Smart Swarm: Using Animal Behaviour to Organise Our World

Smart Swarm

Peter Miller

Foreword by Don Tapscott, author of Wikinomics

For my wife, Priscilla, and my parents, Mary Lou and Bob

Table of Contents

Cover Page

Title Page

FOREWORD BY DON TAPSCOTT

INTRODUCTION WHEN IN DOUBT, TURN TO THE EXPERTS

1 ANTS Who’s in Charge Here?

2 HONEYBEES Making Smart Decisions

3 TERMITES One Thing Leads to Another

4 BIRDS OF A FEATHER Secrets of Flocks, Schools, and Herds

5 LOCUSTS The Dark Sides of Crowds

CONCLUSION Doing the Right Thing

NOTES

INDEX

ACKNOWLEDGMENTS

Copyright

About the Publisher

FOREWORD BY DON TAPSCOTT

Many people have studied how crowds, mass collaboration, business ecosystems, and networks transform the way organizations can do things better. I, for one, am convinced that we’re in the early days of the biggest change to the deep structures, architecture, and modus operandi of the century. But it often feels a lot more like an art than a science.

It turns out that nature can help us with the science.

When it comes to organizing ourselves in society, we often default to traditional hierarchies. This model has worked well as a way of systematizing work, establishing authority, deploying resources, allocating tasks, defining relationships, and enabling organizations to operate. Whether the ancient slave empires of Greece, Rome, China, and the Americas; the feudal kingdoms that later covered the planet; the corporations of industrial capitalism; or the bureaucracies of Soviet-style communism, hierarchies have been with us since the dawn of human history. Even the management literature today that advocates empowerment, teams, and networking takes the command-and-control method as a premise: Every person in an organization is subordinate to someone else. Hierarchies also define the relationships among companies. Every company is positioned in a supply chain whose subordinate companies it controls, and it is in turn beholden to the clients or customers it serves. In the old model of economic development, worker bees are to be supervised in their honey production.

The basic concept is here to stay, but traditional hierarchies have increasing limitations. More than twenty years ago, Peter Drucker described managers as “relays—human boosters for the faint, unfocused signals that pass for information in the traditional, pre-information organization.” Communication from the bottom up is often limited, except through formal labor-management relations. Hierarchies are typically bureaucratic, and employees lack motivation. Increasingly, they are insufficient as a way of organizing for the fast-paced economy where human capital needs to be unleashed for innovation, value creation, and customer relationships.

Then along comes the Internet, a communication medium that radically drops transaction and collaboration costs. This changes two very fundamental things about the protocol of enterprises. First, there are alternatives for organizing the internal workings of companies and other organizations. As Peter Miller describes, companies like Best Buy can harness the wisdom of many with techniques such as prediction markets to operate more effectively, and in doing so they challenge basic tenets of hierarchical control. Peers can collaborate across organizational silos. We can rethink power, now achievable through people rather than over people. Work can be organized on new project models, where the genius of human capital can be unleashed from its old command-and-control constraints. Employees can forge their own self-organized interconnections and form cross-functional teams capable of interacting as a global, real-time work force. Loosening organizational hierarchies and giving more power to employees can lead to faster innovation, lower cost structures, greater agility, improved responsiveness to customers, and more authenticity and respect in the marketplace.

Second, the boundaries of firms can become more porous, enabling powerful new approaches for orchestrating ability to innovate, to create goods and services, and even to produce public value. Rather than hierarchical supply chains, firms can build peer-to-peer networks where the roles, motivations, and behavior of the players are different—with dramatically better results.

What’s missing is a better science to all this, which is where Smart Swarm comes in. What could we learn from the dynamic, complex “collaborations” that exist in nature itself? What can nature tell us to help us bring complexity theory down to earth?

In the past, I and others have compared the networked organizations springing up to a skein of geese forming a V—acting in unison but without centralized control. Some years ago, Thomas Stewart, former editor of the Harvard Business Review, explained that the motion of the group is the aggregate result of the actions of each individual animal acting on the basis of its local perception of the world. There is no one leader. The bird at the front of the V has to work hardest because of wind resistance. But when it gets tired, another bird takes the leadership position. The birds have a collaborative leadership of sorts.

With the publication of Smart Swarm, for the first time, the lessons of flocks, schools, and colonies have been brought together in a readable text about how to get things done better. In a sense it’s a step toward creating a science of collaboration.

And where is all this going? Is it possible that as everyone connects through the global digital platform, we can begin to share not only information but also our ability to remember, process information, and even think? Is this just a fanciful analogy, or will we come to consider networking as the neural routes that are growing to connect human capital and transforming, again, quality (of connections) into quantity (something fundamentally new)?

You’ll enjoy this book, and not only for its speculation about the future. Rather, it’s full of practical guidelines about what nature can tell us about how to build better organizations today. How does your company embrace self-organization, diversity, knowledge, individual collaboration, and adaptive mimicking to outdo your competitors or deliver better value to society. And how can you avoid the dark side of smart swarming?

Read on.

Don Tapscott is the author of fourteen books about new technology in business and society, most recently (with Anthony D. Williams) Macro Wikinomics: Rebooting Business and the World (September 2010).

INTRODUCTION WHEN IN DOUBT, TURN TO THE EXPERTS

Not long ago Southwest Airlines was wrestling with a difficult question: Should it abandon its long-standing policy of open seating on planes? Of all the major airlines, Southwest was the only one that let passengers choose where to sit once they got on board. The airline had done it that way for more than thirty-four years, and it took pride in being an industry maverick. The company’s independent attitude had helped make it one of the largest airlines in the world. Southwest, remember, was the first carrier to encourage flight attendants to tell jokes in the air.

Lately, though, some customers, especially business travelers, had complained that the free-for-all to get on a Southwest plane was no fun. To obtain a good seat, travelers had to arrive at the airport hours before their flight to secure a place at the head of the line, or remember to print out a boarding document the day before from the company’s online reservation system. Some said the process made them feel more like cattle than customers, which, in the competitive airline business, was a problem. So Southwest put the issue on the table; if assigned seating would make people happier, the company was willing to consider it.

The question turned out to be more complicated than it seemed. For one thing, no one knew how assigned seating would affect the amount of time it would take for Southwest to board everybody. If assigned seating made the process faster, then switching made sense, of course. But if it slowed things down, it wouldn’t help. Boarding speed depended, in part, on which pattern was used. Should the company start in the back of the plane and work forward? Should it start in the front and move to the back? What about boarding window seats first, then middle seats, and then, finally, aisle seats? How about alternating among various zones? Each strategy offered advantages and disadvantages, and each required a different amount of time. Given such variables, how was the airline supposed to make a decision?

To a Southwest analyst named Doug Lawson, the answer seemed obvious: the best way to determine whether assigned seating would be faster was to create a computer simulation of passengers boarding a plane, and then try out one pattern after the other. Other airlines had done more or less the same thing over the years. But Lawson’s plan had a difference—it was based on the behavior of ants.

“Ants were a good fit for this study, because we had all these individuals pouring into a tight space, interacting with one another,” he says. “Every individual had a task to do—in this case, obtain a seat—while dealing with all the others doing the same thing. In a way, it was a typical biological problem.”

Like real ants, Lawson’s digital ones followed a few simple rules to guide their behavior. “Each ant was allowed to go down the jet ramp and wander onto the plane. If we were simulating open seating in that run, each ant had its own idea of a good place to sit, based on actual passenger data, and it would look over the situation and say, well, I see that seat is open. I’m going to try to get over to that one.” If the path was clear, then the ant moved down the aisle to the appropriate row and took its seat. If the path was blocked by other ants, it either waited a few seconds, or asked them to move aside. (Lawson had to add the waiting rule after a few raucous simulations. “We had all these ants trying to get in through the galley, and they were pushing and shoving and bouncing off each other,” he says. “They were creating chaos on the plane, so we had to tone some of them down.”)

As soon as all the ants were seated, the simulation was finished, and its elapsed time could be compared with those from other runs. Since Southwest flies only Boeing 737s, the physical constraints of the problem were always the same, which made it easier to calibrate Lawson’s simulations with data from actual boardings. In addition, Southwest staged a full day of experiments using employees on a real plane to ground-truth the results. What Lawson determined from all this, after repeating his simulations for every feasible pattern, was that open seating was relatively fast, but that assigned seating, under certain circumstances, could be faster. The difference, though, was only a minute or two—not enough, by itself, to abandon Southwest’s long-standing tradition.

“We have a lot of loyal customers who just like walking onto the plane and sitting with whomever they want to,” Lawson observes. “They saw that as part of our brand, and they didn’t want the brand changed at all.”

So instead of dumping open seating, the airline took another close look at the way passengers were lined up at the gate. If the real problem was that people didn’t like competing for a spot in line, Southwest figured, then why not assign them a spot when they checked in, so they wouldn’t have to worry about it later? Boarding would still be first-come, first-served, but passengers’ places in line would be held as soon as they checked in, whether in person or online. That way, passengers wouldn’t have to show up hours ahead of time and hold their places, and when they got on the plane they could still sit anywhere they wanted, “as long as they didn’t sit on top of somebody else,” Lawson says. Southwest adopted this new system in late 2007.

Why was an ant-based simulation a good idea for Southwest? What do ants and airlines have in common? The answer has to do with the remarkable phenomenon I call a smart swarm. Evolved over millions of years, a smart swarm might be a colony of ants in the desert that has figured out exactly how many workers to assign to various jobs each morning, despite an unpredictable environment. It might be a hive of honeybees in the forest that has worked out a foolproof system to choose just the right tree for a new home, despite conflicting opinions among many individuals. It might be a school of thousands of fish in the Caribbean Sea that knows how to coordinate its behavior so precisely that it can change direction in a flash, as if it were a single silvery creature. Or it might be a vast herd of caribou on an epic migration to an Arctic coastal plain, each animal certain of reaching the calving grounds even though most haven’t got a clue about exactly where they’re going. Simply put, a smart swarm is a group of individuals who respond to one another and to their environment in ways that give them the power, as a group, to cope with uncertainty, complexity, and change.

Inspired by the practical way in which an ant colony splits a big problem into thousands of little ones, for example, Lawson set out to tap into the same kind of swarm intelligence with virtual ants he called “cognitive moving objects.” Although his digital insects were highly simplified simulations, they were designed to capture the fundamental cleverness of real ant colonies. “Down here in Texas we have lots of different types of ants,” says Lawson, who works at Southwest’s headquarters in Dallas. “Take the leaf-cutting ants of central Texas. They have the most amazing social structure you could imagine.” Like their tropical cousins in South America, this ant species (Atta texana) employs an assembly line of workers to farm a symbiotic fungus, which the colony eats. At one end of the assembly line, skillful workers cut pieces of leaves from trees or bushes and carry them back to the nest, as biologists Bert Hölldobler and E. O. Wilson describe in their book The Superorganism. Inside the nest, a second group of workers, slightly smaller in size than the first, snips the leaves into tiny pieces and leaves them for the next group. The third group of even smaller workers chews the pieces into pulp and shapes the pulp into pellets. Then a fourth group of still smaller workers plants strands of fungus inside a pile of pellets in the colony’s subterranean garden. Finally, the smallest workers of all lovingly tend the fungi, removing unwanted spores. “That’s how that shop is run,” Wilson says.

With several million workers per nest, a leaf-cutter colony can harvest a half-ton or so of vegetation a year, which gives you some indication of the incredible power that ants acquire by combining and coordinating their efforts. Such abilities, managed through a sophisticated communication system based on chemicals, has enabled ant colonies to raise their behavior as a group to a level far above that of individual ants. Which is why Wilson and Hölldobler describe such colonies as superorganisms. “The modern insect societies,” they write, “have a vast amount to teach us today.”

To some people, this may come as a surprise. How could ants, bees, or termites know something we don’t? How could such insignificant creatures solve complicated problems better than an $11-billion-a-year airline like Southwest? If ants are so smart, why aren’t they flying the 737s? The fact is, these and other creatures have been dealing with the most difficult kinds of problems for millions of years: Will there be enough food for the colony this week? Where will it be found? How many workers will the hive need to build a nest? How will the weather affect the herd’s migration this year? The way they’ve responded to these challenges has been to evolve a special form of group behavior that is flexible, adaptive, and reliable.

Translated into mathematical formulas, the principles of a smart swarm have given businesses powerful tools to untangle some of the knottiest problems they face. Manufacturing companies have experimented with them to optimize production, for example. Telephone companies have tested them to speed up calls. Aircraft mechanics and engineers have applied them to identify problems in new airplanes. And intelligence agents have used them to keep track of a dangerous world.

How does a smart swarm work? We’ll find out in the first three chapters by following biologists into the field to unlock the secrets of collective behavior. As these researchers have discovered, social insects such as ants, bees, and termites distribute problem solving among many individuals, each of which is following simple instructions but none of which sees the big picture. Nobody’s in charge. Nobody’s telling anybody else what to do. Instead, individuals in such groups interact with one another in countless ways until a pattern emerges—a tipping point of motion or meaning—that enables a colony of ants to find the nearest pile of seeds, or a school of herring to dodge a hungry seal.

In the fourth chapter we’ll look at the subtle role that individuals play in keeping a group on course. For groups such as flocks of birds, schools of fish, or herds of caribou, which are made up of individuals largely unrelated to one another, the key to survival demands a set of skills that balances group behavior with self-interest. As humans, we share many common problems with such groups, since we’re often torn by the same impulses—to cooperate but also to profit, to do what’s right for the community but also to look out for ourselves and our families.

Not every swarm is smart, of course. Group behavior also has a dark side. In the fifth chapter we’ll find out what scientists have discovered about locusts to explain why peaceful groups of grasshoppers suddenly explode into voracious plagues. To learn how human instincts can go haywire, we’ll also follow the work of researchers who have studied fatal crowd disasters among religious pilgrims in Saudi Arabia—and what’s been done to prevent such accidents in the future. What separates a smart swarm from its stupid cousin? Why does a happy crowd suddenly turn into a rampaging mob? The reason, simply put, is that a smart swarm uses its collective power to sort through countless possible solutions while the mob unleashes its chaotic energy against itself. And that makes it so important to understand how a smart swarm works—and how to harness its power.

As everyday life grows more complicated, we increasingly find ourselves facing the same problems of uncertainty, complexity, and change, drowning in too much information, bombarded with too much instant feedback, facing too many interconnected decisions. Whether we realize it or not, we too are caught up in worlds of collective phenomena that make it more difficult than ever to guide our companies, communities, and families with confidence. These challenges are already upon us, so we need to be prepared. The best way to do that, as you’ll see in the pages ahead, is to turn to the experts—not the ones on cable TV but those in the grass, in the air, in the lakes, and in the woods.

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