from 8,000-5,000 years ago, the Tasmanians relied heavily on fishing, second in their diet only to seal hunting, and much more than hunting wallabies. By 3,800 years ago, fish bones disappear from archaeological sites and it was not part of the Tasmanian diet when Europeans arrived (Henrich 2004: 199). All told, Jared Diamond's hypothesis forty years ago about a loss of knowledge due to connectivity and a shrinking population has been largely borne out by subsequent research.

It is not the only case where such a phenomenon has occurred, though it is undoubtedly one of the most extreme. Other Pacific groups have a history of losing canoe, pottery, and bow technology (Rivers 1926). The Inuit were decimated by a plague and lost knowledge to construct kayaks, bows and arrows, and the leister, until it was reintroduced by migrants from Baffin Island (Rasmussen 1908; Golden 2006). Michelle Kline and Robert Boyd detected a similar trend in Oceania (Kline and Boyd 2010: 2559-2564). The ecological similarity between these environments allowed Kline and Boyd to focus on fishing technology, preventing geographical differences from distorting the results. The groups also had a common cultural descent. The finding was that the number of tools and the complexity of them are higher in larger well-connected populations. Zenobia Jacobs, Bert Roberts, Hilary Deacon, and Lyn Wadley have determined that there was a Tasmanian Effect at Still Bay 72,000 years ago and Howieson's Poort 64,000 years ago (Jacobs et al. 2008: 733-735; Wadley et al. 2009: 9590-9594). At Still Bay, humans created highly complex flake technology, including finely shaped, bifactually worked spearheads. At Howieson's Poort, humans created composite weapons and stone artifacts, both of which were hafted. These two sites were more innovative than much else in Middle Stone Age Africa, and an increasingly complex social organization is implied by the use of bone tools, symbols, and personal ornaments. The strange thing is that these two industrious cultures are separated by several thousand years of stagnation and total disappearance of their technologies. And the differences between the way the technologies of Still Bay and Howieson's Poort are constructed implies that when Still Bay disappeared, the innovators of Howieson's Poort started from scratch.
Both cultures intriguingly fall within the genetic bottleneck that occurred 80-60,000 years ago (Jacobs et al. 2008: 733). It would appear a relatively low carrying capacity for hunter-gatherers ranging across a territory, the small size of their groups, and their vulnerability to ecological changes and disasters made the disappearance of knowledge more common in the Palaeolithic. The Tasmanian Effect is not just confined to hunter-gatherer societies, however, though due to the low connectivity and small populations of those societies it may be more common. The Tasmanian Effect can also occur in agrarian civilizations. It occurred in the post-Roman West in the fourth, fifth and sixth centuries AD. We must make clear, however, that this trend was not mirrored in the Roman-Byzantine East, which underwent a different population trend, including growth through the fourth, fifth, and into the sixth centuries AD. The extreme settlement abandonment of the Roman West, started in 350, intensified by the Germanic invasions, and then further exacerbated by the bubonic plague of Justinian, reduced the already sparse and illiterate population to low levels. The loss of technology and expertise is reflected in the decline of various artisanal practices, pottery methods, military equipment and architectural knowledge (Murray-Driel 2001: 56-64; Pugsley 2001: 112-115; Ward-Perkins 1999: 227-232; Arthur 2007: 181; Mannoni 2007: xlv-xlvii; Knight 2007: 100; Rossiter 2007: 115; Bishop and Coulston 1993: 122-149; Coulston 2002: 23; Williams 2002: 45-49; Murray 1986: 31-32; King 2001: 26-28). It remained to subsequent generations to rediscover classical learning and devise new methods to make up for this shortfall and raise the carrying capacity once again. The process of recovery from the Tasmanian Effect took Western Europe more than 700 years.

#### IV. Song China and Industrial Britain: The Two ‘Great Divergences’

In the past two millennia, certain key innovations in Song China and Industrial Britain have prompted an explosion of growth in collective learning, bringing humanity ever closer to industrialization. There were other periods in human history which arguably could be deemed as ‘explosions’ of collective learning (the Axial Age, the Renaissance, the Enlightenment, the Scientific Revolution, etc.) but what is notable about Song China and Industrial Britain is that they were explosions in collective learning that prompted one world zone to tear ahead of their contemporaries in that time period. Hence, scholars often use the phrase ‘great divergence’ as popularised by Ken Pomeranz (2000). This term has so far applied to the industrial divergence that separated ‘West from
rest', but taken within the context of collective learning it can also apply
to an earlier period.

The first great divergence was in Song China in the ninth and tenth
centuries AD which led to something staggeringly similar to the rates of
innovation and production seen in the Industrial Revolution. In the
sixth century BC, the carrying capacity of China was already ahead of
ancient Europe. China was already growing crops in rows, paying at-
tention to weeding, and frequently employing iron ploughs. All of these
innovations would not be employed in Europe for centuries. The Chi-
nese also used horse harnesses by the third century BC, avoiding the
risk of strangulation by a horse and permitting them to carry ploughs
and heavy equipment. The seed drill came into use by the second cen-
tury BC. In the first-second century BC, the types of mouldboard
ploughs that only became available in Europe after Charlemagne were
already in use in China (Temple 1986: 15–20). At the time, the majority
of the Chinese population concentrated in the north in the Yellow River
valley where they farmed millet and wheat – not rice (Ponting 1991: 93).
Even before the explosion of wet rice agriculture in China, these innova-
tions served to create a higher agricultural output and carrying capacity
compared with Roman Europe centred on the Mediterranean Sea, both
in the East and especially the sparsely populated backwater that was the
Roman West.

Until the first millennium AD, both world zones had supported them-
selves mainly on grain products, with the Chinese sustaining a higher car-
rying capacity than Europe due to better agricultural practices. Even fur-
ther divergence happened between 500 and 1000 AD with the spread of
wet rice production in China, which has a much higher yield than grain.
Per hectare, traditional varieties of rice support around 5.63 people com-
pared to 3.67 people on a hectare of wheat (Fernandez-Armesto 2001:
105). Dry rice farming came first. However, it has a carrying capacity that
is not much higher than wheat. The problem is that dry rice farming re-
quires constant weeding (Woods and Woods 2000: 50). It was also ill-
suited to the climate of northern China. In the north, millet farming in the
Yellow River valley began in 6,000 BC (Higman 2012: 23). By 200 BC,
the Han north was sustained by the farming of millet and wheat in an inef-
ficient two-crop rotation. The inhospitable soils and temperatures of the
Yellow River valley in the north usually permitted only one crop a year.
From AD 1, wheat was immediately planted after millet or soy to in-
crease crop frequency. In order to avoid too much loss of nutrients from
repeated planting, the crop was often planted in alternating furrows,
with new furrows being planted in between the old ones. The Han
plough had limited depth of ploughing. Over-seeding was sometimes
used to save labour at the expense of the yield (Hsu 1980: 112–114).

Meanwhile, in southern China, rice was domesticated in 7,000 BC
along the Yangtze River and by 3,000 BC, a large-scale wet rice farming
was present (Chi and Hung 2010: 11–25; Zheng et al. 2009: 2609–2616).
For several thousand years, the yield was still relatively low because
farmers did not employ terracing and paddy systems. Instead, wet rice
was grown beside streams and in small irrigated plots (Simmons 1996:
99). This is the reason why northern China held the bulk of the popula-
tion despite a long history of wet rice farming in the south. Neverthe-
less, wet rice farming even without terracing and paddies was fairly
productive. In the third century BC, the Qin Emperor Shi Huangdi con-
structed a 20-mile canal to facilitate transport of wet rice from southern
China to the populous north (Headrick 2009: 43). Slowly but surely the
carrying capacity was being raised. Finally, labour intensive methods of
terracing and paddies caught on in southern China in AD 200 (Chang
2003: 16). The employment of a crop with much higher yields than grain
and that can sustain higher population densities, might go some way to
explaining the higher rate of collective learning and innovation that set
these civilizations ahead of other zones in Eurasia in terms of popula-
tion and cultural complexity.

At the fall of the Han dynasty, the barbarian attacks forced more Chi-
nese south to the Yangtze River basin. The reunification under the Sui in
AD 589 made the region more stable, and rice expansion and the migra-
tion of the northern population to the south continued in earnest (Ponting
1991: 93). Gradually, migration between AD 500 and 1300 transformed
the agricultural output and population distributions of China, particu-
larly intensifying in the Song dynasty (AD 960–1276). The Song gov-
ernment initiated a set of policies to shift agricultural production from
the northern millet and wheat regions to the wet rice producing south.
In 1012, the Song introduced a strain of rice from Vietnam that allowed
for multiple harvests per year, or the alternation of rice in summer and
wheat in winter. The government appointed ‘master farmers’ from local
communities, who were to disseminate new farming techniques and
knowledge of new tools, fertilizers, and irrigation methods. The Song
also introduced tax breaks on newly reclaimed land and low-interest
loans for farmers to invest in new agricultural equipment and crops
(Bray 1986: 203). The Song encouraged terracing, created fields that
were evenly flooded and trapped fertile silts from being washed away.
In 1273, the Chinese government distributed 3,000 copies of *Essentials of
Agriculture and Sericulture* to landowners in order to improve crop yields.
Wet rice farming by this method produced two-three crops a year com-
pared to the meagre one-crop harvest of the millet-producing north
(Headrick 2009: 51–52, 85).

The adoption of wet rice farming and the migration of many people
to the south had a profound impact on collective learning in Song
China. In AD 1, the population of China was around 50–60 million and
did not exceed that number level until the tenth century (Faser and Ri-
mas 2010: 118). During the 900s and 1000s under the Song dynasty, mi-
gration to the Yangzi river valley to farm rice raised the carrying capac-
ity of China from 50–60 million to 110–120 million, with record high
population densities of 5 million people farming an area of 40×50 miles
(Korotayev, Malkov, and Khaltourina 2005: 186–188). By 1100, this con-
stituted 30–40 per cent of the population of the globe, compared to all
Europe's 10–12 per cent as it just entered its ‘Great Leap Forward’ (Bira-
ben 1979: 16). The population was raised, so was the density, and so the
number and connectivity between potential innovators was increased.
This really constitutes the first ‘Great Divergence’ between East and
West, when Chinese collective learning advanced by leaps and bounds
by a much higher carrying capacity. It is no coincidence that the Song
dynasty was one of the most technologically advanced and industrially
prodigious societies in pre-modern history, almost to the point that the
late Song dynasty could conceivably have had an Industrial Revolution
of their own. For instance, the annual minting and use of coin currency
was increased greatly under the Song (Hansen 2000: 264). Farming tech-
niques improved: the use of manure became more frequent, new strains
of seed were developed, hydraulic and irrigation techniques improved,
and farms shifted to crop specialization (Elvin 1973: 88). Coal was used to
manufacture iron and iron production increased from 19,000 metric tons
per year under the Tang (AD 618–907) to 113,000 metric tons under the
Song (Hansen 2000: 264). The Song dynasty was the first to invent and
harness the power of gunpowder. Textile production showed the first
ever signs of mechanization (Pacey 1990: 47). Some surprisingly modern
innovations in Song China did not arise in conjunction with an in-
creased population, but the eleventh and twelfth century innovations
followed after the initial rise of the Chinese carrying capacity between
AD 500 and 1000. The adoption of wet rice farming and the migration of
the Chinese farmers from the northern grain producing region to the
Yangzi River valley triggered a rise in the number of potential innovators
and a Great Divergence that placed China as one of the largest, densest,
and most productive regions of the globe from AD 900 to 1700 – at the
very least.
The second explosion of collective learning was the Industrial Revo-
lution itself. It was born out of a collection of small innovations that
were selected and spread, combining into a feedback effect that signifi-
cantly increased the carrying capacity of the human species. In 1709,
Abraham Darby used coke to manufacture iron, inefficiently, until tink-
ering made the practice efficient enough in the 1760s to be selected and
spread across Britain. Henry Cort invented a process in 1784 to create
bars of iron without use of coke, further increasing efficiency (McClellan
and Dorn 1999: 279–281). In seventeenth century France, Denis Papin
revived an invention that was known to the Romans, the Chinese, and
many other cultures using atmospheric pressure, later worked on by Eng-
lishman Thomas Savery, and eventually producing Thomas Newcomen's
steam engine in 1712. More tinkering and the harnessing of a steam en-
gine to power a blast furnace for iron production in 1742 also raised
production. From there James Watt tinkered with the steam engine in
the 1760s making it even more efficient (Ibid.: 282). In textiles, the Dutch
innovations using waterwheels and the Italian factory plans were brought
into England and further innovated into textile production in the 1730s.
Three more innovations in the 1780s – the waterframe, the spinning
jenny, and the spinning mule, all built on these innovations – trans-
formed cotton to a common commodity rather than a luxury good (Mo-
kyr 1990: 96–98, 111). Once the steam engine was brought into these in-
novations, the production efficiency advanced even more. From here
the steam engine was also brought in to enhance locomotion. The nine-
teenth century saw this advanced capacity for production and innova-
tion spread into almost every industry and across Europe and the globe.
Much of the initial practices that led to the spark of industry were familiar
in medieval China, but it was these cultural variations that came together
at the right time in the right place to raise the carrying capacity and pro-
duce a Cambrian explosion of further innovation (Pacey 1990: 113; Mokyr
1990: 84–85; Needham 1970: 202). In many ways, it was a matter of
chance. The occurrence of variation and selection is the key to the ad-
vance of collective learning. Conditions have to be just right, there has
to be an available niche, and certain cultural variations have to be able
to combine to produce material breakthroughs.

V. Collective Learning and the Rise of Complexity

From here collective learning has delivered us to the increased amount
of energy, production, and almost instantaneous connectivity that we
enjoy today. We have split the atom, revealing for the first time a micro-
cosm of the massive amounts of energy that have radiated for billions of
years out from the heart of the sun. We have established highly efficient forms of mass transportation, by sea, land, and air. We have seen the birth and expansion of the Internet, which ties the entire globe of potential innovators together into one community of lightening fast communication. The world's population has just passed seven billion, providing us with an increasing number of potential innovators. Provided we do not exhaust the resources of the planet in the same way that agrarian civilizations occasionally exhausted the resources of the field, we may be facing another explosion of innovation quite soon that shall look as different from the technologies of the industrial and post-industrial eras as factories and assembly lines differ from the implements of early agriculture. Collective learning not only defines our past and present, but our future as well. From this source radiates greater and greater amounts of complexity.

It is important to look at how collective learning ties into the broader Big History themes developed by Eric Chaisson and Fred Spier: the rise of complexity in the Universe and energy flows. It would appear that collective learning plays a direct mechanistic role in increasing the level of free energy rate density and also the number of available cultural variations and technological innovations. This raises the level of complexity in the Universe, just as solar, chemical, and biological evolution do.

Collective learning and rising complexity also ties into Universal Darwinism, an algorithm of random variation and non-random selection, which I have explored in other works (Baker 2011a, 2013, 2014). Variations emerge from collective learning on an unprecedented scale. By comparison, few variations emerge from the chaos of the quantum realm to the Newtonian physical realm, only about a hundred elements emerge from stellar evolution, a few thousand variations emerge from chemical/mineral evolution, millions of variations emerge in the biological realm, and in cultural evolution and collective learning the many variations of innovation are increased further still.

At each stage the free energy rate density increases, as does the magnitude of energy that can be harnessed. And it would appear that the number of possible outcomes is relative to the complexity of the process under discussion. When we arrive at something as complex as culture and modern human society, with a free energy rate density that is many times higher than the average product of genetic evolution and four million times higher than a galaxy, there are a mind-boggling number of cultural and technological combinations. Essentially, if you were to take a human brain and a brain sized chunk of a star, there is no question that the former would have a much higher density of free en-
ergy at any given time. The rate of complexity seems to increase with the number of viable selection paths.

<table><caption>**Table 1.** Amount of free energy running through a gram per second, and the australopithecine and human free energy rate density is determined from the average energy consumption of an individual (Chaisson 2010: 28, 36)</caption><thead><tr><th>Generic Structure</th><th>Average Free Energy Rate Density (erg/s/g)</th></tr></thead><tbody><tr><td>Galaxies</td><td>0.5</td></tr><tr><td>Stars</td><td>2</td></tr><tr><td>Planets</td><td>75</td></tr><tr><td>Plants</td><td>900</td></tr><tr><td>Animals (i.e. human body)</td><td>20,000</td></tr><tr><td>Australopithecines</td><td>22,000</td></tr><tr><td>Hunter-Gatherers (i.e. 250,000–10,000 years ago)</td><td>40,000</td></tr><tr><td>Agriculturalists (i.e. 10,000–250 years ago)</td><td>100,000</td></tr><tr><td>Industrialists (i.e. 1800–1950)</td><td>500,000</td></tr><tr><td>Technologists (i.e. present)</td><td>2,000,000</td></tr></tbody></table>

It would appear, for the time being, that collective learning and the complexity it bestows is the highest point in this process of which we are yet aware. There are two tiers of human evolution. The first is genetics, which operates in the same way as for other organisms. Those genes gave humans a large capacity for imitation and communication. Those two things enabled the second tier. Culture operates under similar laws, but on a much faster scale. Cultural variations are subject to selection and the most beneficial variations are chosen. Unlike genes, these variations can be transmitted between populations of the same generation and can be modified numerous times *within* that generation. Like a highway overpass looming over older roads, collective learning can blaze along at a much faster rate of speed.

We do not yet know where this tremendous capacity for collective learning will lead. It is likely to reveal even higher levels of complexity in the future, if we do not wipe ourselves out. When it comes to the broader trend in the Universe, it is fairly clear that the next rise of complexity will be down to animate rather than inanimate physical processes. As stars burn down, as planetesimals tumble through cold space, it may be that species like us, with a tremendous ability for collective learning and harnessing energy flows, will reveal even more remarkable phases of cosmic evolution. In that sense, collective learning tells us not only about human history, but about the overwhelming thrust of human
destiny in a rising crescendo of complexity. That is, if we do not go extinct
beforehand. An asteroid collision, a volcanic super-eruption, or a nuclear
war could wipe the slate clean. Eventually the Sun will destroy the Earth.
Even in the short term, as the twenty-first century appears to deepen fur-
ther into crisis, the entire arc of collective learning could come very
abruptly to an end. We shall then never know where collective learning
might have led us or what we might have achieved as a population of
billions of increasingly educated and well connected innovators. Man-
kind's great task in the twenty-first century is to survive it.

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# From Particles to Politics

Lowell Gustafson

## Abstract

In this paper we investigate the gradual and uneven development in the complexity of polity, or the sustained, structured relationships that incorporate earlier ones and go on to be subsumed by subsequent relationships. This takes us from the very early and long-lasting relationships among two types of quarks to the emergence of human polity, with annihilations, extinctions, and wars as part of the often unpredictable development. Can the study of this process add to the likelihood that it will move more thoroughly through the latest transition toward the greatest known complexity in polity, or will it face the temporary or even permanent effects of entropy?

**Keywords:** Big Politics, polity and natural science, Big History and politics, politics and science, Political Science.

Big Politics is the process of emergent complexity of sustained, structured relations that began with the Big Bang and has continued in stages through today, as it may continue to do in the future. The natural sciences explain how the simplest forms of sustained, structured relationships emerged and how they gradually, unevenly, and increasingly became more complex over time (Christian 2004, 2011; Chaisson 2006; Brown 2007; Spier 2010; Shubin 2013). Relationships have become progressively complex between sub-atomic particles, atoms, molecules, cells, morphology, animals, human families, villages and cities, nations, regions and empires. Each less complex and older set of relationships is incorporated within newer and more complex ones.

From the beginning, each new combination of units exhibits new properties. One significant new property was the emergence of consciousness and self-consciousness. Exactly how matter comes to be able to reflect on itself is still not fully understood, but the ability emerged out of pre-reflective matter. With this new property, conscious beings have played a greater role in choosing among alternative, imagined futures in ways that can create or inhibit further growth in complexity.

Politics among humans are certainly different from, but also emergent from, earlier types that vastly precede the relatively brief human period. Pre-written and pre-human politics are not mere analogies for human politics nor inevitable causes of it, but its necessary antecedents.
It is not possible to study the formation of atoms 300,000 years after the Big Bang and predict from that the writing of Plato's *Republic*. It is also a misperception that there is a great divide between human and pre-human politics. Human politics, much less politics before writing, did not emerge fully blown and without antecedents. The field of political science still needs to incorporate the story that the natural sciences permit us to tell, and not to begin its study with the ancient world of a few thousand years ago or even 200,000 years ago in political anthropology. As familiar as ancient political thought is to students of political philosophy and contemporary politics to those who use such methodologies as survey analysis, the study of political science can now vastly predate those periods. The study of light, rocks, bones, and blood as well as written texts, surveys, and electoral results, tell a story of the entire past from which human politics has emerged and remains bedded.

In one way, examining the relationship of politics and nature is nothing new. The famous ancient Greek philosopher, Aristotle, wrote books such as one on *Physics* and another on *Politics*. In the latter, he wrote that humans are by nature political animals. In the European medieval period, Thomas Aquinas developed Aristotelian thought on natural law; he argued that humans were created within a politically constituted community. By the seventeenth and eighteenth centuries, such State of Nature political philosophers as Thomas Hobbes, John Locke, and Jean-Jacques Rousseau postulated human politics before or without such institutions as the state. They wanted to determine how to construct states so that they helped resolve the basic problems of human nature. The authors of the U.S. Constitution saw their political construct as consistent with nature (Kammen 2006). For all of their differences, they all saw human politics as rooted in nature. None of them had the same understanding of nature as has developed since Darwin, Einstein, Hubble, and others in recent centuries.

The emergent complexity of sustained, structured relationships that incorporate earlier ones in new combinations and with new properties is possible due to access within pockets to high quality energy. The second law of thermodynamics would lead us to expect entropy, or transitions from greater to lesser order rather than emergent complexity, which is possible in energy rich pockets. From the origins of polity until today, we can observe in certain places a process of increased complexity due to the existence in certain locations of access to energy. If we can resolve our current energy crisis in a sustainable way, and if we have the imagination, this process may continue. However, there was no uniformity in emergent complexity in the past and there is no guarantee it
will continue in the near future. In the distant future, we are virtually certain to face entropy. A narrative of humanity's common origin in Africa, life's origin from LUCA, and the Universe's origin from a singularity, may help foster greater complexity in politics among humans and between humans and our environment.

The major sub-fields of political science are often presented to students with discussions of their origins, structure, and emergent complexity. The origins of these sub-fields occurred centuries or even millennia ago. But our question here is not about the origins and development of American Politics, International Politics, Comparative Politics, or Theoretical Politics; it is about Politics. How has it developed greater complexity and become the human politics that we know today? What instruction might this provide for the future? Politics does not begin with the U.S. Constitution, the Treaty of Westphalia, or Plato's *Republic*. It began long before 1787, 1648, or 2,500 years ago. It cannot be studied only by public opinion polls since it began before any living person. It cannot be studied only by reading primary sources since it began before writing. It is not structured now just by written constitutions or by common law. Politics began long before in ways that continue to make us what we are today. Just as the past did not begin with writing or even with humans, so politics also did not begin with them. Our present and our politics emerge from much earlier antecedents that still includes them. Our well-being in the future may depend on our understanding this and acting on it. In the period since the origin of consciousness and culture, or collective learning, the persuasive narratives we tell ourselves and how we frame our stories become part of the evolution of emergent complexity.

## Baryonic Matter

Sustained, structured relationships emerged quickly after the Big Bang, according to the standard view (Carroll 2012). The many complex properties that would characterize human politics were not inevitable from the sustained structure that began to develop 13.82 billion years ago (Planck 2013 Results Papers).

Perhaps, branes bounced or an infinitely hot and dense point without mass began expanding and cooling 13.82 billion years ago. It may be that nothing is always pulsating and is regularly turning into a variety of forms of something. Perhaps, we live in a multiverse with an infinite number of Big Bangs occurring all the time in ways we cannot detect or imagine. Other universes may be sharing our space or off in other locales. Or maybe our own universe has an infinite set of cycles of trillions of years (Singh 2004; Lederman and Teresi 2006; Greene 2011; Lederman
and Hill 2011; Steinhardt and Turok 2007). We used to think there was
only one galaxy. Then we wondered if there were other inhabitable
planets. We now know there are great numbers of both. Why should
ours be the only universe? However, for now we will restrict our atten-
tion to our own universe and to the development of polity.

At the earliest moment in our universe's known history, there was
little discernible structure. If there was a singularity, it is hard to see
how there was any structure in a point without mass. Ordered relations
among parts did not begin until almost immediately after the Big Bang.
If America was one nation formed by 13 former colonies and could
adopt the Latin motto, *e pluribus unum* (from many one), the universe
might adopt the opposite of from one many (*multa ex uno*). Incredible
variation would emerge after the radiation period immediately after the
Big Bang. Increasingly complex relationships between a relative few of
these varied parts began very quickly.

All but immediately after our own universe's Big Bang, when en-
ergy first congealed into normal or baryonic matter, six types of quarks
appeared. Four of these quarks led extraordinarily brief lives before re-
turning to energy; they did not go on to form more complex forms of
matter. However, two of them – the up and down quarks – did form rela-
tionships as they appeared. This will be a pattern. Some things go on to
participate in emergent complexity. Many do not.

At least those quarks that survived formed relationships. For a bil-
lion and one bits of matter that appeared, a billion bits of anti-matter
with opposite spin did as well. When they come into contact, matter and
anti-matter annihilate each other. This is a rather good thing from our
point of view, since if all the matter that appeared survived, the uni-
verse would have been just too crowded to ever have developed into us.
Enough matter remained after the great annihilation to eventually make
a hundred billion galaxies each with an average of a hundred billion
stars all have been formed by the leftovers of the great annihilation. De-
struction can be very creative.

The surviving quarks did not exist in isolation; they always exist in
threesomes. Their relationship is structured by the strong force that is
mediated by the exchange of the charmingly named gluons. Two up
quarks and a down one form a positively charged proton; two downs
and an up form a neutron. Why is the strong force exactly as strong as it
is and not weaker or stronger? Is it different in other universes? It is
simply not known. But if it differed at all, we would not be here and
neither would anything else that we know of.

Quarks do not merge into one undifferentiated blob. Each proton and
neutron is constituted by two different types of quarks. They relate to
each other through the strong force, but they keep their distance as well. Relative to their own size, quarks have a rather pronounced need for personal space. Both relationship and distinct identity are part of Big Politics.

The protons and neutrons that were formed quickly after the Big Bang are with us still after almost 14 billion years. In fact, they are us, and everything else that we can see or feel. The structured relationships among individual quarks have been remarkably sustained. As inventive and creative as nature is, it also keeps certain things around for a long time. Something seems to have come from nothing at the Big Bang. That is change. Quarks can maintain their relationships for tens of billions of years. You cannot get much more of a status quo than that. We see in the epic of evolution the combination of long periods of stasis connected by periods of transition to greater levels of complex relationships. Both the status quo and periods of significant development are part of Big Politics.

About three hundred thousand years after the Big Bang, when the universe had expanded enough to cool sufficiently, the electromagnetic force mediated by the exchange of photons could structure a sustained relationship between protons and electrons. Atoms appeared. Hydrogen, with one proton and one electron, appeared in the greatest numbers. If you add up their mass, about three quarters of all atoms in the universe are still hydrogen. If you count atoms by number, they constitute about 90 per cent of all atoms. They also constitute 63 per cent of the number of atoms in your body (ten per cent by mass). As has been said, hydrogen is an odorless, colorless gas which, given enough time, turns into us (Harrison 1981).

Helium, with two protons and two electrons each, formed about a quarter of all atoms' mass that then existed (nine per cent by number). There was also a small amount of deuterium, or heavy hydrogen (one proton, one neutron, and an electron), helium isotopes, and lithium (three protons and electrons). Vast primal clouds of hydrogen and helium atoms, millions of light years across, still majestically float in certain areas of space nearly 14 billion years later. Some have gone on to form greater complexity; many have not.

Once formed, and left on their own, positively charged protons kept their distance from each other. While the strong force bound quarks together and protons and neutrons together within atoms, these atoms did not fuse. They might approach each other as they moved about, but usually swerved off, avoiding connections with each other.

We sometimes hear about an 'atomistic society'. For example, political philosopher Russell Kirk wrote that 'Individualism is social atomism; conservatism is community of spirit' (Kirk 1960). Social atomism refers to a rather asocial condition in which individuals have little to do
with each other. The analogy might be a billiard table, with hard billiard
balls usually sitting by themselves, but occasionally knocking into each
other, sending each other off in various directions. Atoms may be the
basic building blocks; in our experience, blocks usually just sit there by
themselves. We are each made of about 6.7*10²⁷ atoms. What are we then
like at our most constitutive level? Are we like the individuals discussed
by Hobbes in Leviathan? Do we live our lives largely isolated from oth-
ers? By nature, are we as asocial as the universe's vast majority of unaffili-
ated atoms? If we seek to form relationships, do we need to find ways to
overcome our natural proclivity for individualism? And since we are
built from atoms, is that what we are really like, all niceties aside?

But what if the story is one of emergent relationship as well as dis-
tinct identity? Recall that even the simplest of atoms – those that have
only one or two protons and are still the most abundant in the uni-
verse – are each a set of sustained, structured relationships. Quarks
which just moments before had not existed, started to be related
through the exchange of gluons mediating a strong force. Atoms, which
had not existed before the Big Bang plus 300,000 years, added a rela-
tionship between protons and electrons. Atoms are sets of sustained,
structured relationships. They are the simplest of polities. At our most
constitutive core, we are built more from relationships than from build-
ing blocks. Quarks and electrons are more fuzzy than blocky. Their
‘hardness’ comes from forces defining their relationships. What exists
between things is as real as the things themselves.

Stars

But what about positively charged protons naturally avoiding each oth-
er? Two hydrogen atoms (H₂) might combine on their own by sharing
electrons, but they do not fuse into helium as they float in enormous
clouds. Helium did not combine with anything. One and two proton
atoms by themselves would never on their own have led to us. To form
larger, more massive atoms, a new set of relationships was required.

When they did form, atoms were not perfectly distributed, if 'per-
fect' means absolute equality. They were slightly more densely distrib-
uted here, a little less there. This asymmetry, unequal distribution, or
imperfection was another very fortunate occurrence. Gravity has no
force at the relatively small distances between quarks. However, the
space between atoms can be just enough to let it start operating.
A clump of atoms here can exert gravitational attraction on a smaller
clump there. If all atoms had been equally distributed, their gravita-
tional attraction on each other would have canceled it all out, and they
would never have been drawn to each other. However, with the asym-
metry, the denser regions could start drawing in the slightly less dense-
ly packed atoms. Gravity kept pulling them together, increasing their
density and heat. As they were pulled closer together, they began to
spin faster like a figure skater drawing in her arms. Once sufficient den-
sity and heat developed, with atoms moving about more and more
quickly, the atoms overcame their preference to stay away from each
other. Hydrogen began fusing. They not only ran into each other, hy-
drogen nuclei could stick to each other, forming helium, with its two
protons and two neutrons, all held together by the strong force.

The newly joined atoms were less than the sum of their parts. Each
new helium atom weighed slightly less than the hydrogen atoms which
had combined to form it. The missing matter had turned into energy.
The fusion caused energy to burst out. Gravity kept trying to draw the
atoms in. The equilibrium between these two forces resulted in the for-
mation of stars.

As the helium was formed, gravity drew it in more, until it heated
up enough for it to start fusing into heavier elements, such as nitrogen.
This released energy and permitted gravity to draw the newly formed
elements further in, until they too began to fuse, forming carbon and
neon. This was repeated as oxygen, magnesium, silicon, and sulfur were
each fused. The largest stars with enough mass to permit gravity to
keep drawing the newly fused elements further in developed an onion
like structure, with the lighter elements on the periphery; the heavier
ones successively formed layers closer to the core. Not only can there be
new things under the stars, the stars themselves were something new.
The strong force, electromagnetism, gravity, and fusion formed rela-
tionships between atoms within the structure of a star.

Gravitational attraction between stars and dark matter formed gal-
axies or groupings of stars in distinct patterns. Galaxies formed rela-
tionships due to gravity in local groups and even larger patterns.
The theoretical work of Fr. Georges Lemaître, confirmed by the evi-
dence collected by Edwin Hubble, demonstrated that not only were
there more galaxies than our own Milky Way, but that once they got to
be further away from each other than those in the local group, they are
racing away from each other. It may be that dark energy or anti-gravity
is causing the galaxies to keep 'falling out' with space and the universe
expanding at ever faster speeds the further from each other they are.
In the long run, this may lead to the final disassociation of the universe
and the end of polity. The continued development of polity within
pockets of available energy is a medium-term possibility. In the long
run, we and the universe may both finally succumb to entropy.

When the largest of the stars began to make iron with its 26 protons,
energy was consumed rather than released. The equilibrium between
gravity and fusion was broken. Almost immediately, the star exploded in a supernova. The sudden increase in temperatures during the explosion permitted the almost instantaneous formation of all the elements with more than 26 protons per atom, all sent streaming into space at incredible speeds, often mixing with pre-existing clouds of hydrogen and helium that had been floating since the Big Bang.

## Molecules

Atoms form in such a way that electrons orbit protons in shells. The innermost shell is full with two electrons, the second with eight, the third with 18, the fourth with 32, the fifth with 50. Hydrogen, with its one electron, has a vacancy sign out in its only electron shell. That shell seems to want one more electron to form a full house. Oxygen, with its eight electrons, has two in its first shell and six in its second. This leaves two vacancies in its second shell. This is a match made in the heavens. If two hydrogen atoms hook up with an oxygen atom, each sharing their electrons, each hydrogen atom can have two electrons in its only shell and oxygen can have eight in its second shell. A new relationship between atoms is formed: H₂O – water. This molecule has a new property. At the right temperature, it has the property of wetness, which did not exist before. Water, which is abundant throughout space, is not the only molecule that forms. Dozens of molecules with 2, 3, 4, 5, or more atoms evolve naturally. Many atoms due to the way electron shells work lead to the formation of these new relationships called molecules.

Not all atoms are anxious to form molecules. Helium has two electrons in its only shell and has a No Vacancy sign well lit. It is called a noble gas. Having all they need, nobility does not require additional relationships with the lesser types that are needy. Relationship added to relationship is not much part of helium's story. While hydrogen becomes us, helium often just goes floating off into space. Not everything is social. Not everything forms polity, or sustained, ordered relationships. We saw that same aloofness with four of the six quarks. A subatomic particle formed in nuclear fusion, neutrinos, are much the same. Like photons, they go shooting from stars off into space, but almost never interact with anything. They can sail through twenty miles of lead and never hit anything. It has taken extraordinary measures to detect them at all. History and polity are not built on the backs of two thirds of quarks, neutrinos, helium, or other asocial phenomena. They are indeed the rugged individualists of the universe. The story of emergent complexity in our universe is not uniform and it may not be eternal.
Earth and the Emergence of Life

After a nearby supernova shot its star dust out into neighboring space,
disturbing pre-existing clouds of hydrogen and helium, gravity again
began pulling together the mixture of elements and molecules. A second
generation star with mostly hydrogen and helium but also with traces
of heavier elements in it – including oxygen, carbon, neon and iron –
eventually began shining as our Sun 4.6 billion years ago. It is not big
enough to permit gravity to create densities high enough to fuse ele-
ments heavier than helium. This is good for us, since huge stars live fast
and die young. Our Sun fuses 600 million tons of hydrogen each second,
turning it into 596 million tons of helium and more energy than man-
kind has ever produced in our species' entire history.

The Sun's rate of consuming its stock of hydrogen will permit it to
continue shining for a total of about, meaning it is at mid-life now. Its
4.6 billion year history has provided energy and the time for Earth to
develop. Although the Sun will likely increase its output of radiation
enough within two billion years to kill most or all life on Earth, it will be
five billion before it turns into a red giant, evaporates the oceans and
engulfs the Earth.

While gravity drew together 99.86 per cent of the total mass of the So-
lar System to make the Sun, the left over debris went to good use. On the
outskirts of the spinning disk that eventually ignited as the Sun, these
leftovers from part of the supernova started accreting through the
power of gravity. Chunks of iron, nickel, silicon, and bits or gold, silver,
uranium and other elements and molecules bumped into each other and
stuck together. All this knocking together that created kinetic energy, as
well as the radioactive decay of uranium and other such elements, made
for a molten, hot planet even on its surface. As its outer layer cooled,
Earth formed its own structure from thousands of molecules and the
minerals they produced. Heavier iron and nickel sunk into a dense core
that is still as hot as the surface of the sun. Silicon and other lighter ele-
ments rose to the top. Eventually, a thin layer made of the frothy basalt
and granite could cool enough to permit land to form. Lighter and
cooler outer layers spinning around denser iron and nickel produced
a magnetic shield around the planet that protected it from solar winds
that might otherwise blow away Earth's atmosphere.

The process of chemical evolution that had begun in space contin-
ued on Earth (Hazen 2005, 2012; Hoffmann 2012; Pross 2012). The most
common elements on the surface of the earth continued to combine in
many ways. Hydrogen, carbon, nitrogen, oxygen, sodium, magnesium,
phosphorus, sulfur, chlorine, potassium, calcium, iron, and other ele-
ments on Earth interacted to form over 4,700 minerals. Around black
smokers at the bottom of the oceans where tectonic plates separated and mineral rich heated waters bellowed up, or on sun soaked pools of water on rocky beaches, the process of chemical evolution continued. Lipids that created films formed, eventually forming membranes. Carbon, with its four electrons in its second orbit and a total of six overall, was able to combine with many other elements, and was central to the Krebs cycle which spins off amino acids. These molecules continued to combine until they integrated membranes, metabolism or access to energy, and RNA and DNA that permitted reproduction with variation in response to environmental changes. The Last Common Universal Ancestor – LUCA – was combined in the most complex relationship in universal history to date – that we know of. The first prokaryote cells were earthlings, formed of the commonly available chemicals and elements on earth. They were also children of the universe, with elements forged in stars that had died long before. We can look to the skies where one or more enormous stars exploded billions of years ago – and to the green scum covering the local pond – to see the equivalents of our ancestors. This might bring us a sense of both pride and humility. It also may elicit a sense of intimate relationship with all of nature.

## Biological Evolution

It has been said that the dream of every bacteria, the simplest of cells, is to become two bacteria. Reproduction has to be important for any species that plans on surviving, since the death of any given individual is part of the way life works. Sustained relationship is not eternal relationship. The nice thing about being a bacterium is that your dreams can come true about every twenty minutes. Reproduction with variation in response to environmental changes is a skill perfected by prokaryote cells. You just cannot argue with success. They live in virtually any setting, however extreme the condition on earth can be. From deep underground to thermal waters, prokaryotes are there. There are more bacterial cells in and on your body than there are cells that constitute your body. They help you digest food. And when you die, they will digest you. These types of cells have survived for almost four billion years. They will be on earth long after humans have vanished. Many prokaryote cells follow a plan that is not broken and does not need fixing, although they do keep adjusting to new conditions such as antibiotics. They evolve quickly, but as a group, they have not become fundamentally more complex.

However, after a couple billion years of happily reproducing at their same level of complexity, some did become more complex (Dawkins 2004, 2010; Lane 2009). About two billion years ago, eukaryote cells de-
veloped with a membrane covered kernel inside the cell in which more
complex DNA was kept. It also maintained a relationship with a mito-
chondrial cell rather than having digested it. This provided an ability to
burn carbohydrates and permits us to enjoy eating donuts.

A more complex set of relationships within the cell led to more
complex relationships among cells. Films of bacteria on the surface of
the ocean or accretions of them in rock like formations of stromatolites
in tidal pools were steps towards multicellular life forms. Another step
in multicellular cooperation came with creatures like the sponges. These
are formed by the same type of cells that could still specialize in serving
different functions. Some cells drew in nutrient rich water, others ex-
pelled nutrient drained water. Same type of cells; different tasks. Push
these cells through a sieve so that they are separated as they fall to the
bottom of a tank, and they scoot back together to form another new
sponge. These are cooperative cells, not hardy individualists.

Relationships among increasingly complex body structures formed
by different types of cells are seen in such examples as cnidarians, or
jelly fish, first seen about 800 million years ago. They have little harpoons
that can inject prey with poison, have such structures as a mouth / anus,
and have two layers of tissue. Their nervous system is pretty uniformly
spread out throughout the animal. Jelly fish are still around and doing
fine. They have existed 4,000 times longer than *Homo sapiens* have. They
see no reason to develop more complexity.

Still, there were additional mutations that worked out in the envi-
ronment of the time. Flatworms introduced a body plan about 590 mil-
lion years ago with a right and a left side, an up and down, and a front
and a back. Sense organs were put up front, along with ganglia of nerve
cells to interpret the incoming data. Chordates like the currently existing
hagfish put a cord along its back to protect the flow of information from
the ganglia to the rest of the body, as well as putting the mouth up front
and an anus in the rear. About 525 million years ago, vertebrates started
breaking that cord into bony segments, offering better protection and
definition. The first animals to venture out from the seas onto land, such
as Tiktalik, had wrists to help scoot on land and a neck to help look
around. About 360 million years ago, the first amniotes could recreate
the watery world in which reproduction had originally taken place, and
start producing eggs with a protective shell and watery interior. About
360 million years ago, mammals first appeared, which had, among other
things, a more complex auditory system with more parts that helped
them hear better. The story of evolution is in part a story of increasing
complexity of body structures, with more complex relationships among
greater numbers of parts.
It is worth recalling a few things. First, part of the reason for this
development was in response to the bitter competition between and
among species. An arms race of those seeking to eat others and those
seeking not to be eaten was good to select which individuals would
survive to reproduce the next generation. Increasingly complex rela-
tionship was spurred in part by sustained relationships that were harsh-
ly competition. Conflict, even deadly conflict, can spur greater complex-
ity. Secondly, there was no steady rise from simplicity to complexity.
Five major extinction periods between 450 mya and 65 mya caused huge
interruptions. This is only part of the reason why over 99 per cent of the
species that have ever existed are now extinct. We may be going
through a sixth (self-induced) extinction period that we hope does not
conclude with our own species' disappearance. However, virtually all
species, including the human one, have gone or will go extinct as the
evolution of life continues.

Relations among Animals and Plants

Relationships among quarks, protons and electrons, atoms, molecules,
cells, and body parts were followed by increasingly complex relations
among and between species. Edward O. Wilson's *The Social Conquest of
the Earth* offers a brilliant discussion of this phenomenon (Wilson 2012).
From quorum sensing of bacteria to schools of fish, bee hives, ant colo-
nies, flocks of birds, herds of bison, troops of chimpanzees, and many
other examples, animals often live in groups and groups often form eco-
systems.

Not all animals live in groups. Many seem to exist in splendid isola-
tion for most of their lives, coming together just long enough for repro-
duction without any care for offspring after birth. Mother guppies and
sharks would just as soon eat their babies. Sea turtles lay their eggs on
the beach, return to the sea, and likely do not think about them after
that. Crocodiles help their offspring out of their eggshell and out of the
nest; after that, the offspring are usually on their own. Childcare is, of
course, more of an issue for various lengths of time for many species.
From weeks of care to a couple years is common. Mothers, fathers, and
others are involved in different ways, depending on the species.

By the time we get to hominids, our ancestors' survival strategy and
increasing sociability went hand in hand (Tattersall 2012). *Australopith-
ecus* and its ancestors were likely more often the hunted than the hunt-
ers. They may have scavenged, eating bone marrow of leftover car-
casses, but gathering fruits, nuts, tubers, and leaves likely provided
a mainstay of their diet. Other than that, they tried to stay out of the
way of predators. They had few natural weapons. Their teeth were no
match for those of lions. Their speed was no match for cheetahs. They had no shells for defense or wings for flight. No wonder that there do not seem to have been huge numbers of hominids, that most species went extinct, and that our own ancestors came close to extinction (Sarmiento, Sawyer *et al.* 2007). They just did not have that much going for them.

Bipedalism, for whatever reason it was adopted, did permit more use of the arms, hands, and opposable thumbs. A parent could hold a child and pick fruit all at once. But it also altered the skeleton, restricting the birth canal, making child birth that much more dangerous. This became a greater problem once the hominids' greatest weapon did finally start to develop. Brain size from *Australopithecus* to Homo sapiens tripled, with Neanderthals winning the brain size competition. (Brain size for *Australopithecus* averaged between 375 and 550 cm³, *Homo habilis* from 500 to 800, *Homo erectus* 750 to 1225, *Homo sapiens* 1200–1750, and Neanderthals 900–1880.) It is not just brain size that is important, but how the structure of the brain develops and its size relative to body size. Hominids' enormous cerebral cortex permits the development of memory, attention, perceptual awareness, thought, language, and self-consciousness. With its development, polity emerges into politics. Hominids could not outfight competing species, but they could start to outthink them. Brains rather than brawn would eventually win the day.

But big brains come at a cost. Even with only partial brain development and soft skulls at birth, delivering children had become highly risky. To permit time for the brain to develop to maturity, grow a bony skull, and learn all that they required to survive, childhood for hominids took years. Breastfeeding and childcare-giving mothers developed close relations with offspring over long childhoods.

Child mortality was still likely high. For a handful of children to reach sexual maturity, birth would need to be given to a number more. For a species with relatively few members, the group had a strong interest in reproduction. Especially with life-spans in the 30s or so for adults who got through childhood, this meant that most or all of a female's adult life was involved with pregnancy and childcare. Working mothers were the norm. They likely provided the bulk of the calories through gathering and carried out many other important tasks. Still, they would have needed support as they did the primarily important work of getting children to adulthood so the species and the kinship group could survive. Long term relations between mothers and children and between child care-taking females and males were necessary for the large skulled hominids to survive.
It is one thing to get together briefly to copulate. That is all sharks need to do since childcare is not a problem. It is a wholly other set of problems to stay together for many years to raise children, a problem that hominids did have to figure out if they were to survive. Resolving the issues of food, shelter, and other necessities for a kinship group over years takes problem solving and relationships to a whole different level. The increased demands of a long childhood and the long term adult relations it required selected for an increased ability to figure out how to live together for many years at a time. The gender relations made necessary by being a big brained bipedal species is a root of hominid polity. Sexual politics has changed markedly recently with longer life spans and lower mortality rates. Mothers no longer spend their entire adult lives dealing with pregnancy and childcare, and have the time and energy to do much else.

As Michael Duffy, who writes within the Montessori tradition, notes that as we go through evolution,

> organisms produce fewer and fewer offspring and require longer and longer periods of care, leading to more important and deeper relationships. Fish produce thousands of eggs and rarely care for their young, reptiles produce hundreds of eggs and have only limited contact with their offspring, most mammals produce only a litter of a half dozen young and care for them for a long time through nursing, and humans have one or maybe two babies at a time and produce the most parent dependent creatures on Earth! (Duffy personal communication, May 13, 2013)

Many species have long developed their own ways of developing and maintaining relationships. Baboons groom each other, checking for parasites in the fur. Frans de Waal discusses how bonobos use sex for much the same purposes. Social primates, who were not genetically identical like ants within a colony are, developed a 'theory of mind'; they could understand each other's reactions. They could even sometimes 'feel for each other', or empathize. The law of the jungle, as de Waal argues, includes the social practices and understandings that would later be self-consciously developed into ethics (Waal 1989, 2005, 2007; Waal, Macedo et al. 2006).

Picking lice out of children's hair and having sexual relations has forever been part of hominid mothers' lives as well (Wade 2006). Hominids' survival strategy led to developed abilities to relate to each other. For their relations to develop, they would need to exchange a lot more than just gluons and photons. If you thought physics was hard to grasp, just try politics.