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Bayesian Dynamic Decision Making

Dynamic decision making (DM) maps knowledge into DM strategy, which ensures reaching DM aims under given constraints. Under general conditions, Bayesian DM, minimizing expected loss over admissible strategies, has to be used. Existing limitations of the paradigm impede its applicability to complex DM as:

  1. Complexity of the information processing often crosses resources accessible.
  2. Quantification of domain-specific knowledge, aims and constraints is weakly supported. It concerns mapping of domain-specific elements on probabilistic distributions (pd).
  3. Methodology of the DM with multiple aims is incomplete.

The research aims to overcome these problems. It relies on distributed DM and fully probabilistic design (FPD) of strategies. The goal is to build a firm theoretical background of FPD of distributed DM strategies. Besides, it will enrich available results and unify them into internally consistent theory suitable for a flat cooperation structure.

This aim implies the main tasks:

  1. Inspection of conditions leading to FPD
  2. Extension of FPD to design with sets of ideal pds
  3. Design of computerized conversion of knowledge and aims into environment-describing and ideal pds
  4. Elaboration of theoretical framework for selecting cooperation tools



Active projects

Former projects

Application-oriented projects

In general, research activities in the department are always motivated by real-world application. Projects listed on this page are those that goes beyonds academic considerations and actually implemented the developed algorithms in real environment. List of theoretical projects is available here

List of former application-oriented projects

Clusters of Research Oriented Activities

Bayesian Dynamic Decision Making

Dynamic decision making (DM) maps knowledge into DM strategy, which ensures reaching DM aims.

Financial Econometrics

Field characteristic

A research in the financial econometrics is on the cutting edge of the current research. Financial markets have very complex nature and cannot be easily understood using simple, tractable models. Thus the financial markets are investigated from the various perspectives.

Stochastic optimization

Field characteristic

Stochastic programming and Decision in Economy

1. The Wasserstein metric (based on L_1 norm) has been employed to obtain upper stability bounds  (considered with respect to  probability measures space) for ``classical" one-stage stochastic programming problems with operator of mathematical expectation in an objective function and deterministic constrained sets. This result has been generalized to some types of problems in which dependence on probability measure is not linear.

Fuzzy approach and uncertainty processing

Field characteristic

Theory of copulass and aggregation operators


Field characteristic


Nonlinear model of closed economy is formulated on an extended and modified Kaldor's model of small closed economy with a simple structure and several nonlinearities. Nonlinear pattern of dependencies in this model is created by a logistic function. The model is realized by four differential equations. The first equation and second equation describe output and capital dynamics. A core of the output dynamics is formulated by investment and savings disequilibrium.

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