2,806 research outputs found
Using weight decision for decreasing the price of anarchy in selfish bin packing games
A selfish bin packing game is a variant of the classical bin packing problem in a game theoretic setting. In our model the items have not only a size but also a nonnegative weight. Each item plays the role of a selfish agent, and any agent/item pays some cost for being in a bin. The cost of a bin is 1, and this cost is shared among the items being in the bin, proportionally to their weight. A packing of the items into bins is called a Nash equilibrium if no item can decrease its cost by moving to another bin. In this paper we present two different settings for the weights which provide better values for the price of anarchy (PoA) than previous settings investigated so far. The improved PoA is not bigger than 16/11 approximate to 1.4545. Moreover, we give a general lower bound for the price of anarchy which holds for all possible choices of the weights. (C) 2019 Elsevier B.V. All rights reserved
On Colorful Bin Packing Games
We consider colorful bin packing games in which selfish players control a set
of items which are to be packed into a minimum number of unit capacity bins.
Each item has one of colors and cannot be packed next to an item of
the same color. All bins have the same unitary cost which is shared among the
items it contains, so that players are interested in selecting a bin of minimum
shared cost. We adopt two standard cost sharing functions: the egalitarian cost
function which equally shares the cost of a bin among the items it contains,
and the proportional cost function which shares the cost of a bin among the
items it contains proportionally to their sizes. Although, under both cost
functions, colorful bin packing games do not converge in general to a (pure)
Nash equilibrium, we show that Nash equilibria are guaranteed to exist and we
design an algorithm for computing a Nash equilibrium whose running time is
polynomial under the egalitarian cost function and pseudo-polynomial for a
constant number of colors under the proportional one. We also provide a
complete characterization of the efficiency of Nash equilibria under both cost
functions for general games, by showing that the prices of anarchy and
stability are unbounded when while they are equal to 3 for black and
white games, where . We finally focus on games with uniform sizes (i.e.,
all items have the same size) for which the two cost functions coincide. We
show again a tight characterization of the efficiency of Nash equilibria and
design an algorithm which returns Nash equilibria with best achievable
performance
A Best Cost-Sharing Rule for Selfish Bin Packing
In selfish bin packing, each item is regarded as a player, who aims to
minimize the cost-share by choosing a bin it can fit in. To have a least number
of bins used, cost-sharing rules play an important role. The currently best
known cost sharing rule has a lower bound on larger than 1.45, while a
general lower bound 4/3 on applies to any cost-sharing rule under which
no items have incentive unilaterally moving to an empty bin. In this paper, we
propose a novel and simple rule with a matching the lower bound, thus
completely resolving this game. The new rule always admits a Nash equilibrium
and its is one. Furthermore, the well-known bin packing algorithm
(Best-Fit Decreasing) is shown to achieve a strong equilibrium, implying that a
stable packing with an asymptotic approximation ratio of can be produced
in polynomial time
A tight lower bound for an online hypercube packing problem and bounds for prices of anarchy of a related game
We prove a tight lower bound on the asymptotic performance ratio of
the bounded space online -hypercube bin packing problem, solving an open
question raised in 2005. In the classic -hypercube bin packing problem, we
are given a sequence of -dimensional hypercubes and we have an unlimited
number of bins, each of which is a -dimensional unit hypercube. The goal is
to pack (orthogonally) the given hypercubes into the minimum possible number of
bins, in such a way that no two hypercubes in the same bin overlap. The bounded
space online -hypercube bin packing problem is a variant of the
-hypercube bin packing problem, in which the hypercubes arrive online and
each one must be packed in an open bin without the knowledge of the next
hypercubes. Moreover, at each moment, only a constant number of open bins are
allowed (whenever a new bin is used, it is considered open, and it remains so
until it is considered closed, in which case, it is not allowed to accept new
hypercubes). Epstein and van Stee [SIAM J. Comput. 35 (2005), no. 2, 431-448]
showed that is and , and conjectured that
it is . We show that is in fact . To
obtain this result, we elaborate on some ideas presented by those authors, and
go one step further showing how to obtain better (offline) packings of certain
special instances for which one knows how many bins any bounded space algorithm
has to use. Our main contribution establishes the existence of such packings,
for large enough , using probabilistic arguments. Such packings also lead to
lower bounds for the prices of anarchy of the selfish -hypercube bin packing
game. We present a lower bound of for the pure price of
anarchy of this game, and we also give a lower bound of for
its strong price of anarchy
Selfish Bin Covering
In this paper, we address the selfish bin covering problem, which is greatly
related both to the bin covering problem, and to the weighted majority game.
What we mainly concern is how much the lack of coordination harms the social
welfare. Besides the standard PoA and PoS, which are based on Nash equilibrium,
we also take into account the strong Nash equilibrium, and several other new
equilibria. For each equilibrium, the corresponding PoA and PoS are given, and
the problems of computing an arbitrary equilibrium, as well as approximating
the best one, are also considered.Comment: 16 page
Generalized selfish bin packing
Standard bin packing is the problem of partitioning a set of items with
positive sizes no larger than 1 into a minimum number of subsets (called bins)
each having a total size of at most 1. In bin packing games, an item has a
positive weight, and given a valid packing or partition of the items, each item
has a cost or a payoff associated with it. We study a class of bin packing
games where the payoff of an item is the ratio between its weight and the total
weight of items packed with it, that is, the cost sharing is based linearly on
the weights of items. We study several types of pure Nash equilibria: standard
Nash equilibria, strong equilibria, strictly Pareto optimal equilibria, and
weakly Pareto optimal equilibria. We show that any game of this class admits
all these types of equilibria. We study the (asymptotic) prices of anarchy and
stability (PoA and PoS) of the problem with respect to these four types of
equilibria, for the two cases of general weights and of unit weights. We show
that while the case of general weights is strongly related to the well-known
First Fit algorithm, and all the four PoA values are equal to 1.7, this is not
true for unit weights. In particular, we show that all of them are strictly
below 1.7, the strong PoA is equal to approximately 1.691 (another well-known
number in bin packing) while the strictly Pareto optimal PoA is much lower. We
show that all the PoS values are equal to 1, except for those of strong
equilibria, which is equal to 1.7 for general weights, and to approximately
1.611824 for unit weights. This last value is not known to be the (asymptotic)
approximation ratio of any well-known algorithm for bin packing. Finally, we
study convergence to equilibria
Enforcing efficient equilibria in network design games via subsidies
The efficient design of networks has been an important engineering task that
involves challenging combinatorial optimization problems. Typically, a network
designer has to select among several alternatives which links to establish so
that the resulting network satisfies a given set of connectivity requirements
and the cost of establishing the network links is as low as possible. The
Minimum Spanning Tree problem, which is well-understood, is a nice example.
In this paper, we consider the natural scenario in which the connectivity
requirements are posed by selfish users who have agreed to share the cost of
the network to be established according to a well-defined rule. The design
proposed by the network designer should now be consistent not only with the
connectivity requirements but also with the selfishness of the users.
Essentially, the users are players in a so-called network design game and the
network designer has to propose a design that is an equilibrium for this game.
As it is usually the case when selfishness comes into play, such equilibria may
be suboptimal. In this paper, we consider the following question: can the
network designer enforce particular designs as equilibria or guarantee that
efficient designs are consistent with users' selfishness by appropriately
subsidizing some of the network links? In an attempt to understand this
question, we formulate corresponding optimization problems and present positive
and negative results.Comment: 30 pages, 7 figure
Packing, Scheduling and Covering Problems in a Game-Theoretic Perspective
Many packing, scheduling and covering problems that were previously
considered by computer science literature in the context of various
transportation and production problems, appear also suitable for describing and
modeling various fundamental aspects in networks optimization such as routing,
resource allocation, congestion control, etc. Various combinatorial problems
were already studied from the game theoretic standpoint, and we attempt to
complement to this body of research.
Specifically, we consider the bin packing problem both in the classic and
parametric versions, the job scheduling problem and the machine covering
problem in various machine models. We suggest new interpretations of such
problems in the context of modern networks and study these problems from a game
theoretic perspective by modeling them as games, and then concerning various
game theoretic concepts in these games by combining tools from game theory and
the traditional combinatorial optimization. In the framework of this research
we introduce and study models that were not considered before, and also improve
upon previously known results.Comment: PhD thesi
- …