824 research outputs found
A Stackelberg Strategy for Routing Flow over Time
Routing games are used to to understand the impact of individual users'
decisions on network efficiency. Most prior work on routing games uses a
simplified model of network flow where all flow exists simultaneously, and
users care about either their maximum delay or their total delay. Both of these
measures are surrogates for measuring how long it takes to get all of a user's
traffic through the network. We attempt a more direct study of how competition
affects network efficiency by examining routing games in a flow over time
model. We give an efficiently computable Stackelberg strategy for this model
and show that the competitive equilibrium under this strategy is no worse than
a small constant times the optimal, for two natural measures of optimality
Routing Games with Progressive Filling
Max-min fairness (MMF) is a widely known approach to a fair allocation of
bandwidth to each of the users in a network. This allocation can be computed by
uniformly raising the bandwidths of all users without violating capacity
constraints. We consider an extension of these allocations by raising the
bandwidth with arbitrary and not necessarily uniform time-depending velocities
(allocation rates). These allocations are used in a game-theoretic context for
routing choices, which we formalize in progressive filling games (PFGs).
We present a variety of results for equilibria in PFGs. We show that these
games possess pure Nash and strong equilibria. While computation in general is
NP-hard, there are polynomial-time algorithms for prominent classes of
Max-Min-Fair Games (MMFG), including the case when all users have the same
source-destination pair. We characterize prices of anarchy and stability for
pure Nash and strong equilibria in PFGs and MMFGs when players have different
or the same source-destination pairs. In addition, we show that when a designer
can adjust allocation rates, it is possible to design games with optimal strong
equilibria. Some initial results on polynomial-time algorithms in this
direction are also derived
Load Balancing Congestion Games and their Asymptotic Behavior
A central question in routing games has been to establish conditions for the
uniqueness of the equilibrium, either in terms of network topology or in terms
of costs. This question is well understood in two classes of routing games. The
first is the non-atomic routing introduced by Wardrop on 1952 in the context of
road traffic in which each player (car) is infinitesimally small; a single car
has a negligible impact on the congestion. Each car wishes to minimize its
expected delay. Under arbitrary topology, such games are known to have a convex
potential and thus a unique equilibrium. The second framework is splitable
atomic games: there are finitely many players, each controlling the route of a
population of individuals (let them be cars in road traffic or packets in the
communication networks). In this paper, we study two other frameworks of
routing games in which each of several players has an integer number of
connections (which are population of packets) to route and where there is a
constraint that a connection cannot be split. Through a particular game with a
simple three link topology, we identify various novel and surprising properties
of games within these frameworks. We show in particular that equilibria are non
unique even in the potential game setting of Rosenthal with strictly convex
link costs. We further show that non-symmetric equilibria arise in symmetric
networks. I. INTRODUCTION A central question in routing games has been to
establish conditions for the uniqueness of the equilibria, either in terms of
the network topology or in terms of the costs. A survey on these issues is
given in [1]. The question of uniqueness of equilibria has been studied in two
different frameworks. The first, which we call F1, is the non-atomic routing
introduced by Wardrop on 1952 in the context of road traffic in which each
player (car) is infinitesimally small; a single car has a negligible impact on
the congestion. Each car wishes to minimize its expected delay. Under arbitrary
topology, such games are known to have a convex potential and thus have a
unique equilibrium [2]. The second framework, denoted by F2, is splitable
atomic games. There are finitely many players, each controlling the route of a
population of individuals. This type of games have already been studied in the
context of road traffic by Haurie and Marcotte [3] but have become central in
the telecom community to model routing decisions of Internet Service Providers
that can decide how to split the traffic of their subscribers among various
routes so as to minimize network congestion [4]. In this paper we study
properties of equilibria in two other frameworks of routing games which exhibit
surprisin
Routing Games over Time with FIFO policy
We study atomic routing games where every agent travels both along its
decided edges and through time. The agents arriving on an edge are first lined
up in a \emph{first-in-first-out} queue and may wait: an edge is associated
with a capacity, which defines how many agents-per-time-step can pop from the
queue's head and enter the edge, to transit for a fixed delay. We show that the
best-response optimization problem is not approximable, and that deciding the
existence of a Nash equilibrium is complete for the second level of the
polynomial hierarchy. Then, we drop the rationality assumption, introduce a
behavioral concept based on GPS navigation, and study its worst-case efficiency
ratio to coordination.Comment: Submission to WINE-2017 Deadline was August 2nd AoE, 201
Learning an Unknown Network State in Routing Games
We study learning dynamics induced by myopic travelers who repeatedly play a
routing game on a transportation network with an unknown state. The state
impacts cost functions of one or more edges of the network. In each stage,
travelers choose their routes according to Wardrop equilibrium based on public
belief of the state. This belief is broadcast by an information system that
observes the edge loads and realized costs on the used edges, and performs a
Bayesian update to the prior stage's belief. We show that the sequence of
public beliefs and edge load vectors generated by the repeated play converge
almost surely. In any rest point, travelers have no incentive to deviate from
the chosen routes and accurately learn the true costs on the used edges.
However, the costs on edges that are not used may not be accurately learned.
Thus, learning can be incomplete in that the edge load vectors at rest point
and complete information equilibrium can be different. We present some
conditions for complete learning and illustrate situations when such an outcome
is not guaranteed
Bottleneck Routing Games with Low Price of Anarchy
We study {\em bottleneck routing games} where the social cost is determined
by the worst congestion on any edge in the network. In the literature,
bottleneck games assume player utility costs determined by the worst congested
edge in their paths. However, the Nash equilibria of such games are inefficient
since the price of anarchy can be very high and proportional to the size of the
network. In order to obtain smaller price of anarchy we introduce {\em
exponential bottleneck games} where the utility costs of the players are
exponential functions of their congestions. We find that exponential bottleneck
games are very efficient and give a poly-log bound on the price of anarchy:
, where is the largest path length in the
players' strategy sets and is the set of edges in the graph. By adjusting
the exponential utility costs with a logarithm we obtain games whose player
costs are almost identical to those in regular bottleneck games, and at the
same time have the good price of anarchy of exponential games.Comment: 12 page
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