Spyros C. Kontogiannis

The Structure and Complexity of Nash Equilibria for a Selfish Routing Game

In this work, we study the combinatorial structure and the computational complexity of Nash equilibria for a certain game that models selfish routing over a network consisting of m parallel links. We assume a collection of n users, each employing a mixed strategy, which is a probability distribution over links, to control the routing of its own assigned traffic. In a Nash equilibrium, each user selfishly routes its traffic on those links that minimize its expected latency cost, given the network congestion caused by the other users. The social cost of a Nash equilibrium is the expectation, over all random choices of the users, of the maximum, over all links, latency through a link.We embark on a systematic study of several algorithmic problems related to the computation of Nash equilibria for the selfish routing game we consider. In a nutshell, these problems relate to deciding the existence of a Nash equilibrium, constructing a Nash equilibrium with given support characteristics, constructing the worst Nash equilibrium (the one with maximum social cost), constructing the best Nash equilibrium (the one with minimum social cost), or computing the social cost of a (given) Nash equilibrium. Our work provides a comprehensive collection of efficient algorithms, hardness results (both as NP-hardness and #P-completeness results), and structural results for these algorithmic problems. Our results span and contrast a wide range of assumptions on the syntax of the Nash equilibria and on the parameters of the system.

Atomic congestion games among coalitions

We consider algorithmic questions concerning the existence, tractability, and quality of Nash equilibria, in atomic congestion games among users participating in selfish coalitions. We introduce a coalitional congestion model among atomic players and demonstrate many interesting similarities with the noncooperative case. For example, there exists a potential function proving the existence of pure Nash equilibria (PNE) in the unrelated parallel links setting; in the network setting, the finite improvement property collapses as soon as we depart from linear delays, but there is an exact potential (and thus PNE) for linear delays. The price of anarchy on identical parallel links demonstrates a quite surprising threshold behavior: It persists on being asymptotically equal to that in the case of the noncooperative KP-model, unless the number of coalitions is sublogarithmic. We also show crucial differences, mainly concerning the hardness of algorithmic problems that are solved efficiently in the noncooperative case. Although we demonstrate convergence to robust PNE, we also prove the hardness of computing them. On the other hand, we propose a generalized fully mixed Nash equilibrium that can be efficiently constructed in most cases. Finally, we propose a natural improvement policy and prove its convergence in pseudopolynomial time to PNE which are robust against (even dynamically forming) coalitions of small size.

Symmetry in network congestion games: pure equilibria and anarchy cost

We study computational and coordination efficiency issues of Nash equilibria in symmetric network congestion games. We first propose a simple and natural greedy method that computes a pure Nash equilibrium with respect to traffic congestion in a network. In this algorithm each user plays only once and allocates her traffic to a path selected via a shortest path computation. We then show that this algorithm works for series-parallel networks when users are identical or when users are of varying demands but have the same best response strategy for any initial network traffic. We also give constructions where the algorithm fails if either the above condition is violated (even for series-parallel networks) or the network is not series-parallel (even for identical users). Thus, we essentially indicate the limits of the applicability of this greedy approach. We also study the price of anarchy for the objective of maximum latency. We prove that for any network of m uniformly related links and for identical users, the price of anarchy is

The structure and complexity of Nash equilibria for a selfish routing game

{\it \Theta}({\frac{{\rm log} m}{{\rm log log} m}}

Efficient algorithms for constant well supported approximate equilibria in bimatrix games

).

Atomic selfish routing in networks: a survey

In this work, we study the combinatorial structure and the computational complexity of Nash equilibria for a certain game that models selfish routing over a network consisting of m parallel links. We assume a collection of n users, each employing a mixed strategy, which is a probability distribution over links, to control the routing of her own traffic. In a Nash equilibrium, each user selfishly routes her traffic on those links that minimize her expected latency cost, given the network congestion caused by the other users. The social cost of a Nash equilibrium is the expectation, over all random choices of the users, of the maximum, over all links, latency through a link.We embark on a systematic study of several algorithmic problems related to the computation of Nash equilibria for the selfish routing game we consider. In a nutshell, these problems relate to deciding the existence of a pure Nash equilibrium, constructing a Nash equilibrium, constructing the pure Nash equilibria of minimum and maximum social cost, and computing the social cost of a given mixed Nash equilibrium. Our work provides a comprehensive collection of efficient algorithms, hardness results, and structural results for these algorithmic problems. Our results span and contrast a wide range of assumptions on the syntax of the Nash equilibria and on the parameters of the system.

Distance Oracles for Time-Dependent Networks

In this work we study the tractability of well supported approximate Nash Equilibria (SuppNE in short) in bimatrix games. In view of the apparent intractability of constructing Nash Equilibria (NE in short) in polynomial time, even for bimatrix games, understanding the limitations of the approximability of the problem is of great importance. We initially prove that SuppNE are immune to the addition of arbitrary real vectors to the rows (columns) of the row (column) players payoff matrix. Consequently we propose a polynomial time algorithm (based on linear programming) that constructs a 0.5-SuppNE for arbitrary win lose games. We then parameterize our technique for win lose games, in order to apply it to arbitrary (normalized) bimatrix games. Indeed, this new technique leads to a weaker ϕ-SuppNE for win lose games, where ϕ = √5-1/2 is the golden ratio. Nevertheless, this parameterized technique extends nicely to a technique for arbitrary [0, 1]-bimatrix games, which assures a 0.658-SuppNE in polynomial time. To our knowledge, these are the first polynomial time algorithms providing e-SuppNE of normalized or win lose bimatrix games, for some nontrivial constant e ∈ [0, 1), bounded away from 1.

Approximability of symmetric bimatrix games and related experiments

In this survey we present some recent advances in the literature of atomic (mainly network) congestion games. The algorithmic questions that we are interested in have to do with the existence of pure Nash equilibria, the efficiency of their construction when they exist, as well as the gap of the best/worst (mixed in general) Nash equilibria from the social optima in such games, typically called the Price of Anarchy and the Price of Stability respectively.

Exploiting concavity in bimatrix games: new polynomially tractable subclasses

We present the first approximate distance oracle for sparse directed networks with time-dependent arc-travel-times determined by continuous, piecewise linear, positive functions possessing the FIFO property. Our approach precomputes