Category Archives: Collective Intelligence

Big Mind. How Collective Intelligence Can Change Our World – Geoff Mulgan. 

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.

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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. 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 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. But making the most of them requires careful attention to methods, avoidance of traps, and investment of scarce resources. 
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. 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 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 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 nineteenth-century 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. 

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 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. 

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 the other grand challenges of climate, health, prosperity, or war without progress in how we think and act together. 

We cannot easily imagine the mind of the future. The past offers clues, though. Evolutionary biology shows that the major transitions in life—from chromosomes to multicellular organisms, prokaryotic to eukaryotic cells, plants to animals, and simple to sexual reproduction—all had a common pattern. Each transition led to a new form of cooperation and interdependence so that organisms that before the transition could replicate independently, afterward could only replicate as “part of a larger whole.”
Each shift also brought with it new ways of both storing and transmitting information. 
It now seems inevitable that our lives will be more interwoven with intelligent machinery that will shape, challenge, supplant, and amplify us, frequently at the same time. The question we should be asking is not whether this will happen but rather how we can shape these tools so that they shape us well—enhancing us in every sense of the word and making us more of what we most admire in ourselves. 

We may not be able to avoid a world of virtual reality pornography, ultrasmart missiles, and spies. But we can create a better version of collective intelligence alongside these—a world where, in tandem with machines, we become wiser, more aware, and better able to thrive and survive. 

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THE STRUCTURE OF THE BOOK 

The rest of this book is divided into four main sections. 

The first section (chapters 1 and 2) maps out the issue and explains what collective intelligence is. I offer illustrations of collective intelligence in practice, outline ways of thinking about it, and describe some of the most interesting contemporary examples. 

The next section focuses on how to make sense of collective intelligence (chapters 3 to 10). It provides a theoretical framework that describes the functional elements of intelligence and how they are brought together, how collectives are formed, and how intelligence struggles with its enemies. 

Chapters 11 to 17 then look at collective intelligence in the wild along with the implications of the theories for specific fields: the organization of meetings and places, business and the economy, democracy, the university, social change, and the new digital commons. In each case, I show how thinking about collective intelligence can unlock new perspectives and solutions. 

Finally, in chapter 18, I pull the themes together and address the politics of collective intelligence, demonstrating what progress toward greater collective wisdom might look like.

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PART I 

What Is Collective Intelligence? 

IN THIS FIRST SECTION, I explain what collective intelligence means in practice and how we can recognize it in the world around us, helping us to plan a journey, diagnose an illness, or track down an old friend. 

It’s an odd paradox that ever more intelligent machines can be found at work within systems that behave foolishly. Despite the unevenness of results, however, there are many promising initiatives to support intelligence on a large scale that have drawn on a cascade of advances in computing, from web science to machine learning. These range from household names like Google Maps and Wikipedia to more obscure experiments in math and chess. 
Connecting large numbers of machines and people makes it possible for them to think in radically new ways—solving complex problems, spotting issues faster, and combining resources in new ways.
How to do this well is rarely straightforward, and crowds aren’t automatically wise. But we are beginning to see subtler forms of what I call assemblies emerge. These bring together many elements of collective intelligence into a single system. They show how the world could think on a truly global scale, tracking such things as outbreaks of disease or the state of the world’s environments, and feeding back into action. For example, an observatory that spots global outbreaks of Zika can predict how the virus might spread and guide public health services to direct their resources to contain any outbreaks. 

Within cities, combining large data sets can make it easier to spot which buildings are at most risk of fires or which hospital patients are most at risk of becoming sick, so that government can be more adept at predicting and preventing rather than curing and fixing. These ways of organizing thought on a large scale are still in their infancy. They lack a convincing guiding theory and professional experts who know the tricks of the trade. In many cases, they lack a reliable economic base. Yet they suggest how in the future, almost every field of human activity could become better at harnessing information and learning fast.

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The Paradox of a Smart World 

WE LIVE SURROUNDED BY NEW WAYS of thinking, understanding, and measuring that simultaneously point to a new step in human evolution and an evolution beyond humans. Some of the new ways of thinking involve data—mapping, matching, and searching for patterns far beyond the capacity of the human eye or ear. Some involve analysis—supercomputers able to model the weather, play chess, or diagnose diseases (for example, using the technologies of firms like Google’s DeepMind or IBM’s Watson). Some pull us ever further into what the novelist William Gibson described as the “consensual hallucination” of cyberspace. 

These all show promise. But there is a striking imbalance between the smartness of the tools we have around us and the more limited smartness of the results. The Internet, World Wide Web, and Internet of things are major steps forward in the orchestration of information and knowledge. Yet it doesn’t often feel as if the world is all that clever. Technologies can dumb down as well as smarten up. 

Many institutions and systems act much more stupidly than the people within them, including many that have access to the most sophisticated technologies. 
Martin Luther King Jr. spoke of “guided missiles but misguided men,” and institutions packed with individual intelligence can often display collective stupidity or the distorted worldview of “idiots savants” in machine form. New technologies bring with them new catastrophes partly because they so frequently outstrip our wisdom (no one has found a way to create code without also creating bugs, and as the French philosopher Paul Virilio put it, the aircraft inevitably produces the air disaster). 

In the 1980s, the economist Robert Solow commented, “You can see the computer age everywhere but in the productivity statistics.” Today we might say again that data and intelligence are everywhere—except in the productivity statistics, and in many of the things that matter most. The financial crash of the late 2000s was a particularly striking example. 

Financial institutions that had spent vast sums on information technologies failed to understand what was happening to them, or understood the data but not what lay behind the data, and so brought the world to the brink of economic disaster. 

In the 1960s and 1970s, the Soviet government had at its disposal brilliant minds and computers, but couldn’t think its way out of stagnation. During the same period, the US military had more computing power at its disposition than any other organization in history, but failed to understand the true dynamics of the war it was fighting in Vietnam. A generation later the same happened in Iraq, when a war was fought based on a profound error of intelligence launched by the US and UK governments with more invested than any other countries in the most advanced intelligence tools imaginable. 

Many other examples confirm that having smart tools does not automatically lead to more intelligent results. 

Health is perhaps the most striking example of the paradoxical combination of smart elements and often-stupid results. We now benefit from vastly more access to information on diseases, diagnoses, and treatments on the Internet. There are global databases of which treatments work; detailed guidance for doctors on symptoms, diagnoses, and prescriptions; and colossal funds devoted to pushing the frontiers of cancer, surgery, or pharmaceuticals. But this is far from a golden age of healthy activity or intelligence about health. The information available through networks is frequently misleading (according to some research, more so than face-to-face advice).  

There are well over 150,000 health apps, yet only a tiny fraction can point to any evidence that they improve their users’ health. The dominant media propagate half-truths and sometimes even lies as well as useful truths. And millions of people make choices every day that clearly threaten their own health. 

The world’s health systems are in many ways pioneers of collective intelligence, as I will show later, but much doesn’t work well. It’s estimated that some 30 to 50 percent of antibiotic prescriptions are unnecessary, 25 percent of medicines in circulation are counterfeit, somewhere between 10 and 20 percent of diagnoses are incorrect, and each year 250,000 die in the United States alone because of medical error (the third leading cause of death there). 

In short, the world has made great strides in improving health and has accumulated an extraordinary amount of knowledge about it, yet still has a long way to go in orchestrating that knowledge to best effect. 

Similar patterns can be found in many fields, from politics and business to personal life: unprecedented access to data, information, and opinions, but less obvious progress in using this information to guide better decisions. 

We benefit from a cornucopia of goods unimaginable to past generations, yet still too often spend money we haven’t earned to buy things we don’t need to impress people we don’t like. 

We have extraordinary intelligence in pockets, for specific, defined tasks. Yet there has been glacial, if any, progress in handling more complex, interconnected problems, and paradoxically the excitement surrounding new capacities to sense, process, or analyze may distract attention from the more fundamental challenges. 

In later chapters, I address what true collective intelligence would look like in some of the most important fields. How could democracy be organized differently if it wanted to make the most of the ideas, expertise, and needs of citizens? Various experiments around the world suggest what the answers might be, but they baffle most of the professionals brought up in traditional politics. 

How could universities become better at creating, orchestrating, and sharing knowledge of all kinds? There are seeds of different approaches to be found, but also extraordinary inertia in the traditional models of three-year degrees, faculty hierarchies, lecture halls, and course notes. 

Or again, how could a city administration, or national government, think more successfully about solving problems like traffic congestion, housing shortages, or crime, amplifying the capabilities of its people rather than dumbing them down? 
We can sketch plausible and achievable options that would greatly improve these institutions. In every case, however, the current reality falls far short of what’s possible, and sometimes tools that could amplify intelligence turn out to have the opposite effect. 

Marcel Proust wrote that “nine tenths of the ills from which intelligent people suffer spring from their intellect.” The same may be true of collective intelligence. 

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The Nature of Collective Intelligence in Theory and Practice 

THE WORD INTELLIGENCE HAS A COMPLEX HISTORY. 

In medieval times, the intellect was understood as an aspect of our souls, with each individual intellect linked into the divine intellect of the cosmos and God. 

Since then, understandings of intelligence have reflected the dominant technologies of the era. René Descartes used hydraulics as a metaphor for the brain and believed that animating fluids connected the brain to limbs. Sigmund Freud in the age of steam power saw the mind in terms of pressure and release. The age of radio and electrics gave us the metaphors of “crossed wires” and being “on the same wavelength,” while in the age of computers the metaphors turned to processing and algorithmic thinking, and the brain as computer. 

There are many definitions of intelligence. But the roots of the word point in a direction that is rather different from these metaphors. Intelligence derives from the Latin word inter, meaning “between,” combined with the word legere, meaning “choose.” This makes intelligence not just a matter of extraordinary memory or processing speeds. Instead it refers to our ability to use our brains to know which path to take, who to trust, and what to do or not do. It comes close in this sense to what we mean by freedom. 

The phrase collective intelligence links this with a related idea. The word collective derives from colligere. This joins col, “together,” and once again, legere, “choose.” The collective is who we choose to be with, who we trust to share our lives with. 

So collective intelligence is in two senses a concept about choice: who we choose to be with and how we choose to act. The phrase has been used in recent years primarily to refer to groups that combine together online. But it should more logically be used to describe any kind of large-scale intelligence that involves collectives choosing to be, think, and act together. 

That makes it an ethical as well as technical term, which also ties into our sense of conscience—a term that is now usually understood as individual, but is rooted in the combination of con (with) and scire (to know). 


POSSIBILITY
 

We choose in a landscape of possibilities and probabilities. In every aspect of our lives we look out into a future of possible events, which we can guess or estimate, though never know for certain. Many of the tools I describe through the course of this book help us make sense of what lies ahead, predicting, adapting, and responding. We observe, analyze, model, remember, and try to learn. Although mistakes are unavoidable, repeated mistakes are unnecessary. But we also learn that in every situation, there are possibilities far beyond what data or knowledge can tell us—possibilities that thanks to imaginative intelligence, we can sometimes glimpse. 


GROUPS
 

One of the first historical accounts of collective intelligence is Thucydides’s description of how an army went about planning the assault on a besieged town. “They first made ladders equal in length to the height of the enemy’s wall, which they calculated by the help of the layers of bricks on the side facing the town, at a place where the wall had accidentally not been plastered. A great many counted at once, and, although some might make mistakes, the calculation would be more often right than wrong; for they repeated the process again and again, and, the distance not being great, they could see the wall distinctly enough for their purpose. In this manner they ascertained the proper length of the ladders, taking as a measure the thickness of the bricks.”

Understanding how we work together—the collective part of collective intelligence—has been a central concern of social science for several centuries. Some mechanisms allow individual choices to be aggregated in a socially useful way without requiring any conscious collaboration or shared identity. This is the logic of the invisible hand of the market and some of the recent experiments with digital collective intelligence like Wikipedia. 

In other cases (such as communes, friends on vacation, or work teams), there is the conscious mutual coordination of people with relatively equal power, which usually involves a lot of conversation and negotiation. Loosely networked organizations such as Alcoholics Anonymous are similar in nature. In others (for instance, big corporations like Google or Samsung, ancient Greek armies, or modern global NGOs), hierarchy organizes cooperation, with a division of labor between different tiers of decision making. Each of these produces particular kinds of collective intelligence. Each feels radically different, and works well for some tasks and not others. 

In some cases there is a central blueprint, command center, or plan—someone who can see how the pieces fit together and may end up as a new building, a business plan, or initiative. In other cases the intelligence is wholly distributed and no one can see the big picture in advance. But in most cases the individual doesn’t need to know much about the system they’re part of: they can be competent without comprehension. 

The detailed study of how groups work shows that we’re bound together not just by interests and habit but also by meanings and stories. But the very properties that help a group cohere can also impede intelligence. These include shared assumptions that don’t hold true, a shared willingness to ignore uncomfortable facts, groupthink, group feel, and mutual affirmation rather than criticism. Shared thought includes not only knowledge but also delusions, illusions, fantasies, the hunger for confirmation of what we already believe, and the distorting pull of power that bends facts and frames to serve itself. 

The Central Intelligence Agency informing President George H. W. Bush that the Berlin Wall wouldn’t fall, just as the news was showing it doing just that; investment banks in the late 2000s piling into subprime mortgages when all the indicators showed that they were worthless; Joseph Stalin and his team ignoring the nearly ninety separate, credible intelligence warnings that Germany was about to invade in 1941—all are examples of how easily organizations can be trapped by their frames of thinking. 

We succumb all too readily to illusions of control and optimism bias, and when in a crowd can suspend our sense of moral responsibility or choose riskier options we would never go for alone. And we like to have our judgments confirmed, behaving all too often like the Texas sharpshooter who sprays the walls with bullets and then draws the target around where they hit. 

These are just a few reasons why collective intelligence is so frequently more like collective stupidity. They show why most groups face a trade-off between how collective they are and how intelligently they can behave. The more they bond, the less they see the world as it really is. Yet the most successful organizations and teams learn how to combine the two—with sufficient suspension of ego and sufficient trust to combine rigorous honesty with mutual commitment. 


GENERAL AND SPECIFIC 

How we think can then be imagined as running in a continuum from general, abstract intelligence to intelligence that is relevant to specific places, people, and times. 

At one extreme there are the general laws of physics or the somewhat less general laws of biology. There are abstracted data, standardized algorithms, and mass-produced products. Much of modernity has been built on an explosion of this kind of context-free intelligence. 

At the other end of the spectrum there is rooted intelligence—intelligence that understands the nuances of particular people, cultures, histories, or meanings, and loses salience when it’s removed from them. 

The first kinds of intelligence—abstract, standardized, and even universal—are well suited to computers, global markets, and forms of collective intelligence that are more about aggregation than integration. By contrast, the ones at the other extreme—like knowing how to change someone’s life or regenerate a town—entangle multiple dimensions, and require much more conscious iteration and integration along with sensitivity to context. 

COLLECTIVE INTELLIGENCE AND CONFLICT 

The simplest way to judge individual intelligence is by how well it achieves goals and generates new ones. But this is bound to be more complex for any large group, which is likely to have many different goals and often-conflicting interests. This is obviously true in the economy, since information is usually hoarded and traded rather than shared  .  .  .
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from

Big Mind. How Collective Intelligence Can Change Our World

by

Geoff Mulgan

get it at Amazon.com