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:
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:
|Fully probabilistic design of dynamic decision strategies||01.01.2008||01.01.2011|
|Advanced techniques of Bayesian decision making in complex systems||01.01.2004||01.01.2009|
|Fully probabilistic design of adaptive decision-making strategies suitable under informationally demanding conditions||01.01.2006||01.01.2009|
|Bayesian Decision Making to Support Change Detection in Complex Manufacturing Systems||01.01.2007||01.01.2008|
|Stochastické modelování dynamických kooperativních sítí||01.01.2007||01.01.2008|
|Data-driven modelling for decision-making support and process monitoring||01.01.2005||01.01.2006|
Dynamic decision making (DM) maps knowledge into DM strategy, which ensures reaching DM aims.
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.
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.
Theory of copulass and aggregation operators
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.