12 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
Pure Nash equilibria in restricted budget games
In budget games, players compete over resources with finite budgets. For every resource, a player has a specific demand and as a strategy, he chooses a subset of resources. If the total demand on a resource does not exceed its budget, the utility of each player who chose that resource equals his demand. Otherwise, the budget is shared proportionally. In the general case, pure Nash equilibria (NE) do not exist for such games. In this paper, we consider the natural classes of singleton and matroid budget games with additional constraints and show that for each, pure NE can be guaranteed. In addition, we introduce a lexicographical potential function to prove that every matroid budget game has an approximate pure NE which depends on the largest ratio between the different demands of each individual player
Approximate Pure Nash Equilibria in Weighted Congestion Games: Existence, Efficient Computation, and Structure
We consider structural and algorithmic questions related to the Nash dynamics
of weighted congestion games. In weighted congestion games with linear latency
functions, the existence of (pure Nash) equilibria is guaranteed by potential
function arguments. Unfortunately, this proof of existence is inefficient and
computing equilibria is such games is a {\sf PLS}-hard problem. The situation
gets worse when superlinear latency functions come into play; in this case, the
Nash dynamics of the game may contain cycles and equilibria may not even exist.
Given these obstacles, we consider approximate equilibria as alternative
solution concepts. Do such equilibria exist? And if so, can we compute them
efficiently?
We provide positive answers to both questions for weighted congestion games
with polynomial latency functions by exploiting an "approximation" of such
games by a new class of potential games that we call -games. This allows
us to show that these games have -approximate equilibria, where is the
maximum degree of the latency functions. Our main technical contribution is an
efficient algorithm for computing O(1)-approximate equilibria when is a
constant. For games with linear latency functions, the approximation guarantee
is for arbitrarily small ; for
latency functions with maximum degree , it is . The
running time is polynomial in the number of bits in the representation of the
game and . As a byproduct of our techniques, we also show the
following structural statement for weighted congestion games with polynomial
latency functions of maximum degree : polynomially-long sequences of
best-response moves from any initial state to a -approximate
equilibrium exist and can be efficiently identified in such games as long as
is constant.Comment: 31 page
Approximating Generalized Network Design under (Dis)economies of Scale with Applications to Energy Efficiency
In a generalized network design (GND) problem, a set of resources are
assigned to multiple communication requests. Each request contributes its
weight to the resources it uses and the total load on a resource is then
translated to the cost it incurs via a resource specific cost function. For
example, a request may be to establish a virtual circuit, thus contributing to
the load on each edge in the circuit. Motivated by energy efficiency
applications, recently, there is a growing interest in GND using cost functions
that exhibit (dis)economies of scale ((D)oS), namely, cost functions that
appear subadditive for small loads and superadditive for larger loads.
The current paper advances the existing literature on approximation
algorithms for GND problems with (D)oS cost functions in various aspects: (1)
we present a generic approximation framework that yields approximation results
for a much wider family of requests in both directed and undirected graphs; (2)
our framework allows for unrelated weights, thus providing the first
non-trivial approximation for the problem of scheduling unrelated parallel
machines with (D)oS cost functions; (3) our framework is fully combinatorial
and runs in strongly polynomial time; (4) the family of (D)oS cost functions
considered in the current paper is more general than the one considered in the
existing literature, providing a more accurate abstraction for practical energy
conservation scenarios; and (5) we obtain the first approximation ratio for GND
with (D)oS cost functions that depends only on the parameters of the resources'
technology and does not grow with the number of resources, the number of
requests, or their weights. The design of our framework relies heavily on
Roughgarden's smoothness toolbox (JACM 2015), thus demonstrating the possible
usefulness of this toolbox in the area of approximation algorithms.Comment: 39 pages, 1 figure. An extended abstract of this paper is to appear
in the 50th Annual ACM Symposium on the Theory of Computing (STOC 2018
DIRECT: A scalable approach for route guidance in Selfish Orienteering Problems
We address the problem of crowd congestion at venues like theme parks, museums and world expos by providing route guidance to multiple selfish users (with budget constraints) moving through the venue simultaneously. To represent these settings, we introduce the Selfish Orienteering Problem (SeOP) that combines two well studied problems from liter-ature, namely Orienteering Problem (OP) and Selfish Rout-ing (SR). OP is a single agent routing problem where the goal is to minimize latency (or maximize reward) in travers-ing a subset of nodes while respecting budget constraints. SR is a game between selfish agents looking for minimum latency routes from source to destination along edges of a network available to all agents. Thus, SeOP is a multi-agent planning problem where agents have selfish interests and in
Equilibrium Analysis of Customer Attraction Games
We introduce a game model called "customer attraction game" to demonstrate
the competition among online content providers. In this model, customers
exhibit interest in various topics. Each content provider selects one topic and
benefits from the attracted customers. We investigate both symmetric and
asymmetric settings involving agents and customers. In the symmetric setting,
the existence of pure Nash equilibrium (PNE) is guaranteed, but finding a PNE
is PLS-complete. To address this, we propose a fully polynomial time
approximation scheme to identify an approximate PNE. Moreover, the tight Price
of Anarchy (PoA) is established. In the asymmetric setting, we show the
nonexistence of PNE in certain instances and establish that determining its
existence is NP-hard. Nevertheless, we prove the existence of an approximate
PNE. Additionally, when agents select topics sequentially, we demonstrate that
finding a subgame-perfect equilibrium is PSPACE-hard. Furthermore, we present
the sequential PoA for the two-agent setting
A Characterization of Undirected Graphs Admitting Optimal Cost Shares
In a seminal paper, Chen, Roughgarden and Valiant studied cost sharing
protocols for network design with the objective to implement a low-cost Steiner
forest as a Nash equilibrium of an induced cost-sharing game. One of the most
intriguing open problems to date is to understand the power of budget-balanced
and separable cost sharing protocols in order to induce low-cost Steiner
forests. In this work, we focus on undirected networks and analyze topological
properties of the underlying graph so that an optimal Steiner forest can be
implemented as a Nash equilibrium (by some separable cost sharing protocol)
independent of the edge costs. We term a graph efficient if the above stated
property holds. As our main result, we give a complete characterization of
efficient undirected graphs for two-player network design games: an undirected
graph is efficient if and only if it does not contain (at least) one out of few
forbidden subgraphs. Our characterization implies that several graph classes
are efficient: generalized series-parallel graphs, fan and wheel graphs and
graphs with small cycles.Comment: 60 pages, 69 figures, OR 2017 Berlin, WINE 2017 Bangalor
Approximating Generalized Network Design under (Dis)economies of Scale with Applications to Energy Efficiency
In a generalized network design (GND) problem, a set of resources are assigned (non-exclusively) to multiple requests. Each request contributes its weight to the resources it uses and the total load on a resource is then translated to the cost it incurs via a resource-specific cost function. Motivated by energy efficiency applications, recently, there is a growing interest in GND using cost functions that exhibit (dis)economies of scale ((D)oS), namely, cost functions that appear subadditive for small loads and superadditive for larger loads. The current article advances the existing literature on approximation algorithms for GND problems with (D)oS cost functions in various aspects: (1) while the existing results are restricted to routing requests in undirected graphs, identifying the resources with the graph's edges, the current article presents a generic approximation framework that yields approximation results for a much wider family of requests (including various types of Steiner tree and Steiner forest requests) in both directed and undirected graphs, where the resources can be identified with either the edges or the vertices; (2) while the existing results assume that a request contributes the same weight to each resource it uses, our approximation framework allows for unrelated weights, thus providing the first non-trivial approximation for the problem of scheduling unrelated parallel machines with (D)oS cost functions; (3) while most of the existing approximation algorithms are based on convex programming, our approximation framework is fully combinatorial and runs in strongly polynomial time; (4) the family of (D)oS cost functions considered in the current article is more general than the one considered in the existing literature, providing a more accurate abstraction for practical energy conservation scenarios; and (5) we obtain the first approximation ratio for GND with (D)oS cost functions that depends only on the parameters of the resources' technology and does not grow with the number of resources, the number of requests, or their weights. The design of our approximation framework relies heavily on Roughgarden's smoothness toolbox [43], thus demonstrating the possible usefulness of this toolbox in the area of approximation algorithms.</p
Approximating Generalized Network Design under (Dis)economies of Scale with Applications to Energy Efficiency
In a generalized network design (GND) problem, a set of resources are assigned (non-exclusively) to multiple requests. Each request contributes its weight to the resources it uses and the total load on a resource is then translated to the cost it incurs via a resource-specific cost function. Motivated by energy efficiency applications, recently, there is a growing interest in GND using cost functions that exhibit (dis)economies of scale ((D)oS), namely, cost functions that appear subadditive for small loads and superadditive for larger loads. The current article advances the existing literature on approximation algorithms for GND problems with (D)oS cost functions in various aspects: (1) while the existing results are restricted to routing requests in undirected graphs, identifying the resources with the graph's edges, the current article presents a generic approximation framework that yields approximation results for a much wider family of requests (including various types of Steiner tree and Steiner forest requests) in both directed and undirected graphs, where the resources can be identified with either the edges or the vertices; (2) while the existing results assume that a request contributes the same weight to each resource it uses, our approximation framework allows for unrelated weights, thus providing the first non-trivial approximation for the problem of scheduling unrelated parallel machines with (D)oS cost functions; (3) while most of the existing approximation algorithms are based on convex programming, our approximation framework is fully combinatorial and runs in strongly polynomial time; (4) the family of (D)oS cost functions considered in the current article is more general than the one considered in the existing literature, providing a more accurate abstraction for practical energy conservation scenarios; and (5) we obtain the first approximation ratio for GND with (D)oS cost functions that depends only on the parameters of the resources' technology and does not grow with the number of resources, the number of requests, or their weights. The design of our approximation framework relies heavily on Roughgarden's smoothness toolbox [43], thus demonstrating the possible usefulness of this toolbox in the area of approximation algorithms.</p