In this paper methods of mixing decision rules are investigated and applied to the so-called multiple job type assignment problem with specialized servers which is modeled as continuous time Markov decision process. Performance optimization is difficult for this assignment problem, but...

In a Bayesian analysis, different models can be compared on the basis of theexpected or marginal likelihood they attain. Many methods have been devised to compute themarginal likelihood, but simplicity is not the strongest point of most methods. At the sametime, the precision of methods is often...

This article presents an agent-based modelof Prato, an Italian textile district where thousands ofsma1l firms specialise into tiny phases of the whole production process. It is an empirical model atscale 1: 1 that reconstructs the information flows between economic actors and connects them...

We study a stochastic dynamic game of process innovation in which firms can initiate and terminate R&D efforts and production at different times. We discern the impact of knowledge spillovers on the investments in existing markets, as well as on the likely structure of newly forming markets, for...

This paper points out the importance of Stochastic Dominance (SD) efficient sets being convex. We reviewclassic convexity and efficient set characterization results on SD efficiency of a given portfolio relative to adiversified set of assets and generalize them in the following aspects. First,...

In this paper we study a challenging call center operation problem. The goal of our analysis is to identify an optimal policy for allocating tasks to agents. As a first step, we discuss promising randomized policies and use stochastic approximation for finding the optimal randomized policy when...

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the...

We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior...

We apply the splitting method to three well-known counting problems, namely 3-SAT, random graphs with prescribed degrees, and binary contingency tables. We present an enhanced version of the splitting method based on the capture-recapture technique, and show by experiments the superiority of...