Embracing Complexity – The Payoff

 When Scientific American directed it’s younger readers to embrace complexity and place their bets on the future of emergent phenomena, it was doing so from a century and a half platform of picking winners. What can the student doubling down on a computational major expect?

Computational majors offer the toolkit needed to operate in complex natural, social and economic landscapes. Tools, such as data mining, simulation and data visualization, cut across the neat boundaries that used to separate domains of knowledge. That’s because every network, whether its made up of stars, stock traders, moviegoers, cells or trees in a forest, is made up of the same Tinker Toy elements.

Agents, that is, molecules, movie fans, segments of the atmosphere or suns, interact with their neighbors, other agents. It’s as simple as that. It’s how the world works. How agents interact with their neighbors can be gamed out in the bowels of any laptop. Whether it’s in a game, like SimCity, a virtual wind tunnel or simulated stock trading floor, its possible, in the computer, to watch agents interact. The output may be the model of tomorrows weather or like failure modes of some planetary, biological, banking or political system. The toolkit for looking at emergent phenomena is remarkably portable.

Expect to see complexity-embracing, grads showing up in a lot of places. This Hawking-hugging horde is going to siddle up, diplomas in hand, to a super-sized row of payoff windows.

For those interested in ecological restoration, the payoff includes entry into a growing worldwide market for restoration of forests, fisheries, lakes, watersheds, rivers, estuaries, wildlife sanctuaries and coastlines [i].[ii]

For Ratey and Manning, one payoff is a way of getting at the illnesses of civilization that have shown dramatic recent increases, like type-2 diabetes, coronary heart disease, and attention deficit disorder.  Another is the “a growing and necessary trend toward restoring wild systems via ecological restoration.”[iii]

For retired General Stanley McChrystal, author of the New York Times bestseller, Team of Teams: New Rules of Engagement for a Complex World, embracing complexity means more nimble and effective military, business and civic organizations. For him, the simplified command organization chart, that places the leader’s box above rows of boxes for underlings is replaced by a web of connections between mutually supporting nodes.[iv] “. . .

Few tasks are tackled alone,” he notes. In place of a “rigid hierarchy” is management in combat or business “is less about preparing people to follow precise orders than it is about developing trust and the ability to adapt within a small group.”[v] In a chapter entitled, “Leading Like a Gardener”, Gen. McChrystal echoed Scientific American’s point of view by stating that “[t]he organization as a rigidly reductionist mechanical beast is an endangered species.”[vi] It is “. . . too stupid and slow to surfive the onslaught of predators. In some cases, it simply lumbers into tar pits, lacks the strength to free itself, and slowly dies. The traditional heroic leader may not be far behind.”[vii]

For Harvard professor and physician, Nicholas Christakis and UC, San Diego professor, James Fowler, paying attention to social networks pays off in improved mental and physical health and greater control of how we lead our lives. In their 2009 book, Connected: The Surprisng Power of Our Socail Networks and How They Shape Our Lives, Christakis and Fowler, invited the reader to take off the blinders and look at the networks that surround them. “Social networks,” they said, “are intricate things of beauty. They are so elaborate and so complex – and so ubiquitous, in fact – that one has to wonder what purpose they serve.”[viii] They went on to explain how our connection to other people makes a difference in our development, mental health, physical well being, dating and mating, politics and economic success. This focus on connection is a departure from the reductionist understanding of man as stand-alone mechanism.

For complexity pioneer, the mathematician Beniot Mandelbrot, getting the Hawking Hug meant getting a better handle on investment risk and reward. His 2004 book, The (mis) Behavior of Markets: A Fractal Guide to Risk, Ruin and Reward, offered the idea that markets are not well-behaved, as elegant traditional investment theories imagined.[ix] The result has been a series of avoidable disasters. “Like the weather,” he argued, “markets are turbulent. We must learn to recognize that, and better cope.”[x]

Low Hanging Fruit

For students, the future is complex.

The 2016 MIT Technology Review article, “How the New Science of Computational History is Changing the Study of the Past,” spelled out the potential for our ability to look more closely at the ways networks operate and what it means:

One of the curious features of network science is that the same networks underlie entirely different phenomena. As a result, these phenomena have deep similarities that are far from obvious at first glance. Good examples include the spread of disease, the size of forest fires, and even the distribution of earthquake magnitude, which all follow a similar pattern. This is a direct result of their sharing the same network structure.[xi]

The conclusion: go computational. “. . . [T]here is low-hanging fruit to be had by the first generation of computational historians . . . ” And, presumably, for those toiling in other fields.

The Difficulty of a New Thing

The idea of a flying machine, the telephone, television, and interplanetary rocket travel must have seemed strange at one time.

The same goes for any new thing, including the idea that collections of things can exhibit an odd set of behaviors, labeled emergent.  But, for emergence, it shouldn’t. People have known for a long time that social insects, like ants and bees, organize well-ordered societies without central direction. The occasional madness of crowds of investors and fad followers is nothing new. The emergence of a unique personality from a collection of brain cells is no secret.

The telephone, airplane, rocket and television took generations to find a comfortable niche. The same goes for emergence and the computational tools that help us understand emergence and put it to work.

Our improved understanding of emergence, like the telephone, airplane and rocket, should have attracted an interested following. And, it has. But, for the many people steeped in a faith in science, emergence is a strangely difficult thing to come to grips with. For some, emergence flies in the face of science. It sounds like a religious belief. It offers something for nothing. That sounds dangerously close to imagining the miraculous and magical thinking. It sounds like science denial.

[i] Cunningham, Storm, The Restoration Economy: The Greatest New Growth Frontier, Berrett-Koehler Publishers, San Franscisco, 2002.

[ii] See, for example, Messier, Christian, et al, Managing Forests as Complex Adaptive Systems: Building Resilence to the Challenge of Global Change, Routledge, New York, 2013.

[iii] Ratey and Mannning, at page 10.

[iv] McChrystal, Ret. Gen. Stanley, Team of Team: New Rules of Engagement for a Complex World, Penguin, New York, 2015, page 97.

[v] Ibid.

[vi] McChrystal, at page 221.

[vii] Ibid.

[viii] Christakis, Nicholas, MD, PhD, and Fowler, James, PhD, Connected: the Surprising Power of Our Social Networks and How They Shape Our Lives, Little, Brown and Company, New York, 2009, at page ix.

[ix] Mandelbrot, Beniot, and Hudson, Richard, The (Mis) Behavior of Markets: A Fractal View of Risk, Ruin and Reward, Basic Books, New York, 2004, at page 11

[x] Ibid.

[xi] .Emerging Technology from the airXiv, “How the New Science of Computational History Is Changing the Study of the Past,” MIT Technology
Review, June 23, 2016, How the new science of computational history is changing the study of the past