MANIAC Challenge: The Wolf-pack strategy

MANIAC Challenge raises a problem of game theory, different players strategies intertwine and the success of any player is dependent on the actions of all players in the system. A truly fair scenario is when all the strategies are identical, all the nodes co-operate and they all equally share the rewards and risks that come with every transfer. A successful strategy is one that tries to diverge from the equilibrium to maximize its own gains and it manages to do so. We propose the wolf-pack strategy. Unlike standard game-theory based strategies, our strategy does not penalize the nodes that diverge from fairness or from equilibrium, as we believe most nodes will do so in an attempt to get an advantage over the other nodes. The wolf-pack strategy will try to always find the most successful node or nodes and penalize them. We believe that just like in nature, a number of small predators can take down the bigger, more profitable ones. Furthermore during the Challenge we test two different strategies that provide completely opposite results. These offer a clear picture of what the best strategy is and the problems of the current system. Keywords— game theory, trust, ad-hoc networks, fairness.