It also encourages more parents to have their kids tested, thus detecting mild and meaningless allergies to nuts. And, finally, this encourages still more avoidance of nuts, which may actually lead to a rise in true nut allergies because lack of exposure to allergens early in life is thought to contribute to the onset of allergies later.22
MPI is a pathological phenomenon, but it takes advantage of a nonpathological process that is fundamental in humans, namely, the tendency to mimic the emotional state of others. Real laughter also can be contagious and so can real happiness. But comparing epidemic hysteria to these more normal processes is like comparing the stampede of a herd to its more usual and orderly migration.
Tracking the Spread of Emotions
Measuring the subjective experience of emotions (as compared with their visible, biological, or neurological manifestations) requires asking people how they are feeling. One of the more systematic ways of doing this is known as the experience-sampling method. This method uses a series of alerts (such as signals sent to a beeper or cell phone) at unexpected times to prompt subjects to document their feelings, thoughts, and actions while they are experiencing them.23 The result is a thorough picture of the ups and downs of subjects’ daily emotional lives.
One of the advantages of this method is that it allows groups of interacting people to be evaluated simultaneously in real time. For example, one team of investigators, interested in the spread of emotions within families, outfitted fifty-five families (consisting of a mother, father, and one adolescent) with beepers for one week. The participants were beeped roughly every 90 to 120 minutes between 7:30 a.m. and 9:30 p.m., and a total of 7,100 time points were observed in these 165 individuals. Various emotional states were measured, such as whether the subjects were happy or unhappy. Although the investigators could not rule out the possibility that the entire family was simultaneously exposed to one thing that made them all sad or happy at once (a confounding effect that we will discuss in greater detail in chapter 4), they did try to tease out how emotions spread within these families.
The strongest path was from daughters to both parents, while, conversely, the parents’ emotional state appeared to have no effect on their daughters. Fathers’ emotions affected their wives and their sons but not their daughters. This appeared to be especially true when fathers returned from work: when dad came home in a lousy mood, he soon made the whole household miserable.24
A similar method has been used to examine the transmission of emotions among teams of nurses, athletes, and even accountants.25 In such professional settings, a key question was whether one fired-up team member could improve the mood and thus the performance of his teammates. Not surprisingly, positive mood is associated with a range of team-performance-enhancing changes, including greater altruistic behavior, increased creativity, and more efficient decision making. A nice demonstration involved outfitting thirty-three professional male cricket players with pocket computers that recorded their moods four times a day during a match (which can have the insane duration of five days). There was a strong association between a player’s own happiness and the happiness of his teammates, independent of the state of the game; further, when a player’s teammates were happier, the team’s performance improved.
The Spread of Happiness
Despite the biological and psychological evidence for emotional mimicry, and the numerous cases of MPI arising from epidemic anxiety, until recently little was known about the precise role of social networks in the spread of emotions. Yet, the MPI cases suggest that emotions spread far and wide, flowing through social-network ties from person to person to person, and that there should be a normal analogue to this pathological phenomenon. Indeed, there can be waves of emotions in the vast fabric of human social relationships, so that people in particular locations in the social network have one emotional experience, and others elsewhere who come under different influences have a different experience altogether.
Strangely, while researchers in diverse fields, including medicine, economics, psychology, neuroscience, and evolutionary biology, have identified a broad range of stimuli of individual human happiness, they have not addressed a key (perhaps the key) determinant: the happiness of others. It may be obvious that our friends and family can make us happy, but before we undertook our own investigation, no one had ever explored how happiness can spread through social networks from person to person to person.
We became curious about this. We were particularly interested in determining whether the spread of emotions occurred not just between you and your friends (dyadic spread) but also between you and your friends’ friends, and their friends, and beyond (hyper-dyadic spread). How far did emotions travel in the network? And were there geographic or temporal constraints on the spread?
Our first step in answering these questions was to assemble a data set that had measures of emotions and social connections over time. (We discuss that process in chapter 4.) We then created a graph of the social network of happiness, as shown in plate 1. This illustration shows ties among siblings, friends, and spouses in a sample drawn from 12,067 people originally from Framingham, Massachusetts, in the year 2000, along with their levels of happiness. No one had ever plotted such a graph before. One thousand twenty people are represented, and each node is colored on a spectrum from blue (unhappy) to yellow (happy) according to the subject’s level of happiness. Looking at this image suggests two observations. First, unhappy people cluster with unhappy people in the network, and happy people cluster with happy people. Second, unhappy people seem more peripheral: they are much more likely to appear at the end of a chain of social relationships or at the edge of the network.26
Clustering of this kind in social networks can arise from a variety of processes. Happy people might choose each other as friends or be exposed to the same environments that cause them all to be happy at the same time. But our analyses allowed us to adjust for these effects. And we found that clustering is also due to the causal effect of one person’s happiness on another’s. Mathematical analyses of the network suggest that a person is about 15 percent more likely to be happy if a directly connected person (at one degree of separation) is happy. And the spread of happiness doesn’t stop there. The happiness effect for people at two degrees of separation (the friend of a friend) is 10 percent, and for people at three degrees of separation (the friend of a friend of a friend), it is about 6 percent. At four degrees of separation, the effect peters out. Here we have our first evidence of the Three Degrees of Influence Rule. Emotions (and, as we will see later, norms and behaviors) spread in social networks from person to person to person, but they do not spread to everyone. Just as a ripple in a pond eventually fades away, so too does the ripple of an individual’s happiness fade through the social network.
At first glance, these effects may not seem very significant. But compare them to the effect of having a higher income. An extra $5,000 in 1984 dollars (which corresponds to about $10,000 in 2009 dollars) was associated with only a 2 percent increased chance of a person being happy. So, having happy friends and relatives appears to be a more effective predictor of happiness than earning more money. And the amazing thing is that even people who are three degrees removed from you, whom you may have never met, can have a stronger impact on your personal happiness than a wad of hundreds in your pocket. Being in a particular spot in a social network, exposed to people with particular feelings, has important implications for your life.
It is well known that having more friends and relatives is much more likely to put a smile on your face than having more cash.27
