Read The Gardens of Democracy: A New American Story of Citizenship, the Economy, and the Role of Government Online

Authors: Eric Liu,Nick Hanauer

Tags: #Political Science, #Political Ideologies, #Democracy, #History & Theory, #General

The Gardens of Democracy: A New American Story of Citizenship, the Economy, and the Role of Government (4 page)

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But now a new story is unfolding.

Our century is yielding a second Enlightenment, and the narrative it offers about what makes us tick, individually and collectively, is infinitely more sophisticated than what we got the last time around. Since the mid-1960s, there have been profound advances in how we understand the systemic nature of botany, biology, physics, computer science, neuroscience, oceanography, atmospheric science, cognitive science, zoology, psychology, epidemiology, and even, yes, economics. Across these fields, a set of conceptual shifts is underway:

Simple → Complex
Atomistic → Networked
Equilibrium → Disequilibrium
Linear → Non-linear
Mechanistic → Behavioral
Efficient → Effective
Predictive → Adaptive
Independent → Interdependent
Individual ability → Group diversity
Rational calculator → Irrational approximators
Selfish → Strongly reciprocal
Win-lose → Win-win or lose-lose
Competition → Cooperation

These shifts, of course, are not as clean or simple as they may appear in such a list. We acknowledge that there are volumes of nuance condensed here. But at a macro level, these shifts are real, consequential, and too often unseen.

Simple → Complex

The reductionist spirit of the first Enlightenment yielded a passion for classification—of species, of races, of types of all kinds of things—and this had the virtue of clarifying and simplifying what had once seemed fuzzy. But Enlightenment mathematics was limited in its ability to depict complicated systems like ecosystems and economies. The second Enlightenment is giving us the tools to understand complexity, as Scott Page and John Miller explain in
Complex Adaptive Systems.
Such systems—whether they are stock markets or immune systems, biospheres or political movements—are made of interacting agents, operating interdependently and unpredictably, learning from experience at individual and collective levels. The patterns we see are not mere aggregations of isolated acts but are the dynamic, emergent properties of all these interactions. The way these patterns behave may not be predictable, but they can be understood. We understand now how whirlpools arise from turbulence, or how bubbles emerge from economic activity.

Atomistic → Networked

The first Enlightenment was excellent for breaking phenomena into component parts, ever smaller and more discrete. It was an atomic worldview that conceptualized us as separate and independent. The second Enlightenment proves that while we are made of atoms we are
not
atoms—that is, we behave not in atomistic ways but as permeable, changeable parts of great networks and ecosystems. In particular, human societies are made up of vast, many-to-many networks that have far greater impact on us as individuals and on the shape and nature of our communities than we ever realized. The “six degrees” phenomenon is not a party game; it is a way of seeing more clearly what Albert-Laszlo Barabasi, author of
Linked
, describes as “scale-free networks”: networks with an uneven distribution of connectedness, whose unevenness shapes how people behave. Recognizing ourselves as part of networks—rather than as isolated agents or even niches in a hierarchy—enables us to see behavior as contagious, even many degrees away. We are all on the network, part of the same web, for better or worse. Thus does consumption of Middle East oil produce climate change, which creates drought in North Africa, which raises food prices there, which leads a vendor in Tunis to set himself afire, which sparks a revolution that upends the Middle East.

Equilibrium → Disequilibrium

Classical economics, with us still today, relied upon 19th-century ideas from physics about systems in equilibrium. On this account, shocks or inputs to the system eventually result in the system going back to equilibrium, like water in a bucket or a ball bearing in a bowl (or the body returning to “stasis” after “sickness”). Such systems are closed, stable, and predictable. By contrast, complex systems like ecosystems and economies (or hurricanes or Facebook) are open and never stay in equilibrium. In non-equilibrium systems, a tiny input can create a catastrophic change—the so-called butterfly effect. The natural, emergent state of such systems—open rather than closed—is not stability but rather booms and busts, bubbles and crashes. It is from this tumult, says Eric Beinhocker, author of the magisterial
The Origin of Wealth
, that evolutionary opportunities for innovation and wealth creation arise.

Linear → Non-linear

The first Enlightenment emphasized linear, predictable models for change, whether at the atomic or the global level. The second Enlightenment emphasizes the butterfly effect, path dependence, high sensitivity to initial conditions and high volatility thereafter: in short, it gives us chaos, complexity, and non-linearity. What once seemed predictable is now understood to be quite unpredictable.

Mechanistic → Behavioral

The first Enlightenment made the stable, order-seeking machine the generative metaphor for economic activity (assembly lines), social organization (political machines), and government’s role (that of a mechanic or clockmaker). The second Enlightenment studies not how people process things independently but rather how they behave interdependently. As David Brooks describes in
The Social Animal
, behavior is contagious, often unconsciously and unpredictably so, and individual choices can cascade suddenly into great waves of social change.

Efficient → Effective

The metaphors of the Enlightenment, taken to scale during the Industrial Age, led us to conceptualize markets as running with “machine-like efficiency” and frictionless alignment of supply and demand. But in fact, complex systems are tuned not for efficiency but for
effectiveness
—not for perfect solutions but for adaptive, resilient,
good-enough
solutions. This, as Rafe Sagarin depicts in the interdisciplinary survey
Natural Security
, is how nature works. It is how social and economic systems work too. Evolution relentlessly churns out effective, good-enough-for-now solutions in an ever-changing landscape of challenges. Effectiveness is often inefficient, usually messy, and always short-lived, such that a system that works for one era may not work for another.

Predictive → Adaptive

In the old Enlightenment and the machine age that followed, inputs were assumed to predict outputs. In the second Enlightenment, once we recognize that the laws that govern the world are laws of complex systems, we must trade the story of inputs and predictability for a story of influence and ever-shifting adaptation. In complex human societies, individuals act and adapt to changing circumstances; their adaptations in turn influence the next round of action, and so on. This picture of how neither risks nor outcomes can be fully anticipated makes flexibility and resilience more valuable at every scale of decision-making.

Independent → Interdependent

The Enlightenment allowed us to see ourselves as individuals and agents. Free from supernatural authority, people were first allowed and then expected to act independently and selfishly for themselves. This extraordinary cultural shift sparked invention, innovation, and the autonomy we expect in our daily lives. But this mode of thinking, particularly applied to the American frontier, persuaded us that we were independent rather than interdependent. A new understanding of systems and human behavior and physiology shows this to be untrue. From the quantum level up, we are far more interdependent than our politics and culture generally let us think. We are at all times both cause and effect. Our mirror neurons and evolved social rites mean that how we behave influences how others behave, and how they behave influences us. The permutating patterns formed by those interactions become the shape our societies take. And obviously, the denser and more connected the network—compare, say, America today with America 300 years ago—the greater these effects.

Rational calculator → Irrational approximators

The Enlightenment encouraged scientists to apply mathematics and physics to human nature and social dynamics, but these were of course blunt instruments for such complex work, requiring many simplifying assumptions. Over time, the caveat that these assumptions were simplifying fell away and what was left was a mechanical view that people are rational calculators of their own interest. Economists even today assume that an ordinary consumer can make complex instantaneous calculations about net present value and risk when making decisions in grocery stores between tomatoes and carrots. This
homo economicus
stands at the center of traditional economics, and his predilection for perfect rationality and selfishness permeates our politics and culture. By contrast, the behavioral science of our times is pulling us back to common sense and reminding us that people are often irrational or at least a-rational and emotional, and that we are at best approximators of interest who often don’t know what’s best for us and even when we do, often don’t do it. This accounts for the “animal spirits” of fear, longing, and greed that seem to drive markets in unpredictable and irrational ways.

Selfish → Strongly reciprocal

For centuries, a bedrock economic, legal, and social assumption was that people were inherently so selfish that they could not be expected to support or aid others not in their own genetic line. Now the study of human behavior reinforces the neglected fact that we are hardwired equally to be cooperative. As social psychologist Dacher Keltner writes in
Born to Be Good
, humans could not have survived and evolved without the social organization that only cooperation, mutuality, and reciprocity make possible. In fact, we are so tilted toward cooperation that we punish non-cooperators in our communities, even at cost to ourselves. This “strong reciprocation” strategy reflects a deep recognition, made instinctual through millennia of group activity, that all behavior is contagious, and that rewarding good with good and bad with punishment is the best way to protect our societies and therefore ourselves. Reciprocity makes compassion not a form of weakness but a model of strength; it makes pro-social morality not just moral but natural and smart.

Win-lose → Win-win or lose-lose

The story that grew out of Enlightenment rationalism and then Social Darwinism had a strong streak of “your gain is my loss.” The more that people and groups were seen as competing isles of ambition, all struggling for survival, the more life was analogized at every turn into a win-lose scenario. But the stories and science of the second Enlightenment prove what has long been a parallel intuition: that in fact, the evolution of humanity from cave dweller to Facebooker is the story of increasing adoption of nonzero, or positive-sum, attitudes; and that societies capable of setting up win-win (or lose-lose) scenarios always win. Robert Wright’s
Nonzero
describes this dynamic across civilizations. Unhealthy societies think zero-sum and fight over a pie of fixed size. Healthy societies think 1+1=3 and operate from a norm that the pie can grow. Open, non-equilibrium systems have synergies that generate increasing returns and make the whole greater than the sum of the parts. The proper goal of politics and economics is to maximize those increasing returns and win-win scenarios.

Competition → Cooperation

A fundamental assumption of traditional economics is that competitiveness creates prosperity. This view, descended from a misreading of Adam Smith and Charles Darwin, weds the invisible hand of the market to the natural selection of nature. It justifies atomistic self-seeking. A clearer understanding of how evolutionary forces work in a complex adaptive human society shows that
cooperation
is the true foundation of prosperity (as does a full reading of Adam Smith’s lesser-known masterpiece
A Theory of Moral Sentiments
)
.
Competition properly understood—in nature or in business—is between groups of cooperators. Groups that know how to cooperate—whose members attend to social and emotional skills like empathy—defeat those that do not. That’s because only cooperation can create symbiotic, nonzero outcomes. And those nonzero outcomes, borne and propelled by ever-increasing trust and cooperation, create a feedback loop of ever-increasing economic growth and social health.

Now: what does all this have to do with self-interest?

Everything. Our previous understanding of the world animated and enabled a primitive and narrow perspective on self-interest, giving us such notions as: