Stochastic or Markov games serve as reasonable models for a variety of domains from biology to computer security, and are appealing due to their versatility. In this paper we address the problem of finding the complete set of correlated equilibria for general-sum stochastic games with perfect information. We present QPACE – an algorithm orders of magnitude more efficient than previous approache...

We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel (MMSB). It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world networks with up to 60,000 nodes. It converg...

Several Wilson loops on several lattice sizes are computed in Perturbation Theory via a stochastic method. Applications include: Renormalons, the Mass Term in HQET and (possibly) the β-function. Outlook Wilson Loops (WL) were the historic playground (and success. . .) of Numerical Stochastic Perturbation Theory (NSPT) for Lattice Gauge Theory [1]. Having by now an increased computing power avai...

We consider the problem of determining (for a short lifecycle) retail product initial and replenishment order quantities that minimize the cost of lost sales, back orders, and obsolete inventory. We model this problem as a two-stage stochastic dynamic program, propose a heuristic, establish conditions under which the heuristic finds an optimal solution, and report results of the application of ...

The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion ...

Satis cing is a hugely in uential model of boundedly rational choice, yet it cannot be easily tested using standard choice data. We develop necessary and su cient conditions for stochastic choice data to be consistent with satis cing, assuming that preferences are xed, but search order may change randomly. The model predicts that stochastic choice can only occur amongst elements that are always...

For nonnegative random variables X and Y we write X ≤ttt Y if TX(p) ≤ TY (p) for all p ∈ (0, 1), where TX(p) ≡ ∫ F−1(p) 0 (1− F (x)) dx and TY (p) ≡ ∫ G−1(p) 0 (1−G(x)) dx; here F and G denote the distribution functions of X and Y , respectively. The purpose of this article is to study some properties of this new stochastic order. New properties of the excess wealth (or right spread) order, and...

In recent years the Numerical implementation of Stochastic Perturbation Theory (NSPT) was introduced, which was able to reach unprecedented high orders in perturbative expansions in Lattice Gauge Theories (LGT). Till now the main limitation of the method has been the quenched approximation, an inclusion of fermionic loops contributions missing. We can now fill this gap. Let us first of all remi...

The present paper is devoted to different methods of choice under risk in an actuarial setting. The classical expected utility theory is first presented, and its drawbacks are underlined. A second approach based on the so-called distorted expectation hypothesis is then described. It will be seen that the well-known stochastic dominance as well as the stop-loss order have common interpretations ...

We describe computational procedures to solve a wide-ranging class of stochastic programs with chance constraints where the random components of the problem are discretely distributed. Our procedures are based on a combination of Lagrangian relaxation and scenario decomposition, which we solve using a novel variant of Rockafellar and Wets’ progressive hedging algorithm. Experiments demonstrate ...