  Today's multiagent systems have grown too complex to rely on centralized
controllers, prompting increasing interest in the design of distributed
algorithms. In this respect, game theory has emerged as a valuable tool to
complement more traditional techniques. The fundamental idea behind this
approach is the assignment of agents' local cost functions, such that their
selfish minimization attains, or is provably close to, the global objective.
Any algorithm capable of computing an equilibrium of the corresponding game
inherits an approximation ratio that is, in the worst case, equal to its
price-of-anarchy. Therefore, a successful application of the game design
approach hinges on the possibility to quantify and optimize the equilibrium
performance.
  Toward this end, we introduce the notion of generalized smoothness, and show
that the resulting efficiency bounds are significantly tighter compared to
those obtained using the traditional smoothness approach. Leveraging this
newly-introduced notion, we quantify the equilibrium performance for the class
of local resource allocation games. Finally, we show how the agents' local
decision rules can be designed in order to optimize the efficiency of the
corresponding equilibria, by means of a tractable linear program.
