Article

Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study

Department of Political Science, University of California, San Diego, CA, USA.
BMJ (online) (Impact Factor: 16.38). 02/2008; 337:a2338. DOI: 10.1136/bmj.a2338
Source: PubMed

ABSTRACT To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks.
Longitudinal social network analysis.
Framingham Heart Study social network.
4739 individuals followed from 1983 to 2003.
Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties.
Clusters of happy and unhappy people are visible in the network, and the relationship between people's happiness extends up to three degrees of separation (for example, to the friends of one's friends' friends). People who are surrounded by many happy people and those who are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km) and who becomes happy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation.
People's happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.

Download full-text

Full-text

Available from: James Henry Fowler, Jul 30, 2015
2 Followers
 · 
381 Views
    • "In a similar vein, some studies consider the persuasive power of opinion leaders to be overrated: A sufficient number of easily influenceable individuals is seen more important than few influential network hubs (e.g., Watts and Dodds, 2007); people tend to associate and conform with others from similar socio-economic classes (e.g., Eppstein et al., 2011; Kossinets and Watts, 2006). However, this homophily claim is contested (e.g., Wejnert, 2002; Fowler and Christakis, 2008). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Early adopters promoting electric vehicles in their social network may speed up market uptake of this technology. Apart from their opinion leader status, few previous research details the motivations which turn early adopters into advocates for innovation who approach the non-adopters among their family and friends, or casual acquaintances. Drawing on a survey among 1398 e-bike and 133 e-scooter early adopters in Austria, personal drivers of engagement in interpersonal diffusion are investigated. Longitudinal data one year later for 157 e-bike users allows tests of causal relations. A complementary sample of 33 network peers illustrates the early adopters’ social impact. Early adopters engage actively in discussing product features, instigating trial behavior and recommending purchase. Analyses by structural equation modeling show that efforts at interpersonal diffusion are driven by opinion leadership, experienced product performance, and perceived normative expectations of others toward pro-environmental technologies. Mediator and moderator analyses underline that opinion leadership is conveyed upon early adopters because personal norms and technophilia qualify them as credible and competent for the specific topic of e-vehicles. Social norm interrelations point to dynamic interactions and discourse between early adopters and their addressees. Evidence from the peer sample suggests though that the persuasive impact of early adopters is small. To accelerate market entry of electric vehicles, public or private agencies should foremost approach early adopters scoring high in the identified drivers, and empower them in their role as multiplicators by providing pre-prepared product information and encouraging them to continuously address peers.
    Transportation Research Part A Policy and Practice 08/2015; 78:146-160. DOI:10.1016/j.tra.2015.04.017 · 2.73 Impact Factor
  • Source
    • "This methodology can be applied well beyond the present study, e.g. to study the spread of healthy behavior or productive work practices in an organization. Our methodology quantified complex patterns of social influence that go beyond the contagion metaphor [21] [27]. The associations we identified between social-situational aspects and transition probabilities – attraction, repulsion, inertia and push – account for a consistent majority (70%, 43 out of 63 ) of the transition types (3 transitions for each state level × three levels per state × 7 states) within the seven personality and affect states addressed in this work. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Contagion, a concept from epidemiology, has long been used to characterize social influence on people's behavior and affective (emotional) states. While it has revealed many useful insights, it is not clear whether the contagion metaphor is sufficient to fully characterize the complex dynamics of psychological states in a social context. Using wearable sensors that capture daily face-to-face interaction, combined with three daily experience sampling surveys, we collected the most comprehensive data set of personality and emotion dynamics of an entire community of work. From this high-resolution data about actual (rather than self-reported) face-to-face interaction, a complex picture emerges where contagion (that can be seen as adaptation of behavioral responses to the behavior of other people) cannot fully capture the dynamics of transitory states. We found that social influence has two opposing effects on states: \emph{adaptation} effects that go beyond mere contagion, and \emph{complementarity} effects whereby individuals' behaviors tend to complement the behaviors of others. Surprisingly, these effects can exhibit completely different directions depending on the stable personality or emotional dispositions (stable traits) of target individuals. Our findings provide a foundation for richer models of social dynamics, and have implications on organizational engineering and workplace well-being.
  • Source
    • "Cognitive psychologists hold the opinions that herding effect emerges because of local interactions between individuals instead of central coordination [6]. Socialists think herding effect is caused by social networks [10] [11] [12], while some economists believe that it is due to the fact that people are incapable of dealing with new information effectively [13] [14]. Recent researches in social psychology about herding effect in emergency [15] [16] [17] indicate that, escaping behaviors among individuals are rational actions instead of crowd panic and a series of phenomena including herding effect are the result of rational choices in behaviors for escaping agents. "
    [Show abstract] [Hide abstract]
    ABSTRACT: So far, there has been no conclusion on the mechanism for herding, which is often discussed in the academia. Assuming escaping behavior of individuals in emergency is rational rather than out of panic according to recent findings in social psychology, we investigate the behavioral evolution of large crowds from the perspective of evolutionary game theory. Specifically, evolution of the whole population divided into two subpopulations, namely the co-evolution of strategy and game structure, is numerically simulated based on the game theoretical models built and the evolutionary rule designed, and a series of phenomena including extinction of one subpopulation and herding effect are predicted in the proposed framework. Furthermore, if the rewarding for rational agents becomes significantly larger than that for emotional ones, herding effect will disappear. It is exciting that some phase transition points with interesting properties for the system can be found. In addition, our model framework is able to explain the fact that it is difficult for mavericks to prevail in society. The current results of this work will be helpful in understanding and restraining herding effect in real life.
    Chaos Solitons & Fractals 06/2015; 75. DOI:10.1016/j.chaos.2015.02.008 · 1.50 Impact Factor
Show more