2 research outputs found
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