KI-Net: "Kinetic description of emerging challenges in multiscale problems of natural sciences" a new NSF Research Network in Mathematical Sciences Winter School: An introduction to kinetic models in the emergence of complex behavior in social and economic systems Feb 18 - 21, 2013 The University of Texas at Austin The Institute for Computational Engineering and Sciences These are graduate level short courses in an introductory series of lectures on the derivation, analysis and simulations of network structures and kinetic aspects of complex systems models. Such models appear in problems that range from traffic, flocking dynamics, supply chain networks, information exchange or more general dynamics in networks. One of the goals consists into looking at the derivations and dynamics of statistical transients or flows in discrete and continuous probabilistic settings that give rise to statistical transport models. For information go to http://www.ki-net.umd.edu/ Kinetic theory for the emergence of complex behavior in social and economic systems Feb 21 - 24, 2013 Arizona State University Center for Social Dynamics and Complexity Kinetic theory describes the stochastic interaction of many particles or agents via high dimensional evolution equations of probability densities. Computationally tractable, low dimensional equations for macroscopic observables (emergence) are obtained via asymptotics for large time scales and many agents. This follows the recipe of Boltzmann's kinetic gas theory, leading to the basic equations of gas dynamics in the limit. In the social science context, this methodology has been extended from simple gas molecules to birds and fish to describe flocking, and to opinions to describe the evolution of rumors in a crowd. The main advantage of this theory is that in its core it is an accurate, analytic and explicit first principle theory and, when it works, it allows for an analytic description of Agent Based Simulations (ABS). It may also be used to relate popular aggregate models like population models in the social sciences or diffusion models in economy to their microscopic origins. Applying such methods in biology is well within the experience of most applied mathematicians - they typically know physics and therefore have an idea what the interesting questions for a fish swarm are. This is less the case for simulation models in social science and economics. As a result, mathematical applications in social sciences are often not very relevant and seem to be staying at a descriptive qualitative level. In essence the theory for emergent social phenomena is just emerging and the relationship between model, their purpose and data is unresolved. For information go to http://www.ki-net.umd.edu/