Dynamic Decision Making 2

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Mathematics

Synopsis:

1.Overview of the formalised decision-making task and tools for its solution

2.Application of the general fully probabilistic design of decision-making strategies for Markov chains and linear-Gaussian models

3.Aproximation and completion of probabilities serving to processing data-based as well as probabilistic knowledge and preferences for Markov chains

4.Introduction into multi-participants decision making and its formalisation

5.Usability of general tools for knowledge sharing and cooperation within multiple-participants decision making

6.Ilustrative case studies of solving decision-making tasks

7.Open decision-making problems

Requirements:

Syllabus of lectures:

1.Overview of the formalised decision-making task and tools for its solution

2.Application of the general fully probabilistic design of decision-making strategies for Markov chains and linear-Gaussian models

3.Aproximation and completion of probabilities serving to processing data-based as well as probabilistic knowledge and preferences for Markov chains

4.Introduction into multi-participants decision making and its formalisation

5.Usability of general tools for knowledge sharing and cooperation within multiple-participants decision making

6.Ilustrative case studies of solving decision-making tasks

7.Open decision-making problems

Syllabus of tutorials:

Study Objective:

Acquired knowledge: Deepening of the insight into the general formalisation and the solution methodology of real-life decision-making tasks addressed under uncertainty and incomplete knowledge: all this is gained during the lecture 01DRO1

Abilities: To formalise a specific real-life decision-making problem, to fill its elements with appropriately chosen methods for their constructing as well as for solving the resulting formalised decision-making problem

Study materials:

Recommended literature: selected parts from

[1] M. Kárný, J. Bohm, T.V. Guy, L. Jirsa, I. Nagy, P. Nedoma, and L. Tesař. Optimized Bayesian Dynamic Advising: Theory and Algorithms. Springer, London, 2006.

[2] M. Kárný, T.V. Guy. Fully probabilistic control design. Systems & Control Letters, 55(4), 2006.

[3] M. Kárný, T.V. Guy Tatiana Valentine: On the Origins of Imperfection and Apparent Non-Rationality, 57-92, in T.V. Guy, M. Kárný, D.H. Wolpert, Decision Making: Uncertainty, Im- perfection, Deliberation and Scalability, Springer, Studies in Computational Intelligence 538, 2014

The needed support: lecture room with projector

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Matematické inženýrství (elective course)
• Aplikované matematicko-stochastické metody (elective course)