Research Areas


 

Biology of Social Interactions

Correlated Genotypes in Friendship Networks

A full understanding of social networks must take biology into account. We explore both the biology of individuals that plays a role in social network phenomena (including genetics, epigenetics, physiology, and neurobiology), and the genetic and evolutionary processes shaping networks in our species and in other social species as a whole.

We are particularly interested in understanding why our tendency to form networks may ultimately exist. We are approaching this question from a number of angles. For example, we have examined the heritability of social network features, and the genetic similarity of friends, and we have conducted field research on the features and functions of social networks cross-culturally, including in an “evolutionarily relevant” population of hunter-gatherers. We have also done work developing mathematical models for the evolution of social networks and related phenomena, such as cooperation and in-group favoritism. Ongoing work in the lab explores such topics as the role of violence and antagonistic interactions in network formation; the origins and consequences of homogamy and active partner choice; the physiological correlates of various social network positions; and the biology of partner choice in diverse species (e.g., involving pheromones).

Selected Papers

Model of Genetic Variation in Human Social Networks
PNAS: Proceedings of the National Academy of Sciences, 2009
Friendship and Natural Selection
PNAS: Proceedings of the National Academy of Sciences, 2014

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Experiments with Online Networks

The great majority of traditional work in the social sciences (outside psychology) has involved observational data, and not the controlled experiments that are so common in the natural sciences. In 1969, sociologist Morris Zelditch asked, rhetorically, “Can you really study an army in a laboratory?” Nearly half a century later, the answer appears to be “yes.” In order to build a more unified understanding of the causes of social behavior, we therefore conduct experiments, particularly in online settings. We have created a powerful software platform (‘Breadboard’) that allows us to recruit thousands of subjects to come to a virtual lab environment and participate in experiments in which we can manipulate social interactions in diverse ways.

Our general approach is to explore the implications of social network structure for collective outcomes, using cooperation and other prosocial behaviors as model systems. Some of this work is done in cooperation with David Rand’s laboratory – for example, when we have explored how the dynamism inherent to social networks may promote cooperative behavior. In other work, we have found that the propensity toward cooperative behavior spreads through social networks, much like other behavioral phenomena we have studied using observational data (such as obesity, smoking, or happiness). Our current agenda is to further examine complex social behavioral patterns that are typical of human sociality, including the emergence of economic inequality; the ability of groups to work together to solve problems; and the flow of true and false information in social graphs.

Selected Papers

Dynamic Social Networks Promote Cooperation in Experiments with Humans
PNAS: Proceedings of the National Academy of Sciences, 2011
Static Network Structure Can Stabilize Human Cooperation
PNAS: Proceedings of the National Academy of Sciences, 2014

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Experiments with Face-to-Face Networks

A significant amount of our attention at the Human Nature Lab is devoted to the development of new ways to intervene in social networks to promote wellness in human populations. It’s part of our effort to answer the “so what?” question: “So what if we can understand how networks form and operate, what can we do what this knowledge to make the world better?” Our first effort in this regard, which took place during the H1N1 flu outbreak in 2009, involved the demonstration of a whole new way to forecast epidemics in advance of their striking the general population. Indeed, in the past few years, we and others have been showing that it is possible to manipulate attributes not only of online, but also of offline, face-to-face, networks in order to enhance health behaviors and improve well-being. We have explored how network science may be used as a force for good, in order to change population-level outcomes. Manipulating the structure and function of social network ties can foster the adoption of new health behaviors, or influence cooperation. We have also explored how innovation and creativity might be fostered in real life groups, by exploiting network methods.

Ongoing experimental work on face-to-face networks includes (1) examining how school children influence each another’s health behaviors through social interactions captured using RFID technology; (2) investigating the spread of affect within social networks using techniques from experimental psychology; and (3) a large-scale network intervention study involving a randomized controlled trial of a maternal and newborn health intervention in rural Honduran villages. Through this work, we hope to develop more effective ways to conduct public health (and other) interventions to optimize the spread of desirable behaviors.


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Computational Analysis of Online Networks

We live in a world of pervasive data, and this has led to the advent of a new kind of computational social science. If one had asked social scientists even 20 years ago what powers they dreamed of acquiring, they might have cited the capacity to inconspicuously track the behaviors, purchases, movements, interactions, and thoughts of whole cities of people, in real time. Of course, this is exactly what is possible now that so many of us – via credit cards, cell phones, online social networks, employer records, health information systems, and so on – leave just such digital breadcrumbs as we move through our lives. Such “massive/passive” data will revolutionize our ability to understand behavior.

In addition to the specific content areas that our lab is studying (e.g., related to health behaviors), our goal is to further inform our observational research through advanced computational methods. As part of this research thrust, we take advantage of new tools in computational social science, and our access to novel (often commercial) datasets, to study naturally occurring online and offline networks, including telecommunications networks.


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Observational Studies of Social Networks

The first steps we took to understand social networks involved not experiments, but rigorous observational methods, often involving datasets we ourselves collected and assembled. It is our belief that careful observation of the natural world is a proper predicate for subsequent experiments of the kind described above

Beginning in 2007, we published an extensive set of papers using the Framingham Heart Study social network (a version of which we have placed in the public domain) as well as several other large-scale observational data sets, exploring social contagion for phenomena as diverse as obesity, smoking, drug use, sexual practices, and happiness. At present, our observational work is mainly focused on the developing world. Many developing world contexts not only have a greater burden of health problems, but also tend to have more traditional social systems with less modern technology. Such settings are ideal for examining how social networks affect health outcomes. Sample sizes for these projects range from 2,000 to 40,000 people, from 4 to 160 villages. We’ve examined latrine use in India, AIDS treatment in Uganda, and neonatal care in Honduras. A key current focus of our work is to understand the role of both positive and negative ties in networks, and the impact of membership in network communities upon diverse behaviors.


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Network Methodology

Network science is an area of active methodological development across many disciplines, 097 Social Network Visualizations in Epidemiologyfrom sociology to statistics to public health to physics. The Human Nature Lab brings numerous methodologists together to tackle the complex problems inherent in the field. We have published on the longitudinal analysis of large social networks; on the estimation of peer effects in social networks; and on understanding spreading phenomena in partially observed networks. We have also pioneered the use of new tools for doing experiments with networks online. Our lab’s interest in the genetic bases of social interactions has also led to advances in this area.

Ongoing work involves improving methods for understanding longitudinal network data (including through the use of experiments and instrumental variable approaches); methods for studying bipartite networks; community detection within networks; and improving methods for understanding fundamental network dynamics. We have also given thought to the ethical use of network technology, and the ethics of human subjects research related to the use of online and other network data.


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