1,315 research outputs found
Characterizing Convexity of Games using Marginal Vectors
In this paper we study the relation between convexity of TU games and marginal vectors.We show that if specfic marginal vectors are core elements, then the game is convex.We characterize sets of marginal vectors satisfying this property, and we derive the formula for the minimum number of marginal vectors in such sets.game theory;convexity;marginal vectors
Characterizing Convexity of Games using Marginal Vectors
In this paper we study the relation between convexity of TU games and marginal vectors.We show that if specfic marginal vectors are core elements, then the game is convex.We characterize sets of marginal vectors satisfying this property, and we derive the formula for the minimum number of marginal vectors in such sets.
Characterizing Compromise Stability of Games Using Larginal Vectors
The core cover of a TU-game is a superset of the core and equals the convex hull of its larginal vectors. A larginal vector corresponds to an order of the players and describes the efficient payoff vector giving the first players in the order their utopia demand as long as it is still possible to assign the remaining players at least their minimum right. A game is called compromise stable if the core is equal to the core cover, i.e. the core is the convex hull of the larginal vectors. In this paper we describe two ways of characterizing sets of larginal vectors that satisfy the condition that if every larginal vector of the set is a core element, then the game is compromise stable. The first characterization of these sets is based on a neighbor argument on orders of the players. The second one uses combinatorial and matching arguments and leads to a complete characterization of these sets. We find characterizing sets of minimum cardinality, a closed formula for the minimum number of orders in these sets, and a partition of the set of all orders in which each element of the partition is a minimum characterizing set.Core;core cover;larginal vectors;matchings
Convexity and Marginal Vectors
In this paper we construct sets of marginal vectors of a TU game with the property that if the marginal vectors from these sets are core elements, then the game is convex.This approach leads to new upperbounds on the number of marginal vectors needed to characterize convexity.An other result is that the relative number of marginals needed to characterize convexity converges to zero.game theory;convexity;marginal vectors
Cores of Cooperative Games in Information Theory
Cores of cooperative games are ubiquitous in information theory, and arise
most frequently in the characterization of fundamental limits in various
scenarios involving multiple users. Examples include classical settings in
network information theory such as Slepian-Wolf source coding and multiple
access channels, classical settings in statistics such as robust hypothesis
testing, and new settings at the intersection of networking and statistics such
as distributed estimation problems for sensor networks. Cooperative game theory
allows one to understand aspects of all of these problems from a fresh and
unifying perspective that treats users as players in a game, sometimes leading
to new insights. At the heart of these analyses are fundamental dualities that
have been long studied in the context of cooperative games; for information
theoretic purposes, these are dualities between information inequalities on the
one hand and properties of rate, capacity or other resource allocation regions
on the other.Comment: 12 pages, published at
http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/318704 in EURASIP
Journal on Wireless Communications and Networking, Special Issue on "Theory
and Applications in Multiuser/Multiterminal Communications", April 200
On 1-convexity and nucleolus of co-insurance games
The situation, in which an enormous risk is insured by a number of insurance companies, is modeled through a cooperative TU game, the so-called co-insurance game, first introduced in Fragnelli and Marina (2004). In this paper we show that a co-insurance game possesses several interesting properties that allow to study the nonemptiness and the structure of the core and to construct an efficient algorithm for computing the nucleolus
Max-convex decompositions for cooperative TU games
We show that any cooperative TU game is the maximum of a finite collection of convex games. This max-convex decomposition can be refined by using convex games with nonnegative dividends for all coalitions of at least two players. As a consequence of the above results we show that the class of modular games is a set of generators of the distributive lattice of all cooperative TU games. Finally, we characterize zero-monotonic games using a strong max-convex decomposition.zero-monotonic, convex games, lattice, modular games, games, cooperative tu-game
Characterizing Compromise Stability of Games Using Larginal Vectors
The core cover of a TU-game is a superset of the core and equals the convex hull of its larginal vectors. A larginal vector corresponds to an order of the players and describes the efficient payoff vector giving the first players in the order their utopia demand as long as it is still possible to assign the remaining players at least their minimum right. A game is called compromise stable if the core is equal to the core cover, i.e. the core is the convex hull of the larginal vectors. In this paper we describe two ways of characterizing sets of larginal vectors that satisfy the condition that if every larginal vector of the set is a core element, then the game is compromise stable. The first characterization of these sets is based on a neighbor argument on orders of the players. The second one uses combinatorial and matching arguments and leads to a complete characterization of these sets. We find characterizing sets of minimum cardinality, a closed formula for the minimum number of orders in these sets, and a partition of the set of all orders in which each element of the partition is a minimum characterizing set.
Multipartite Causal Correlations: Polytopes and Inequalities
We consider the most general correlations that can be obtained by a group of
parties whose causal relations are well-defined, although possibly
probabilistic and dependent on past parties' operations. We show that, for any
fixed number of parties and inputs and outputs for each party, the set of such
correlations forms a convex polytope, whose vertices correspond to
deterministic strategies, and whose (nontrivial) facets define so-called causal
inequalities. We completely characterize the simplest tripartite polytope in
terms of its facet inequalities, propose generalizations of some inequalities
to scenarios with more parties, and show that our tripartite inequalities can
be violated within the process matrix formalism, where quantum mechanics is
locally valid but no global causal structure is assumed.Comment: 14 pages and 1 supplementary CDF fil
Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information
Zero-sum stochastic games provide a rich model for competitive decision
making. However, under general forms of state uncertainty as considered in the
Partially Observable Stochastic Game (POSG), such decision making problems are
still not very well understood. This paper makes a contribution to the theory
of zero-sum POSGs by characterizing structure in their value function. In
particular, we introduce a new formulation of the value function for zs-POSGs
as a function of the "plan-time sufficient statistics" (roughly speaking the
information distribution in the POSG), which has the potential to enable
generalization over such information distributions. We further delineate this
generalization capability by proving a structural result on the shape of value
function: it exhibits concavity and convexity with respect to appropriately
chosen marginals of the statistic space. This result is a key pre-cursor for
developing solution methods that may be able to exploit such structure.
Finally, we show how these results allow us to reduce a zs-POSG to a
"centralized" model with shared observations, thereby transferring results for
the latter, narrower class, to games with individual (private) observations
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