62 research outputs found
Sharing Non-Anonymous Costs of Multiple Resources Optimally
In cost sharing games, the existence and efficiency of pure Nash equilibria
fundamentally depends on the method that is used to share the resources' costs.
We consider a general class of resource allocation problems in which a set of
resources is used by a heterogeneous set of selfish users. The cost of a
resource is a (non-decreasing) function of the set of its users. Under the
assumption that the costs of the resources are shared by uniform cost sharing
protocols, i.e., protocols that use only local information of the resource's
cost structure and its users to determine the cost shares, we exactly quantify
the inefficiency of the resulting pure Nash equilibria. Specifically, we show
tight bounds on prices of stability and anarchy for games with only submodular
and only supermodular cost functions, respectively, and an asymptotically tight
bound for games with arbitrary set-functions. While all our upper bounds are
attained for the well-known Shapley cost sharing protocol, our lower bounds
hold for arbitrary uniform cost sharing protocols and are even valid for games
with anonymous costs, i.e., games in which the cost of each resource only
depends on the cardinality of the set of its users
Complexity and Approximation of the Continuous Network Design Problem
We revisit a classical problem in transportation, known as the continuous
(bilevel) network design problem, CNDP for short. We are given a graph for
which the latency of each edge depends on the ratio of the edge flow and the
capacity installed. The goal is to find an optimal investment in edge
capacities so as to minimize the sum of the routing cost of the induced Wardrop
equilibrium and the investment cost. While this problem is considered as
challenging in the literature, its complexity status was still unknown. We
close this gap showing that CNDP is strongly NP-complete and APX-hard, both on
directed and undirected networks and even for instances with affine latencies.
As for the approximation of the problem, we first provide a detailed analysis
for a heuristic studied by Marcotte for the special case of monomial latency
functions (Mathematical Programming, Vol.~34, 1986). Specifically, we derive a
closed form expression of its approximation guarantee for arbitrary sets S of
allowed latency functions. Second, we propose a different approximation
algorithm and show that it has the same approximation guarantee. As our final
-- and arguably most interesting -- result regarding approximation, we show
that using the better of the two approximation algorithms results in a strictly
improved approximation guarantee for which we give a closed form expression.
For affine latencies, e.g., this algorithm achieves a 1.195-approximation which
improves on the 5/4 that has been shown before by Marcotte. We finally discuss
the case of hard budget constraints on the capacity investment.Comment: 27 page
Strong Nash Equilibria in Games with the Lexicographical Improvement Property
We introduce a class of finite strategic games with the property that every
deviation of a coalition of players that is profitable to each of its members
strictly decreases the lexicographical order of a certain function defined on
the set of strategy profiles. We call this property the Lexicographical
Improvement Property (LIP) and show that it implies the existence of a
generalized strong ordinal potential function. We use this characterization to
derive existence, efficiency and fairness properties of strong Nash equilibria.
We then study a class of games that generalizes congestion games with
bottleneck objectives that we call bottleneck congestion games. We show that
these games possess the LIP and thus the above mentioned properties. For
bottleneck congestion games in networks, we identify cases in which the
potential function associated with the LIP leads to polynomial time algorithms
computing a strong Nash equilibrium. Finally, we investigate the LIP for
infinite games. We show that the LIP does not imply the existence of a
generalized strong ordinal potential, thus, the existence of SNE does not
follow. Assuming that the function associated with the LIP is continuous,
however, we prove existence of SNE. As a consequence, we prove that bottleneck
congestion games with infinite strategy spaces and continuous cost functions
possess a strong Nash equilibrium
Packing a Knapsack of Unknown Capacity
We study the problem of packing a knapsack without knowing its capacity.
Whenever we attempt to pack an item that does not fit, the item is discarded;
if the item fits, we have to include it in the packing. We show that there is
always a policy that packs a value within factor 2 of the optimum packing,
irrespective of the actual capacity. If all items have unit density, we achieve
a factor equal to the golden ratio. Both factors are shown to be best possible.
In fact, we obtain the above factors using packing policies that are universal
in the sense that they fix a particular order of the items and try to pack the
items in this order, independent of the observations made while packing. We
give efficient algorithms computing these policies. On the other hand, we show
that, for any alpha>1, the problem of deciding whether a given universal policy
achieves a factor of alpha is coNP-complete. If alpha is part of the input, the
same problem is shown to be coNP-complete for items with unit densities.
Finally, we show that it is coNP-hard to decide, for given alpha, whether a set
of items admits a universal policy with factor alpha, even if all items have
unit densities
Finding all minimal curb sets
Sets closed under rational behavior were introduced by Basu and Weibull (1991) as subsets of the strategy space that contain all best replies to all strategy profiles in the set. We here consider a more restrictive notion of closure under rational behavior: a subset of the strategy space is strongly closed under rational behavior, or sCURB, if it contains all best replies to all probabilistic beliefs over the set. We present an algorithm that computes all minimal sCURB sets in any given finite game. Runtime measurements on two-player games (where the concepts of CURB and sCURB coincide) show that the algorithm is considerably faster than the earlier developed algorithm, that of Benisch et al. (2006)
Optimal Impartial Selection
This is the final version of the article. It first appeared from Society for Industrial and Applied Mathematics via http://dx.doi.org/10.1137/140995775We study a fundamental problem in social choice theory, the selection of a member of a set of agents based on impartial nominations by agents from that set. Studied previously by Alon et al. [Proceedings of TARK, 2011, pp. 101--110] and by Holzman and Moulin [Econometrica, 81 (2013), pp. 173--196], this problem arises when representatives are selected from within a group or when publishing or funding decisions are made based on a process of peer review. Our main result concerns a randomized mechanism that in expectation selects an agent with at least half the maximum number of nominations. This is best possible subject to impartiality and resolves a conjecture of Alon et al. Further results are given for the case where some agent receives many nominations and the case where each agent casts at least one nomination
Computing all Wardrop Equilibria parametrized by the Flow Demand
We develop an algorithm that computes for a given undirected or directed
network with flow-dependent piece-wise linear edge cost functions all Wardrop
equilibria as a function of the flow demand. Our algorithm is based on
Katzenelson's homotopy method for electrical networks. The algorithm uses a
bijection between vertex potentials and flow excess vectors that is piecewise
linear in the potential space and where each linear segment can be interpreted
as an augmenting flow in a residual network. The algorithm iteratively
increases the excess of one or more vertex pairs until the bijection reaches a
point of non-differentiability. Then, the next linear region is chosen in a
Simplex-like pivot step and the algorithm proceeds. We first show that this
algorithm correctly computes all Wardrop equilibria in undirected
single-commodity networks along the chosen path of excess vectors. We then
adapt our algorithm to also work for discontinuous cost functions which allows
to model directed edges and/or edge capacities. Our algorithm is
output-polynomial in non-degenerate instances where the solution curve never
hits a point where the cost function of more than one edge becomes
non-differentiable. For degenerate instances we still obtain an
output-polynomial algorithm computing the linear segments of the bijection by a
convex program. The latter technique also allows to handle multiple
commodities
Maximizing a Submodular Function with Bounded Curvature under an Unknown Knapsack Constraint
This paper studies the problem of maximizing a monotone submodular function
under an unknown knapsack constraint. A solution to this problem is a policy
that decides which item to pack next based on the past packing history. The
robustness factor of a policy is the worst case ratio of the solution obtained
by following the policy and an optimal solution that knows the knapsack
capacity. We develop an algorithm with a robustness factor that is decreasing
in the curvature of the submodular function. For the extreme cases
corresponding to a modular objective, it matches a previously known and best
possible robustness factor of . For the other extreme case of it
yields a robustness factor of improving over the best previously
known robustness factor of
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