Network creation games model the autonomous formation of an interconnected system of selfish users. In particular, when the network will serve as a digital communication infrastructure, each user is identified by a node of the network, and contributes to the build-up process by strategically balancing between her building cost (i.e., the number of links she personally activates in the network) and her usage cost (i.e., some function of the distance in the sought network to the other players). When the corresponding game is analyzed, the generally adopted assumption is that players have a common and complete information about the evolving network topology, which is quite unrealistic though, due to the massive size this may have in practice. In this paper, we thus relax this assumption, by instead letting the players have only a partial knowledge of the network. To this respect, we make use of three popular traceroute-based knowledge models used in network discovering (i.e., the activity of reconstructing the topology of an unknown network through queries at its nodes), namely: (i) distance vector, (ii) shortest-path tree view, and (iii) layered view. For all these models, we provide exhaustive answers to the canonical algorithmic game theoretic questions: convergence, computational complexity for a player of selecting a best response, and tight bounds to the price of anarchy, all of them computed w.r.t. a suitable (and unifying) equilibrium concept. © 2014 Springer International Publishing Switzerland.
Network creation games with traceroute-based strategies
Proietti G
2014
Abstract
Network creation games model the autonomous formation of an interconnected system of selfish users. In particular, when the network will serve as a digital communication infrastructure, each user is identified by a node of the network, and contributes to the build-up process by strategically balancing between her building cost (i.e., the number of links she personally activates in the network) and her usage cost (i.e., some function of the distance in the sought network to the other players). When the corresponding game is analyzed, the generally adopted assumption is that players have a common and complete information about the evolving network topology, which is quite unrealistic though, due to the massive size this may have in practice. In this paper, we thus relax this assumption, by instead letting the players have only a partial knowledge of the network. To this respect, we make use of three popular traceroute-based knowledge models used in network discovering (i.e., the activity of reconstructing the topology of an unknown network through queries at its nodes), namely: (i) distance vector, (ii) shortest-path tree view, and (iii) layered view. For all these models, we provide exhaustive answers to the canonical algorithmic game theoretic questions: convergence, computational complexity for a player of selecting a best response, and tight bounds to the price of anarchy, all of them computed w.r.t. a suitable (and unifying) equilibrium concept. © 2014 Springer International Publishing Switzerland.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.