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Unformatted text preview: BIG MIND GEOFF MULGAN BIG
MIND HOW COLLECTIVE INTELLIGENCE
CAN CHANGE OUR WORLD Princeton University Press
Princeton and oxford Copyright © 2018 by Princeton University Press
Published by Princeton University Press
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1 3 5 7 9 10 8 6 4 2 CONTENTS Preface vii Introduction Collective Intelligence as a Grand Challenge 1 Part I
What Is Collective Intelligence? 9
1 The Paradox of a Smart World 2 The Nature of Collective Intelligence in Theory and Practice 11 Part II
Making Sense of Collective Intelligence as Choice 33
3 The Functional Elements of Collective Intelligence 4 The Infrastructures That Support Collective Intelligence 5 The Organizing Principles of Collective Intelligence 6 Learning Loops 7 Cognitive Economics and Triggered Hierarchies 8 The Autonomy of Intelligence 9 The Collective in Collective Intelligence 35
48 60 70
99 10 Self-Suspicion and Fighting the Enemies of Collective
Collective Intelligence in Everyday Life 129
11 Mind-Enhancing Meetings and Environments 131 12 Problem Solving: How Cities and Governments Think 145 14 vi • Contents 13 Visible and Invisible Hands: Economies and Firms as
Collective Intelligence 161
14 The University as Collective Intelligence 174 15 Democratic Assembly 181
16 How Does a Society Think and Create as a System? 193 17 The Rise of Knowledge Commons: It’s for Everyone 200 Part IV
Collective Intelligence as Expanded Possibility
18 Collective Wisdom and Progress in Consciousness
Afterword: The Past and Future of Collective
Intelligence as a Discipline 229
Summary of the Argument
Notes 239 Index 263 237 215 217 PREFACE This book has been several decades in the making. It grows out of both
experience and research. The experience has been the practical work of trying to help businesses, governments, and nongovernmental organizations
(NGOs) solve problems, use technologies, and act smarter. Alongside that,
much of my research and writing has essentially been about how thought
happens on a large scale. Communication and Control: Networks and the
New Economies of Communication (Blackwell, 1990) was about the nature
of the new networks being made possible by digital technologies and the
kinds of control they brought with them. It showed how networks could
both empower and disempower (and was intended as a corrective to the
hopes that networks would automatically usher in an era of greater democracy, equality, and freedom). Connexity: How to Live in a Connected World
(Harvard Business Press, 1997) was a more philosophical essay about the
morality of a connected world, and the types of people and character that
would be needed in a networked environment. Good and Bad Power: The
Ideals and Betrayals of Government (Penguin, 2005) and The Art of Public
Strategy: Mobilizing Power and Knowledge for the Common Good (Oxford
University Press, 2009) were about how the state could use its unique
powers to the greatest good, including mobilizing and working with the
brainpower of citizens. The Locust and the Bee: Predators and Creators in
Capitalism’s Future (Princeton University Press, 2013) set out a new agenda
for economics, suggesting how economies could expand collective intelligence and creative potential while reining in predatory tendencies.
What follows here builds on each of these, weaving them into what I
hope is both a convincing theory and useful guide.
The ideas draw on my previous work, but have also benefited greatly
from many conversations, readings, and arguments. I owe a significant
debt to my colleagues at Nesta, particularly Stefana Broadbent, Tom Saunders, John Loder, Francesca Bria, Stian Westlake, and Zosia Poulter (for viii • Preface the diagrams). My colleagues at Harvard’s Ash Center were generous in
terms of their time and engagement, especially Mark Moore, who provided extensive and useful comments, and Jorrit de Jong. I am particularly
grateful to the Ash Center for having given me the chance to be a senior
visiting scholar over three years, from 2015 to 2018, to try out some of the
ideas explored in this book. Also at Harvard, Roberto Mangabeira Unger
and Howard Gardner once again offered invaluable stimulus.
In addition, I want to extend my appreciation for the intellectual collaborators in and around GovLab at New York University, especially Beth
Noveck and Stefan Verhuist. Marta Struminska from Warsaw provided
useful early comments, as did Rushanara Ali, Lynne Parsons, Robin Murray, Soh Yeong Roh, Gavin Starks, Sarah Savant, Vaughn Tan, and Francois
Taddei. Colin Blakemore and Mattia Gallotti organized a fascinating conference at Nesta on collective intelligence in 2015. Mattia went on to offer
helpful and detailed comments on a draft, for which I’m hugely grateful.
I’ve also benefited greatly from support and insights from others in and
around this field, including Karim Lakhani at Harvard, Tom Malone at
the Massachusetts Institute of Technology, and Joshua Ramo. I owe thanks
as well to Julia Hobsbawm for giving me the opportunity to test out some
of the arguments on a group that included the historian Simon Schama
and journalist David Aaronovich, to Luciano Floridi for letting me air
some of the ideas in the journal Philosophy and Technology, and Jens Wandel and Gina Lucarelli at the UN Development Program for the chance to
put them into practice in the field of development. BIG MIND Introduction
Collective Intelligence as a Grand Challenge There are libraries fUll of books on individual intelligence, investigating where it comes from, how it manifests, and whether it’s one thing or
many. Over many years, I’ve been interested in a less studied field. Working in governments and charities, businesses and movements, I’ve been fascinated by the question of why some organizations seem so much smarter
than others—better able to navigate the uncertain currents of the world
around them. Even more fascinating are the examples of organizations full
of clever people and expensive technology that nevertheless act in stupid
and self-destructive ways.
I looked around for the theories and studies that would make sense of
this, but found little available.1 And so I observed, assessed, and drew up
I was helped in this study by having been trained in things digital, completing a PhD in telecommunications. Digital technologies can sometimes
dumb people down. But they have the virtue of making thought processes
visible. Someone has to program how software will process information,
sensors will gather data, or memories will be stored. All of us living in a
more pervasively digital age, and those of us who have to think digitally
for our work, are inevitably more sensitive to how intelligence is organized,
where perhaps in another era we might have thought it a fact of nature,
magical, and mysterious.
The field that led me to has sometimes been given the label collective intelligence. In its narrow variants, it’s mainly concerned with how
groups of people collaborate together online. In its broader variants
it’s about how all kinds of intelligence happen on large scales. At its
extreme, it encompasses the whole of human civilization and culture,
which constitutes the collective intelligence of our species, passed down 2 • Introduction imperfectly through books and schools, lectures and demonstrations, or
by parents showing children how to sit still, eat, or get dressed in the
My interest is less ambitious than this. I’m concerned with the space between the individual and the totality of civilization—an equivalent to the
space in biology between individual organisms and the whole biosphere.
Just as it makes sense to study particular ecologies—lakes, deserts, and
forests—so it also makes sense to study the systems of intelligence that
operate at this middle level, in individual organizations, sectors, or fields.
Within this space, my primary interest is narrower still: How do societies, governments, or governing systems solve complex problems, or to put
it another way, how do collective problems find collective solutions?
Individual neurons only become useful when they’re connected to billions of other neurons. In a similar way, the linking up of people and machines makes possible dramatic jumps in collective intelligence. When this
happens, the whole can be much more than the sum of its parts.
Our challenge is to understand how to do this well; how to avoid
drowning in a sea of data or being deafened by the noise of too much irrelevant information; how to use technologies to amplify our minds rather
than constrain them in predictable ruts.
What follows in this book is a combination of description and theory
that aims to guide design and action. Its central claim is that every individual, organization, or group could thrive more successfully if it tapped
into a bigger mind—drawing on the brainpower of other people and machines. There are already some three billion people connected online and
over five billion connected machines.2 But making the most of them requires careful attention to methods, avoidance of traps, and investment
of scarce resources.3 As is the case with the links between neurons in our
brain, successful thought depends on structure and organization, not just
the number of connections or signals.
This may be more obvious in the near future. Children growing up
in the twenty-first century take it for granted that they are surrounded
by sensors and social media, and their participation in overlapping group
minds—hives, crowds, and clubs—makes the idea that intelligence resides
primarily in the space inside the human skull into an odd anachronism.
Some feel comfortable living far more open and transparent lives than
their parents, much more part of the crowd than apart. A Grand Challenge • 3 The great risk in their lifetimes, though, is that collective intelligence
won’t keep up with artificial intelligence. As a result, they may live in a future where extraordinarily smart artificial intelligence sits amid often-inept
systems for making the decisions that matter most.
To avoid that fate we need clear thinking. For example, it was once
assumed that crowds were by their nature dangerous, deluded, and cruel.
More recently the pendulum swung to an opposite assumption: that
crowds tend to be wise. The truth is subtler. There are now innumerable
examples that show the gains from mobilizing more people to take part in
observation, analysis, and problem solving. But crowds, whether online or
off-line, can also be foolish and biased, or overconfident echo chambers.
Within any group, diverging and conflicting interests make any kind of
collective intelligence both a tool for cooperation and a site for competition, deception, and manipulation.
Taking advantage of the possibilities of a bigger mind can also bring
stark vulnerabilities for us as individuals. We may, and often will, find our
skills and knowledge quickly superseded by intelligent machines. If our
data and lives become visible, we can more easily be exploited by powerful
For institutions, the rising importance of conscious collective intelligence is no less challenging, and demands a different view of boundaries and roles. Every organization needs to become more aware of how it
observes, analyses, remembers, and creates, and then how it learns from
action: correcting errors, sometimes creating new categories when the old
ones don’t work, and sometimes developing entirely new ways of thinking.
Every organization has to find the right position between the silence and
the noise: the silence of the old hierarchies in which no one dared to challenge or warn, and the noisy cacophony of a world of networks flooded
by an infinity of voices. That space in between becomes meaningful only
when organizations learn how to select and cluster with the right levels of
granularity—simple enough but not simplistic; clear but not crude; focused but not to the extent of myopia. Few of our dominant institutions
are adept at thinking in these ways. Businesses have the biggest incentives
to act more intelligently, and invest heavily in hardware and software of
all kinds. But whole sectors repeatedly make big mistakes, misread their
environments, and harvest only a fraction of the know-how that’s available
in their employees and customers. Many can be extremely smart within 4 • Introduction narrow parameters, but far less so when it comes to the bigger picture.
Again and again, we find that big data without a big mind (and sometimes
a big heart) can amplify errors of diagnosis and prescription.
Democratic institutions, where we, together, make some of our most
important decisions, have proven even less capable of learning how to learn.
Instead, most are frozen in forms and structures that made sense a century
or two ago, but are now anachronisms. A few parliaments and cities are
trying to harness the collective intelligence of their citizens. But many democratic institutions—parliaments, congresses, and parties—look dumber
than the societies they serve. All too often the enemies of collective intelligence are able to capture public discourse, spread misinformation, and fill
debates with distractions rather than facts.
So how can people think together in groups? How might they think and
act more successfully? How might the flood of new technologies available
to help with thinking—technologies for watching, counting, matching,
and predicting—help us together solve our most compelling problems?
In this book, I describe the emerging theory and practice that points to
different ways of seeing the world and acting in it. Drawing on insights
from many disciplines, I share concepts with which we can make sense
of how groups think, ideas that may help to predict why some thrive and
others falter, and pointers as to how a firm, social movement, or government might think more successfully, combining the best of technologies
with the best of the gray matter at its disposal.
I sketch out what in time could become a full-fledged discipline of collective intelligence, providing insights into how economies work, how democracies can be reformed, or the difference between exhilarating and depressing meetings. Hannah Arendt once commented that a stray dog has
a better chance of surviving if it’s given a name, and in a similar way this
field may better thrive if we use the name collective intelligence to bring
together many diverse ideas and practices.
The field needs to be both open and empirical. Just as cognitive science has drawn on many sources—from linguistics to neuroscience, psychology to anthropology—to understand how people think, so will a new
discipline concerned with thought on larger scales need to draw on many
disciplines, from social psychology to computer science, economics to
sociology, and use these to guide practical experiments. Then, as the new
discipline emerges—and is hopefully helped by neighboring disciplines A Grand Challenge • 5 rather than attacked for challenging their boundaries—it will need to be
closely tied into practice: supporting, guiding, and learning from a community of practitioners working to design as well as operate tools that help
systems think and act more successfully.
Collective intelligence isn’t inherently new, and throughout the book
I draw on the insights and successes of the past, from the nineteenthcentury designers of the Oxford English Dictionary (OED) to the Cybersyn project in Chile, from Isaac Newton’s Principia Mathematica to the
National Aeronautics and Space Administration (NASA), from Taiwanese
democracy to Finnish universities, and from Kenyan web platforms to the
dynamics of football teams.
In our own brains, the ability to link observation, analysis, creativity,
memory, judgment, and wisdom makes the whole much more than the
sum of its parts. In a similar way, I argue that assemblies that bring together many elements will be vital if the world is to navigate some of its
biggest challenges, from health and climate change to migration. Their
role will be to orchestrate knowledge and also apply much more systematic methods to knowledge about that knowledge—including metadata,
verification tools, and tags, and careful attention to how knowledge is used
in practice. Such assemblies are multiplicative rather than additive: their
value comes from how the elements are connected together. Unfortunately
they remain rare and often fragile.
To get at the right answers, we’ll have to reject appealing conventional
wisdoms. One is the idea that a more networked world automatically becomes more intelligent through processes of organic self-organization. Although this view contains important grains of truth, it has been deeply
misleading.4 Just as the apparently free Internet rests on energy-hungry
server farms, so does collective intelligence depend on the commitment of
scarce resources. Collective intelligence can be light, emergent, and serendipitous. But it more often has to be consciously orchestrated, supported
by specialist institutions and roles, and helped by common standards. In
many fields no one sees it as their role to make this happen, as a result of
which the world acts far less intelligently than it could.
The biggest potential rewards lie at a global level. We have truly global
Internet and social media. But we are a long way short of a truly global collective intelligence suitable for solving global problems—from pandemics
to climate threats, violence to poverty. There’s no shortage of interesting 6 • Introduction pilots and projects. Yet we sorely lack more concerted support and action
to assemble new combinations of tools that can help the world think and
act at a pace as well as scale commensurate with the problems we face.
Instead, in far too many fields the most important data and knowledge
are flawed and fragmented, lacking the organization that’s needed to make
them easy to access and use, and no one has the means or capacity to bring
Perhaps the biggest problem is that highly competitive fields—the military, finance, and to a lesser extent marketing or electoral politics—account
for the majority of investment in tools for large-scale intelligence. Their
influence has shaped the technologies themselves. Spotting small variances
is critical if your main concern is defense or to find comparative advantage
in financial markets. So technologies have advanced much further to see,
sense, map, and match than to understand. The linear processing logic of
the Turing machine is much better at manipulating inputs than it is at creating strong models that can use the inputs and create meanings. In other
words, digital technologies have developed to be good at answers and bad
at questions, good at serial logic and poor at parallel logic, and good at
large-scale processing and bad at spotting nonobvious patterns.
Fields that are less competitive but potentially offer much greater
gains to society—such as physical and mental health, environment, and
community—have tended to miss out, and have had much less influence
on the direction of technological change.5 The net result is a massive misallocation of brainpower, summed up in the lament of Jeff Hammerbacher,
the former head of data at Facebook, that “the best minds of my generation are thinking about how to make people click ads.”
The stakes could not be higher. Progressing collective intelligence is in
many ways humanity’s grandest challenge since there’s little prospect of
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- Artificial Intelligence, Central Intelligence Agency, Intelligent agent