arXiv subjects

Elmira Shirazi Sheykhdarabadi

Publications and source records attributed to Elmira Shirazi Sheykhdarabadi.

2 recordsLinked to original sources

arXiv2016

Swap Equilibria under Link and Vertex Destruction

We initiate the study of the \emph{destruction model} (\aka \emph{adversary model}) introduced by Kliemann (2010), using the stability concept of \emph{swap equilibrium} introduced by 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 swap equilibrium (SE) concept, we extend the model from an attack on the edges of the network to an attack on its vertices. Vertex destruction can generally cause more harm and tends to be more difficult to analyze. We prove structural results and linear upper bounds or super-linear lower bounds on the social cost of SE under different attack scenarios. The most complex case is when the vertex to be destroyed is chosen uniformly at random from the set of those vertices where each causes a maximum number of player pairs to be separated (called a max-sep vertex). We prove a lower bound on the social cost of $Ω(n^{3/2})$ for this case and initiate an understanding of the structural properties of SE in this scenario. Namely, we prove that there is no SE that is a tree and has only one max-sep vertex. We conjecture that this result can be generalized, in particular we conjecture that there is no SE that is a tree. On the other hand, we prove that if the vertex to be destroyed is chosen uniformly at random from the set of \emph{all} vertices, then each SE is a tree (unless it is two-connected). Our conjecture would imply that moving from the uniform probability measure to a measure concentrated on the max-sep vertices, means moving from no SE having a cycle (unless two-connected) to each SE having a cycle. This would ask for a more detailed study of this transition in future work.

cs.GT↗

arXiv2015

Price of Anarchy for Graph Coloring Games with Concave Payoff

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 $\frac{k}{k-1}$ 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 $0,\dots,k-1$. 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 $\lfloor\frac{k}{2}\rfloor$. Matching lower bounds are given for the monotone case and for the case that $f^*$ is assumed in $\frac{k}{2}$ 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 $λ\geq 1$ on the price of anarchy if we manage to give a splitting $λ_1 + \dots + λ_k = λ$ such that $\sum_{s=1}^k λ_s \cdot f(|s-p|) \geq f^*$ for all $p \in \{1,\dots,k\}$. 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.

cs.GT↗