Abstract:We initiate the study of the destruction or adversary model (Kliemann 2010) using the swap equilibrium (SE) stability concept (Alon et al., 2010). The destruction model is a network formation game incorporating the robustness of a network under a more or less targeted attack. In addition to bringing in the SE concept, we extend the model from an attack on the edges to an attack on the vertices of the network. We prove structural results and linear upper bounds or super-linear lower bounds on the social cost of SE under different attack scenarios. For the case that the vertex to be destroyed is chosen uniformly at random from the set of max-sep vertices (i.e., where each causes a maximum number of separated player pairs), we show that there is no tree SE with only one max-sep vertex. We conjecture that there is no tree SE at all. On the other hand, we show that for the uniform measure, all SE are trees (unless two-connected). This opens a new research direction asking where the transition from "no cycle" to "at least one cycle" occurs when gradually concentrating the measure on the max-sep vertices.
We study the price of anarchy in a class of graph coloring games (a subclass of polymatrix common-payoff games). In those games, players are vertices of an undirected, simple graph, and the strategy space of each player is the set of colors from 1 to k. A tight bound on the price of anarchy of k k´1 is known (Hoefer 2007, Kun et al. 2013, for the case that each player's payoff is the number of her neighbors with different color than herself. The study of more complex payoff functions was left as an open problem.We compute payoff for a player by determining the distance of her color to the color of each of her neighbors, applying a non-negative, real-valued, concave function f to each of those distances, and then summing up the resulting values. This includes the payoff functions suggested by Kun et al. (2013) for future work as special cases.Denote f˚the maximum value that f attains on the possible distances 0, . . . , k´1. We prove an upper bound of 2 on the price of anarchy for concave functions f that are non-decreasing or which assume f˚at a distance on or below k 2 . Matching lower bounds are given for the monotone case and for the case that f˚is assumed in k 2 for even k. For general concave functions, we prove an upper bound of 3. We use a simple but powerful technique: we obtain an upper bound of λ ě 1 on the price of anarchy if we manage to give a splitting λ 1 + . . .The discovery of working splittings can be supported by computer experiments. We show how, once we have an idea what kind of splittings work, this technique helps in giving simple proofs, which mainly work by case distinctions, algebraic manipulations, and real calculus.Graph coloring, especially in distributed and game-theoretic settings, is often used to model spectrum sharing scenarios, such as the selection of WLAN frequencies. For such an application, our framework would allow to express a dependence of the degree of radio interference on the distance between two frequencies. Model, Notation, Basic NotionsLet G = (V, E) be an undirected, simple graph without any isolated vertices (n := |V| and m := |E|) and k P N ě2 . For v P V denote N(v) := {w P V ; {v, w} P E} the set of its neighbors and deg(v) := |N(v)| its degree. A function c : V ÝÑ [k] is called a k-coloring or coloring, where [k] = {1, . . . , k}. Vertices of the graph represent players, with the set of colors [k] being the strategy space for each player. We sometimes call the set [k] the spectrum. Clearly, k-colorings are exactly the strategy profiles of this game. 1 Given a coloring c, define the payoff for player v aswhere f is a non-negative, real-valued function defined on [0, k] (choosing this domain instead of {0, . . . , k´1} is technically easier). Given such f , we denote f˚:= max iPD f (i) the maximum value that f attains on the possible distances D := {0, . . . , k´1} between two colors, and D˚( f ) := {i P D ; f (i) = f˚} the set of distances where f˚is attained. We call f (|c(v)´c(w)|) the contribution of edge {v, w}. So f˚is an upper bound on the contribution of...
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