Abstract: A correlated equilibrium for a normal form game is a mediator whose recommendations the players find optimal to follow obediently. A (direct) correlation device or a mediator however may face the multiple equilibrium problem as the obedient strategy profile may be dominated by some other

SUMMARY leaves the domain of attraction of equilibrium point i for the domain of attraction of equilibrium point j.When a dynamical system with multiple equilibrium Define the finite state Markov process z(T) on the state points is perturbed by continous wide-band noise, it is space {xl,..xn by its

Since the time of Wald [19] and Arrow, Block, and Hurwicz [2], the standard sufficiency condition used to prove uniqueness of general exchange equilibrium has been that of gross substitutability introduced by Metzler [15] (cf. [13, p. 613]). Other sufficient conditions have been introduced by Arrow

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Nash equilibrium under specified conditions. This algorithm is useful for finding the optimal strategy when there exists a unique Nash equilibrium in the game. When there exist multiple Nash equilibria in the game, this algorithm should be combined with other learning techniques to find optimal

; multiple equilibrium regime). However, the modeled Hachijo Island causes two differences. First, the Kuroshio takes a detouring path around the island (a quasi-steady C-type path) at the lower end of regime III. Secondly, a transient C-type path always appears in the course of the transition from a

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We study a spectrum sharing problem in an unlicensed band where multiple sys-tems coexist and interfere with each other. Due to asymmetries and selfish system behavior, unfair and inefficient situations may arise. We investigate whether efficiency and fairness can be obtained with self

for training, even if these skills are general. The model can lead to multiple equilibria. In one equilibrium quits are endogenously high, and as a result employers have limited monopsony power and provide little training, while in another equilibrium quits are low and training is high. Using microdata

and simulated data sets, we demonstrate the advantages of our Bayesian algorithm, and we show that it is robust to the violation of Hardy-Weinberg equilibrium, to the presence of missing data, and to occurrences of recombination hotspots.