Showing papers by "Duncan J. Watts published in 2011"

The structure and dynamics of networks

Abstract: Networks have become a general concept to model the structure of arbitrary relationships among entities. The framework of a network introduces a fundamentally new approach apart from ‘classical’ structures found in physics to model the topology of a system. In the context of networks fundamentally new topological effects can emerge and lead to a class of topologies which are termed ‘complex networks’. The concept of a network successfully models the static topology of an empirical system, an arbitrary model, and a physical system. Generally networks serve as a host for some dynamics running on it in order to fulfill a function. The question of the reciprocal relationship among a dynamical process on a network and its topology is the context of this Thesis. This context is studied in both directions. The network topology constrains or enhances the dynamics running on it, while the reciprocal interaction is of the same importance. Networks are commonly the result of an evolutionary process, e.g. protein interaction networks from biology. Within such an evolution the dynamics shapes the underlying network topology with respect to an optimal achievement of the function to perform. Answering the question what the influence on a dynamics of a particular topological property has requires the accurate control over the topological properties in question. In this Thesis the degree distribution, twopoint correlations, and clustering are the studied topological properties. These are motivated by the ubiquitous presence and importance within almost all empirical networks. An analytical framework to measure and to control such quantities of networks along with numerical algorithms to generate them is developed in a first step. Networks with the examined topological properties are then used to reveal their impact on two rather general dynamics on networks. Finally, an evolution of networks is studied to shed light on the influence the dynamics has on the network topology.

Everyone's an influencer: quantifying influence on twitter

Abstract: In this paper we investigate the attributes and relative influence of 1.6M Twitter users by tracking 74 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we find that the largest cascades tend to be generated by users who have been influential in the past and who have a large number of followers. We also find that URLs that were rated more interesting and/or elicited more positive feelings by workers on Mechanical Turk were more likely to spread. In spite of these intuitive results, however, we find that predictions of which particular user or URL will generate large cascades are relatively unreliable. We conclude, therefore, that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects. Finally, we consider a family of hypothetical marketing strategies, defined by the relative cost of identifying versus compensating potential "influencers." We find that although under some circumstances, the most influential users are also the most cost-effective, under a wide range of plausible assumptions the most cost-effective performance can be realized using "ordinary influencers"---individuals who exert average or even less-than-average influence.

Who says what to whom on twitter

Abstract: We study several longstanding questions in media communications research, in the context of the microblogging service Twitter, regarding the production, flow, and consumption of information. To do so, we exploit a recently introduced feature of Twitter known as "lists" to distinguish between elite users - by which we mean celebrities, bloggers, and representatives of media outlets and other formal organizations - and ordinary users. Based on this classification, we find a striking concentration of attention on Twitter, in that roughly 50% of URLs consumed are generated by just 20K elite users, where the media produces the most information, but celebrities are the most followed. We also find significant homophily within categories: celebrities listen to celebrities, while bloggers listen to bloggers etc; however, bloggers in general rebroadcast more information than the other categories. Next we re-examine the classical "two-step flow" theory of communications, finding considerable support for it on Twitter. Third, we find that URLs broadcast by different categories of users or containing different types of content exhibit systematically different lifespans. And finally, we examine the attention paid by the different user categories to different news topics.

Cooperation and contagion in web-based, networked public goods experiments

Abstract: A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of experiments on Amazon Mechanical Turk, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial "seed" players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation did not spread beyond direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and cost-effectiveness of web-based experiments over those conducted in physical labs.

Everything is obvious : how common sense fails

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? 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. 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. Only by understanding how and when common sense fails can we improve how we plan for the future, as well as understand the present-an argument that has important implications in politics, business, marketing, and even everyday life.

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.

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.

The Measure of All Things: Finding Out That Something Doesn't Work Is the First Step Toward Learning What Does Work

Abstract: Duncan Watts shares insights from his new book Everything is Obvious , including a recent randomized experiment testing online advertising with 1.6 million Yahoo! users. The researchers estimated that the additional revenue generated by the advertising was roughly four times the cost of the campaign in the short run, and possibly much higher over the long run. But what they also discovered was that almost all the effect was for older consumers—the ads were largely ineffective for people under 40. At first, this latter result seems like bad news. But the right way to think about it is that finding out that something doesn't work is also the first step toward learning what does work.