Impossibility of distributed consensus with one faulty process
Journal of the ACM (JACM)
Reaching Agreement in the Presence of Faults
Journal of the ACM (JACM)
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Extensive games with possibly unaware players
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Beyond nash equilibrium: solution concepts for the 21st century
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Iterated regret minimization: a new solution concept
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Lower bounds on implementing robust and resilient mediators
TCC'08 Proceedings of the 5th conference on Theory of cryptography
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An often useful way to think of security is as a game between an adversary and the “good” participants in the protocol. Game theorists try to understand games in terms of solutionconcepts; essentially, this is a rule for predicting how the game will be played. The most commonly used solution concept in game theory is Nashequilibrium. Intuitively, a Nash equilibrium is a strategyprofile (a collection of strategies, one for each player in the game) such that no player can do better by deviating. The intuition behind Nash equilibrium is that it represent a possible steady state of play. It is a fixed point where each player holds correct beliefs about what other players are doing, and plays a best response to those beliefs. Part of what makes Nash equilibrium so attractive is that in games where each player has only finitely many possible deterministic strategies, and we allow mixed (i.e., randomized) strategies, there is guaranteed to be a Nash equilibrium [11] (this was, in fact, the key result of Nash's thesis).