Small Worlds: The Dynamics of Networks between Order and Randomness: 36 (Princeton Studies in Complexity) - Tapa blanda

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Duncan J. Watts, is Associate Professor of Sociology at Columbia University and an external faculty member of the Santa Fe Institute. He holds a Ph.D. in theoretical and applied mechanics from Cornell University and is the author of "Six Degrees: The Science of A Connected Age". He lives in New York City.

De la contraportada

"Duncan Watts has created that rarity of rarities: a book with enough fascinating facts and stories to keep the casual reader turning the pages coupled with enough engaging detail to satisfy the most technically sophisticated reader. Thus, whether you are just curious about the world around you or eager to begin your own small-world research, this is the definitive guide to the fascinating and profound world of small-world networks."--William L. Ditto, Applied Chaos Laboratory, Georgia Institute of Technology

"A good book on a fascinating topic--why two widely separated people are often connected by a small number of steps from friend to friend. We do indeed live in a 'small world.' When something happens so often there must be a reason--Duncan Watts is looking for it."--Gilbert Strang, Department of Mathematics, Massachusetts Institute of Technology

"Duncan Watts's and Steve Strogatz's 1998 Nature paper on 'The collective dynamics of small-world networks' reinvigorated interest in the small-world phenomenon. Now, in Small Worlds, Watts follows up on this work with a detailed but accessible account of small-world networks that will appeal to both scientists and nonscientists. With examples ranging from the Kevin Bacon Game to models for the spread of diseases, Watts provides a clear description of how the structure of small-world networks can be characterized and a sense of how the interconnectivity of such networks can lead to intriguing dynamics. Be sure to tell your friends and their friends about this book."--J. J. Collins, Center for BioDynamics and Department of Biomedical Engineering, Boston University

"Enchanting! A voyage of exploration with fascinating byroads that yet brings the reader to powerful and useable conclusions. This work is worthy of Stanley Milgram exactly because Watts goes well beyond the original visualization while retaining its transparency."--Harrison White, Department of Sociology, Columbia University

"If you are a postgraduate looking to make your name or a seasoned researcher looking for new challenges, this book offers something rare: a chance to get in at the ground floor of a whole new area of research whose variety of exciting applications is exceeded only by their abundance."--Robert A. J. Matthews, Aston University, U.K.

"Small Worlds is outstanding. Watts begins with a simple observation: clustered networks, networks characterized by a large fraction of short ties and a small fraction of 'shortcuts' linking clusters with one another, appear in diverse settings and more frequently than might be expected. Watts then demonstrates that the dynamical behavior of these networks is highly sensitive to structure. The book is must reading, although not easy reading, for social scientists interested in networks, decision-making, and organizational design.(In other words, this is a high-investment, high-payoff book.)"--Marshall W. Meyer, The Wharton School, University of Pennsylvania

"This is a remarkably novel analysis, with implications for a broad range of scientific disciplines, including neurobiology, sociology, ecology, economics, and epidemiology. . . . The results are potentially profoundly important."--Simon A. Levin, Department of Ecology and Evolutionary Biology, Princeton University

"Theoretical research on social networks has been hampered by a lack of models which capture the essential properties of large numbers of graphs with only a few key parameters. All the dyads, triads and acyclic mappings which fill the social network literature lead merely to a long enumeration of special cases. The random graph models introduced by Watts provide a rich foundation for future analytical and empirical research. The applications to dynamics in part 2 illustrate the richness of these models and promise even more exciting work to come."--Larry Blume, Cornell University