The past decade has witnessed a momentous transformation in the way people interact and exchange information with each other. Content is now co-produced, shared, classified, and rated on the Web by millions of people, while attention has become the ephemeral and valuable resource that everyone seeks to acquire. I will describe our research on the interplay between popularity, novelty and collective attention in the Web, as well as the role that attention plays in crowdsourcing.
About the Speaker:
Bernardo Huberman is a Senior HP Fellow and Director of the Social Computing Lab at Hewlett Packard Laboratories. He is also a Consulting Professor in the Department of Applied Physics at Stanford University. For a number of years he worked in statistical physics and dynamical systems and then moved on to study large distributed systems, both human and artificial. In that context he designed and implemented market mechanisms for resource allocation, and investigated the phenomenon of cooperation in large groups and organizations.
Bernardo’s research into the phenomenon of the web led to the discovery of a number of strong regularities, which are described in this book “The Laws of the Web: Patterns in the Ecology of Information” (MIT Press). Presently, his work centers on the phenomenon of social attention in the design of novel mechanisms for discovering and aggregating information in distributed systems.
Dr. Huberman received his Ph.D. in Physics from the University of Pennsylvania. He is one of the creators of the field of ecology of computation and edited “The Laws of the Web: Patterns in the Ecology of Information” (MIT Press, 2001). Bernardo is a Fellow of the American Physical Society, a Fellow of the American Association for the Advancement of Science and Fellow of the Japan Society for the Promotion of Science, as well as a faculty member in the Symbolic Systems Program at Stanford University. He is co-winner of the 1990 CECOIA prize in Economics and Artificial Intelligence and shared the IBM Prize of the Society for Computational Economics.