  Much work in AI deals with the selection of proper actions in a given (known
or unknown) environment. However, the way to select a proper action when facing
other agents is quite unclear. Most work in AI adopts classical game-theoretic
equilibrium analysis to predict agent behavior in such settings. This approach
however does not provide us with any guarantee for the agent. In this paper we
introduce competitive safety analysis. This approach bridges the gap between
the desired normative AI approach, where a strategy should be selected in order
to guarantee a desired payoff, and equilibrium analysis. We show that a safety
level strategy is able to guarantee the value obtained in a Nash equilibrium,
in several classical computer science settings. Then, we discuss the concept of
competitive safety strategies, and illustrate its use in a decentralized load
balancing setting, typical to network problems. In particular, we show that
when we have many agents, it is possible to guarantee an expected payoff which
is a factor of 8/9 of the payoff obtained in a Nash equilibrium. Our discussion
of competitive safety analysis for decentralized load balancing is further
developed to deal with many communication links and arbitrary speeds. Finally,
we discuss the extension of the above concepts to Bayesian games, and
illustrate their use in a basic auctions setup.
