Transit Price Negotiation: Decentralized Learning of Optimal Strategies with Incomplete Information

We present a distributed learning algorithm for optimising transit prices in a negotiation problem in the inter-domain routing framework. We present a combined game theoretic and distributed algorithmic analysis, where the notion of Nash equilibrium with the first approach model meets the notion of stability in the second. We show that minimum cost providers can learn how to strategically set their prices according to a Nash equilibrium; even when assuming incomplete information. We validate our theoretic model by simulations confirming the expected outcome.