Geoff Mulgan - Big Mind_ How Collective Intelligence Can Change Our World-Princeton University Press - BIG MIND GEOFF MULGAN BIG MIND HOW COLLECTIVE

Geoff Mulgan - Big Mind_ How Collective Intelligence Can Change Our World-Princeton University Press

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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 41 William Street, Princeton, New Jersey 08540 In the United Kingdom: Princeton University Press 6 Oxford Street, Woodstock, Oxfordshire OX20 1TR press.princeton.edu Jacket design by Karl Spurzem Vectors courtesy of Shutterstock All Rights Reserved ISBN 978-0-691-17079-4 Library of Congress Control Number: 2017954117 British Library Cataloging-in-Publication Data is available This book has been composed in Adobe Garamond Pro Printed on acid-free paper. ∞ Printed in the United States of America 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 76 90 99 10 Self-Suspicion and Fighting the Enemies of Collective Intelligence 119 Part III 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 hypotheses. 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 morning. 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 predators. 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 them together. 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 solving t...
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