Hacking Happiness (3 page)

It’s when augmented reality becomes ubiquitous that these issues of data privacy and commerce will become visible. They’ll literally be right in front of our eyes. And a final technological component people are already utilizing will cause widespread cultural concern. Facial recognition technology lets the user point their phone or device at someone’s face and instantly obtain their name and other available data. And remember—Facebook has allowed millions of people to tag themselves and others for years in pictures they’ve posted to their pages. While the idea of crowdsourcing users to stay in touch with and tag friends may or may not be of concern to you, what will likely be upsetting is when strangers can access photos and data instantly by simply looking at you in public. Remember the tagging idea from Facebook Graph Search?
Now strangers will see those pictures floating above your head while you’re waiting in line at Starbucks.

The good news about this existing digital economy is that many consumers have stopped being complacent about the misuse of their data. A recent Edelman study
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found that 90 percent of consumers are concerned about the data security and privacy of their online information, and roughly 70 percent reported that privacy and security was a concern they had regarding their social media accounts. In the same study, respondents stated that they were, on average, 67 percent likely to switch their social media providers or stop using such services entirely if their information were accessed without permission.

So there’s a reason to Get H(app)y—people are beginning to feel their data has renewed value, and are claiming the right to know how it’s being used. There are dozens of companies like Personal or Reputation.com providing a model of data for people to store all the elements of their digital identity safely in one place, a model known as “personal clouds” (also called “data banks” or “data vaults”). Likewise, there is a greater sense of desired accountability from companies who are, by and large, controlling the data economy.

But here’s a sticky wicket—the same standards of accountability we apply to Facebook and Google will be measured against us. How we conduct ourselves in the realm of the Outernet will leave digital fingerprints that will define our character while leaving a trail of identity-defining data. I refer to this trend as the rise of accountability-based influence (ABI), in which scores similar to eBay’s detailed seller ratings gauge individuals’ actions versus their words. Tracking trust will soon become akin to seeing a person’s credit score, and the lines between Klout and commerce will blur even further. Will people benefit more from being popular and having influence, or by demonstrating positive character as defined by the digital portraits of their actions?

Both models will likely evolve in unison. And the growing
adoption of virtual currency platforms will mean people will become more comfortable with the idea of exchanging specie (market-based money) for currency (social capital in the form of trust or influence).

The Science of Happiness

A maturing field of science known as positive psychology is helping people see themselves in a new light. Measuring ourselves by our virtuous potential rather than focusing on our brokenness is transforming the nature of therapy in the modern world. We all have pain, but it doesn’t have to be a stigma—actions and behavior associated with that pain are also
data
. When allowed the opportunity to optimize our lives unhindered by condemnatory scrutiny, we can use data and new digital tools to make ourselves happier.

This science of happiness, which encompasses the fields of psychology, physiology, and economics, is proving that we aren’t born with set levels of well-being. Unlike the medieval idea of humors, we’re not predetermined to suffer throughout our lives based on rudimentary assessments of temperament. Some call it “well-being.” Some called it “eudaimonia.” Some call it “flourishing.” Some call it “flow.” Some call it “life satisfaction.” However you phrase the idea of a deeper, intrinsic, and long-lasting increase of happiness, I have great news—you can increase it no matter who you are. Science is proving this fact. When we better protect and manage our personal data, we’ll also be able to decide who gets access to and benefits from the specific attributes of our emotional lives.

Happinomics—or the Economics of Happiness

There is an economy of happiness. This isn’t figurative. In the sense that our actions, moods, and collective behavior can be tracked with greater nuance than ever before, monetary and policy
decisions can be made that affect the economic standing of a population. Note that “happiness indicators” in economics typically don’t refer to just the mood of a country—these indicators are metrics referring to multiple aspects of “well-being” which typically comprise details about things like the environment, education, and physical and mental health. Countries such as Bhutan, the United Kingdom, and the United States have all begun exploring how these metrics, which measure a wider breadth of attributes than GDP (gross domestic product), can give a clearer picture regarding the health of their citizens. These indicators typically focus on measuring increased well-being, a term that goes beyond mood and refers to a state of balance between multiple factors that affect you overall. On an individual level, well-being comprises your physical, mental, and emotional health. On a national level, well-being examines issues like education, the environment, civic engagement, and citizen health.

Multiple experts who study the science of happiness believe the positive increase in mood many associate with happiness comes as a result of action. In a sense, happiness is an output you experience after achieving a goal. On a national level, metrics gauging happiness are utilized to best understand how the actions of a government are improving people’s lives.

Does it seem strange to measure people’s happiness as an indication of a country’s success? It is a newer idea, but has become a global trend because of increased sentiment that the measurement of gross domestic product (GDP) isn’t working. First developed in 1934 by Simon Kuznets, a Russian-American economist, the GDP metric was adopted as the main tool for measuring a country’s economy in 1944 after the Bretton Woods conference. This gathering of 730 United Nations delegates was tasked with trying to regulate the global economy after the Second World War. And in the sense that the model was adopted globally and used as a standard measure, it’s been helpful.

But the GDP is primarily focused on financial measures—things like increases in goods production and salary levels. It doesn’t account for the quality of a country’s educational resources or care of the environment, and it wasn’t designed to. In many ways, the GDP has become the primary measure of a country’s success, casting a value judgment on citizens based primarily on wealth.

But a focus on increased productivity as measured by the GDP hasn’t increased happiness. As Jeffrey Sachs, the renowned economist from Columbia University and one of the editors of the
World Happiness Report
created for the United Nations, notes: “The U.S. has had a three-time increase of GDP per capita since 1960, but the happiness needle hasn’t budged.”
¹⁰
Happiness measurement in this regard is based on something called subjective well-being, which means asking people to rate their happiness on a numbered scale.

It’s remarkable that the GDP has become such an influential measurement of value considering all the things it doesn’t account for. Famed New Zealand politician and author Marilyn Waring pointed out in her book
If Women Counted
that the GDP systematically underreports work performed by women who take on the traditional role of primary caregiver in the home. In this context, in a very real sense, according to the GDP’s assessment of value,
women don’t exist.

What we choose to measure matters.

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I want to pause here and remind you of something. The measures of subjective well-being and GDP produce data. Traditionally these metrics have been collected largely through survey responses. But with the advent of social media, wearable devices, and ubiquitous computing, capturing happiness data will become commonplace. This is significant because in a world where fiscal wealth has been the predominant measure of value, the hidden strengths and attributes of people as revealed by technology will allow for a form of
“merrytocracy” based on personal design. Quantified happiness, determined by individuals, will begin to drive a new form of economics based on data.

Let’s be clear—in the same way that I can’t tell you what makes you happy, no technology can necessarily fully quantify your emotional state. But technology can provide what’s known as a “proxy” for behavior or emotion, and it’s the insights gained from these examples that can imply happiness or well-being, especially as related to health.

As an example, the Georgia Tech Homelab did a study with seniors living at home alone in which a simple sensor was placed on their bathroom doors, indicating when it opened or closed. Over the course of a longitudinal study they discovered that a 1 percent increase or decrease in the movements of that door suggested upwards of a 50 to 60 percent deterioration in health. Bathroom visits are a prime indicator of physical well-being—fewer visits could indicate bloating or more frequent trips could suggest dehydration.

Mobilyze is a platform designed to help people suffering from depression, employing mobile technology and context sensing to help with time-specific interventions. As described in the
Journal of Medical Internet Research
, the team behind the project developed an app where algorithms predicted patients’ moods, activities, and environmental and social contexts based on thirty-eight different types of mobile sensors.
¹¹

The study, conducted in 2011, was one of the earliest attempts to use context sensing to identify mental health–related states. The relevance of context here also means the insights that can be gained by comparing different sensor data. For instance, did a patient register a more negative mood indoors than outdoors? Noted once, this observation is merely information. If the behavior is repeated, a caregiver could suggest a patient spend more time outdoors to quantify whether that behavior would have ongoing positive results.

The algorithm reference for the app above is also critical in this description. If any quantified behavior is repeated on multiple individuals in a study with enough data to categorize a pattern, a machine-learning model (algorithm) can be generated. This algorithm could help identify behaviors in new users and predict how they may react—in a sense, the tool “gets to know” a patient and can help them
before they even have a negative incident
. Combined with a voice recognition and search platform like Apple’s Siri, the “personal digital assistant” model is evolving rapidly. Where patients or individuals are part of shaping how their data is studied, privately and in context, this technology is transformative. When people are kept out of the loop, and intimate data is exploited, algorithms may be driven solely from the intention of profit versus benefit.

As a final example, a research team at the University of Cambridge built Emotion Sense for Android, an app that lets you “explore how your mood relates to the data your smartphone can invisibly capture as you carry it throughout the day.”
¹²
Using the highly articulated microphone in an Android phone, the app identifies multiple emotional states from users based on the inflections of their voices.

This passive quality of information capture, where users don’t need to actively input data, is what is transforming modern measurement of people and their emotions. A report written by the researchers, “Emotion Sense: A Mobile Phones–based Adaptive Platform for Experimental Social Psychology Research,” emphasizes this point: “Mobile sensing technology has the potential to bring a new perspective to the design of social psychology experiments, both in terms of accuracy of the results of the study and from a practical point of view. Mobile phones are already part of the daily life of people, so their presence is likely to be ‘forgotten’ by users, leading to accurate observation of spontaneous behavior.”
¹³

Until now, anyone attempting to measure emotion via traditional means has always confronted the problem of what’s known as
survey bias—people respond differently to questions when they know their responses are being measured. Passive sensors and ubiquitous computing mean that the objective or quantified measure of people’s data will become more accurate. And the subjective assessment of their well-being (asking if they’re happy) will still be utilized to determine people’s responses to whatever is being measured.

What I’ve demonstrated with these examples is that we’re in an era when people are beginning to realize that technology will help them accurately assess their emotions and the actions that contribute to them. We’ve all thought we were in love with someone who turned out to be wrong for us. We’ve all had jobs we thought would be great and we ended up not being fulfilled by the work. What if you had a Mio Alpha watch that measures heart rate that you wore on multiple first dates to see who, literally, made your heart skip a beat? What if the watch worn at work could help you identify the stress patterns brought on by an abusive manager, so you’d know the real reason you didn’t like your job?

The hidden is becoming visible. Culture will shift. The rules have not yet been set.

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So let’s recap—people are now tracking their own behavior, moods, and health via quantified self tools. Objects around us (our cars, appliances) are outputting data directly related to how we move through the world. Soon, devices like Google Glass will let consumers record the world around them, tagging and posting massive amounts of content further relating to people’s data.