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.

Everything is obvious, once you know the answer: How common sense fails us.

Abstract: Why is the Mona Lisa the most famous painting in the world? Why did Facebook succeed when other social networking sites failed? Did the surge in Iraq really lead to less violence? How much can CEO’s impact the performance of their companies? And does higher pay incentivize people to work harder? If you think the answers to these questions are a matter of common sense, think again. As sociologist and network science pioneer Duncan Watts explains in this provocative book, the explanations that we give for the outcomes that we observe in life-explanations that seem obvious once we know the answer-are less useful than they seem. Drawing on the latest scientific research, along with a wealth of historical and contemporary examples, Watts shows how commonsense reasoning and history conspire to mislead us into thinking that we understand more about the world of human behavior than we do; and in turn, why attempts to predict, manage, or manipulate social and economic systems so often go awry. It seems obvious, for example, that people respond to incentives; yet policy makers and managers alike frequently fail to anticipate how people will respond to the incentives they create. Social trends often seem to be driven by certain influential people; yet marketers have been unable to identify these “influencers” in advance. And although successful products or companies always seem in retrospect to have succeeded because of their unique qualities, predicting the qualities of the next hit product or hot company is notoriously difficult even for experienced professionals. Only by understanding how and when common sense fails, Watts argues, can we improve how we plan for the future, as well as understand the present -an argument that has important implications in politics, business, and marketing, as well as in science and everyday life.

Objecting to experiments that compare two unobjectionable policies or treatments

Abstract: Randomized experiments have enormous potential to improve human welfare in many domains, including healthcare, education, finance, and public policy. However, such "A/B tests" are often criticized on ethical grounds even as similar, untested interventions are implemented without objection. We find robust evidence across 16 studies of 5,873 participants from three diverse populations spanning nine domains-from healthcare to autonomous vehicle design to poverty reduction-that people frequently rate A/B tests designed to establish the comparative effectiveness of two policies or treatments as inappropriate even when universally implementing either A or B, untested, is seen as appropriate. This "A/B effect" is as strong among those with higher educational attainment and science literacy and among relevant professionals. It persists even when there is no reason to prefer A to B and even when recipients are treated unequally and randomly in all conditions (A, B, and A/B). Several remaining explanations for the effect-a belief that consent is required to impose a policy on half of a population but not on the entire population; an aversion to controlled but not to uncontrolled experiments; and a proxy form of the illusion of knowledge (according to which randomized evaluations are unnecessary because experts already do or should know "what works")-appear to contribute to the effect, but none dominates or fully accounts for it. We conclude that rigorously evaluating policies or treatments via pragmatic randomized trials may provoke greater objection than simply implementing those same policies or treatments untested.

Political Ideology and Racial Preferences in Online Dating

Abstract: What explains the relative persistence of same-race romantic relationships? One possible explanation is structural-this phenomenon could reflect the fact that social interactions are already stratified along racial lines-while another attributes these patterns to individual-level preferences. We present novel evidence from an online dating community involving more than 250,000 people in the United States about the frequency with which individuals both express a preference for same-race romantic partners and act to choose same-race partners. Prior work suggests that political ideology is an important correlate of conservative attitudes about race in the United States, and we find that conservatives, including both men and women and blacks and whites, are much more likely than liberals to state a preference for same-race partners. Further, conservatives are not simply more selective in general; they are specifically selectivewithregardtorace. Dothesestatedpreferencespredictrealbehaviors? Ingeneral,wefindthatstatedpreferences are a strong predictor of a behavioral preference for same-race partners, and that this pattern persists across ideological groups. At the same time, both men and women of all political persuasions act as if they prefer same-race relationships even when they claim not to. As a result, the gap between conservatives and liberals in revealed same-race preferences, while still substantial, is not as pronounced as their stated attitudes would suggest. We conclude by discussing some implications of our findings for the broader issues of racial homogamy and segregation.

Dissecting the Spirit of Gezi: Influence vs. Selection in the Occupy Gezi Movement

Abstract: Do social movements actively shape the opinions and attitudes of participants by bringing together diverse groups that subsequently influence one another? Ethnographic studies of the 2013 Gezi uprising seem to answer “yes,” pointing to solidarity among groups that were traditionally indifferent, or even hostile, to one another. We argue that twomechanisms with differing implications may generate this observed outcome: “influence” (change in attitude caused by interacting with other participants); and “selection” (individuals who participated in the movement were generally more supportive of other groups beforehand). We tease out the relative importance of these mechanisms by constructing a panel of over 30,000 Twitter users and analyzing their support for the main Turkish opposition parties before, during, and after the movement. We find that although individuals changed in significant ways, becoming in general more supportive of the other opposition parties, those who participated in the movement were also significantly more supportive of the other parties all along. These findings suggest that both mechanisms were important, but that selection dominated. In addition to our substantive findings, our paper also makes a methodological contribution that we believe could be useful to studies of social movements and mass opinion change more generally. In contrast with traditional panel studies, which must be designed and implemented prior to the event of interest, our method relies on ex post panel construction, and hence can be used to study unanticipated or otherwise inaccessible events. We conclude that despite the well known limitations of social media, their “always on” nature and their widespread availability offer an important source of public opinion data.

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.