We seek to study control strategies for multi-agent systems in which agents have different
information and possibly different objectives. We organize these decision-making settings
as cooperative or non-cooperative games. The objective is to design the 'rules of the game'
in such a way that agents acting selfishly on their own behalf arrive at promoting socially
desirable strategies. We will focus on 'combinatorial auctions' as the paradigmatic game.
Second, we want to study control strategies with low computational complexity, which rules
out dynamic programming or global optimization. The approach is to reduce complexity by
resoring to 'learning', based on simulation or experiments, in order to estimate the value
of a particular strategy.
Examples to test our approach will be drawn from bandwidth trading in communication networks.
