The Quantified Citizen
Engineering happiness might sound good, but it will leave us all less free.
In 1992, when The Man from Hope established a new standard for campaign trail empathy, there were no smartphones, no wireless activity wristbands, no life-tracking apps, no cloud. Bill Clinton felt our pain, but couldn't do much about it. In contrast, today's government caregivers have a vast new arsenal of tools at their disposal. They can feel our pain, aggregate it, analyze it, and implement policies that will reduce it by at least 10 percent. Or at least they can aspire to such grand ambitions.
"There's this pretension that everything that's of importance to human beings can be measured," says Mark D. White, chair of the philosophy department at the College of Staten Island. "This whole trend toward digitizing human life and quantifying it. And if something can be measured, it can also be influenced, manipulated, engineered."
Granted, the power to perform such feats is typically presented as the domain of technology companies, not the Department of Health and Human Services. In the reigning narrative, Silicon Valley is an anti-government force, a haven for techno-libertarian disruptors who want to gut licensing commissions, review boards, and all the other safeguards of the regulatory state and replace them with citizen-bureaucrats who maintain order through one-star Yelp reviews and below-average Uber ratings.
But whatever Silicon Valley has done so far to dismantle Big Taxi, it has also popularized and normalized a mind-set that the writer Evgeny Morozov calls "solutionism"-the idea that all human systems can be improved through the judicious application of sensor networks, commodity computing clusters, and other technologies that amplify our ability to track, say, the length of our morning showers or the number of milk cartons we throw in the trash instead of recycling.
Solutionism isn't just for start-ups. As pioneer solutionist Bill Gates suggested in a 2013 Wall Street Journal op-ed, it's highly extensible. "In the past year, I have been struck by how important measurement is to improving the human condition," he wrote. "You can achieve incredible progress if you set a clear goal and find a measure that will drive progress toward that goal."
Or to put it another way: What's good for the software mogul is good for the philanthropist. And, by extension, the policy maker.
The idea that slow-moving and largely unaccountable government agencies can increase their efficiency and impact by adopting the goal-setting discipline of private enterprise is a central tenet of the solutionist vision. But it also shifts the business of governance from processes to outcomes. It imposes an imperative not to create laws and institutions that make it possible for people to safely and freely pursue their own paths through life, but rather to achieve specific results.
In his book The Manipulation of Choice, published in 2013, Mark White examined the political ramifications of choice architecture, a.k.a. "nudging" or "libertarian paternalism," the practice of making a "good" choice the easy choice. In his book The Illusion of Well-Being, published in 2014, and in a recent paper authored for George Mason University's Mercatus Center called "The Problems with Measuring and Using Happiness for Policy Purposes," White addresses our increasing faith in quantification. There is obviously a great deal of overlap between these two subjects; the more relentlessly you measure people's behavior and begin to understand their actions and behavior, the greater the temptation to steer that behavior in subtle and sometimes not-so-subtle ways.
In The Illusion of Well-Being, White focuses on efforts to enhance gross domestic product (GDP) and other measures of economic output "with more direct measures of people's actual well-being-in simple terms, their happiness." For decades, economists, behavioral psychologists, and the occasional benevolent despot have argued that merely toting up economic gains from year to year does not give us a complete enough picture of a country's aggregate well-being. We need more wide-ranging and sophisticated data to help guide our policy makers.
In 1972, the fourth Dragon King of Bhutan pioneered the idea of a "Gross National Happiness Index." Since then, the idea that we might express such qualitative phenomena as "happiness" or "life satisfaction" in quantitative ways has gained a surprising degree of credence. In 2011, the White House Office of Management and Budget announced that it was considering how various forms of happiness measurement might help improve "regulatory policy in ways that promote the goals of economic growth, innovation, competitiveness, and job creation."
But as White argues, the sense of precision such quantification produces is largely illusory. How do we arrive at a single definition of "happiness" or "well-being" that we can apply to people of widely divergent temperaments and living situations? And even if we could agree on a definition, how do we then accurately translate highly subjective feelings and perceptions into actionable data?
Typically, happiness surveys ask respondents to choose from a selection of potential phrases to describe how they are feeling, then convert these answers into numerical amounts. "We know the spaces between inch or centimeter markings on a ruler are the same, as are the spaces between degrees on a thermometer," White observes in his book. "But we shouldn't have any confidence that the difference between zero as 'utterly unhappy' and one as 'fairly unhappy' has any particular meaning, much less the same meaning as the difference between one as 'fairly unhappy' and two as 'neither happy nor unhappy.'"
As arbitrary as these transmutations may be, they offer the appearance of precision. And that precision-and the seeming knowledge and insight it implies-legitimates intervention. If we can determine that banning all car traffic for one day each month in a given test city leads to a 0.5 percent uptick in Average Regional Happiness, aren't we compelled, and perhaps even morally obligated, to implement this tactic on a national scale?
Alas, the quantified state's interventionist mandate remains just as presumptuous in cases where the data is more solid—say Gross National Electrodermal Response or Aggregate Shower Hours. "All measures-including [gross national product]-are outcome-oriented measures that in my view are irrelevant to proper governance," White declares. "Government should be guaranteeing just and fair and free processes for people to make choices and live lives of their own choosing, without harming anyone else. Even with GDP-let's say that GDP falls by 2.1 percent one quarter. If you take the viewpoint that that decline was the result of decisions made voluntarily, under relatively good information, what does it matter if it fell? What right does the government have to say, 'This collective mass of decisions people made weren't good enough, so we're going to fix that'?"
But how likely is it that government policy makers at any level will decide to check their ambitions when it's getting easier and easier to collect and/or manufacture data that legitimizes increasingly proactive behavior? If anything, the idea that government should adopt such tactics will only become more commonplace. After all, it's what Google does. It's what Facebook does. If the Department of Health and Human Services is going to pour millions into combatting obesity, why not measure outcomes? And once we understand what influences those outcomes, shouldn't we deploy tactics that help deliver the intended results?
The problem is that such thinking imposes a viewpoint about what's "right" or what's "best" upon myriad individual lives. A state that emphasizes processes over outcomes is a pluralist state, whose citizens have the freedom to define and pursue happiness in their own particular fashion. A quantified state optimizes outcomes by narrowing possibilities—and establishing "efficiency and uplift for all" as the new national mandate. You don't need a sophisticated sensor network to register that as a step backward.