Duncan J. Watts

Bio: Duncan J. Watts is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Randomness & Small-world network. The author has an hindex of 62, co-authored 146 publications receiving 83816 citations. Previous affiliations of Duncan J. Watts include Cornell University & Microsoft.

The virtual lab

Abstract: The Internet and the Web have transformed society, spawning new industries, altering social and cultural practices, and challenging long-accepted notions of individual privacy, intellectual property, and national security. In this talk, I argue that social science is also being transformed. In particular, I describe how crowd sourcing sites like Amazon's Mechanical Turk are increasingly being used by researchers to create "virtual labs" in which they can conduct behavioral experiments on a scale and speed that would have been hard to imagine just a decade ago. To illustrate the point, I describe some recent experiments that showcase the advantages of virtual over traditional physical labs, as well as some of the limitations. I then discuss how this relatively new experimental capability may unfold in the near future, along with some implications for social and behavioral science.

Collective Problem Solving in Networks

Abstract: Many complex problems in science, business, and engineering require a trade-off between exploitation of known solutions and exploration of new possibilities. When complex problems are solved by collectives rather than individuals, this explore-exploit trade-off is complicated by the presence of communication networks, which can accelerate collective learning, but can also lead to convergence on suboptimal solutions. In this paper, we report on a series of 195 web-based experiments in which groups of 16 individuals collectively solved a complex problem and shared information through different communication networks. We found that network structure affected collective performance indirectly, via its impact on individual search strategies, as well as directly, by impacting the speed of information diffusion. We also found that networks in general suppress individual exploration, but greatly amplify the benefits of the exploration that takes place. Finally, we identified two ways in which individual and collective performance were in tension, consistent with longstanding theoretical claims.

Resilient Cooperators Stabilize Long-Run Cooperation in the Finitely Repeated Prisoner's Dilemma

Abstract: The dynamics of learning in finitely repeated games of cooperation remains an open question in large part because the timescale on which learning takes place is much longer than that of traditional lab experiments. Here we report results of a “virtual lab” experiment in which 94 subjects play up to 400 ten-round games of Prisoners Dilemma over the course of twenty consecutive weekdays. Consistent with previous work, the typical round at which players first defect creeps steadily earlier over the first several days; however, this unraveling process slows after roughly one week and remains stable for the rest of the experiment. Analyzing individual strategies shows that roughly 40% of players resist the temptation to unravel, cooperating conditionally throughout the experiment, even at a significant cost to themselves. We call these players resilient cooperators. Finally, using a standard learning model we predict that the presence of more than a critical fraction of resilient cooperators can permanently stabilize unraveling among a majority of rational players. These results shed new and hopeful light on the long-term dynamics of cooperation, and demonstrate the importance of conducting behavioral experiments on longer timescales than previously contemplated.

Emergence of Scaling in Random Networks

Abstract: Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Statistical mechanics of complex networks

Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Community structure in social and biological networks

Abstract: A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known—a collaboration network and a food web—and find that it detects significant and informative community divisions in both cases.