Метод динамического программирования (МДП) Беллмана исключительно эффективен для решения широкого класса задач экономико-математического моделирования. В...

Method of dynamic programming (MDP) of Bellman is exceptionally efficient for solving a wide class of tasks of economic and mathematical modelling. In a number of cases MDP has no alternative. Meanwhile, statements of specialists with respect to correctness of justification of MDP and also with...

This paper continues Dietrich and List's [2010] work on propositional-attitude aggregation theory, which is a generalised unification of the judgment-aggregation and probabilistic opinion-pooling literatures. We first propose an algebraic framework for an analysis of (many-valued)...

This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and...

This paper proposes a new method for approximating vector autoregressions by a finite-state Markov chain. The method is more robust to the number of discrete values and tends to outperform the existing methods over a wide range of the parameter space, especially for highly persistent vector...

This paper continues Dietrich and List's [2010] work on propositional-attitude aggregation theory, which is a generalised unification of the judgment-aggregation and probabilistic opinion-pooling literatures. We first propose an algebraic framework for an analysis of (many-valued)...

In this paper, we apply the idea of $k$-local contraction of \cite{zec, zet} to study discounted stochastic dynamic programming models with unbounded returns. Our main results concern the existence of a unique solution to the Bellman equation and are applied to the theory of stochastic optimal...