|
Lecturer(s)
|
-
Stoklasa Jan, Mgr. et Mgr. Ph.D.
|
|
Course content
|
The course is intended to cover the following areas: o Introduction to the theory of decision making - benefits of the formal theory of decision making, examples of its use o Rationality of the decision maker, criteria and scales in decision making o Basic approaches and methods in decision making under certainty - examples, methods for setting weights o Multiple criteria decision making, basic methods, methods of computing the overall evaluation, consensus o Uncertainty and risk in decision making - basic models, examples o Expert knowledge and its representation (goals, criteria, scales, rules), advantages and drawbacks of models based on expert knowledge o Dealing with uncertainty that results from linguistic description of reality - representing uncertain quantities, linguistically defined goals of evaluation (fuzzy sets and operations with fuzzy sets) o Formal representation of uncertain relations (fuzzy relations), operations with fuzzy objects (dealing with qualitative variables, aggregation with other types of variables, inclusion of these into more complex applications. o Formal representation of expert knowledge using the tools of linguistic fuzzy modeling (linguistic variables, fuzzy rule bases) o Deduction under uncertainty, using linguistically defined rule bases o Enhancement of several known methods of decision making under certainty and risk with fuzzy approach, fuzzy classifications. As Prof. Lotfi A. Zadeh formulated in the incompatibility principle - as the complexity of the system increases, our ability to describe it using precise mathematical tools diminishes. We all would probably agree, that systems that social sciences deal with are complex ones. It is therefore important to have a tool, that would enable us to describe these systems sufficiently (if not completely). Fuzzy set theory is capable of proving such a tool. In this course, students will learn how to deal with linguistic descriptions of systems and relations among their components and how to obtain reasonable results. He/she will understand, how to model human decision making based on its linguistic description and how to formally deal with such uncertain descriptions of reality as "high revenue", "large enough amount" or "a little bigger than". The course will introduce to the students - in an easy to understand manner - a new tool of working with expert knowledge (and uncertainty that is inherent to its linguistic description) - the fuzzy set theory and theory of linguistic fuzzy modeling. It will introduce the basic concepts of fuzzy modeling in practical context, basic methods that utilize these concepts and are applicable in management and psychology (generally in social sciences). This subject arose from a project CZ.1.07/2.2.00/28.0138 Modularization and psychological education at Palacky University in Olomouc through innovation and links between economic and psychological study programs.
|
|
Learning activities and teaching methods
|
Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration, Projection (static, dynamic)
- Homework for Teaching
- 50 hours per semester
- Preparation for the Course Credit
- 25 hours per semester
- Attendace
- 25 hours per semester
|
|
Learning outcomes
|
The world around us can be described in a comprehensible, yet simple way by the use of language. Language is a very strong tool not only for communication, but also for sharing experience, advice, for finding solutions of various problems (including the decision making ones). This course will show the students: - how the linguistic description of the world and problems in it can be treated formally - how to deal with the uncertainty of linguistic description and - how to use linguistically described rules (set expertly in the form a fuzzy rule bases) to solve decision making problems or perform deduction. We will discuss, how to provide support to a manager, HR specialist or any other particular practitioner in their decision making process, how to provide them with a tool to deal with nontrivial problems (such as staff selection based on a larger amount of qualitative and quantitative criteria, deriving conclusions from a large amount of diagnostics reflecting expert knowledge).
Student will be able to understand the basic concepts of fuzzy set theory and linguistic fuzzy modelling and will be able to utilise these concepts in his/her field using appropriate software tools (FuzzME for example). The student will be familiar with the basic principles of working with linguistic description of reality (and of working with uncertain objects - such as quantities or goals). He will be oriented in methods able to deal with fuzzy objects (including deduction methods), i.e. such object, that cannot be satisfactorily dealt with using the classical quantitative methodology. In his/her work or research the student will be able to think outside the classical qualitative or quantitative framework.
|
|
Prerequisites
|
Basic understanding of the concept of sets can be an advantage.
|
|
Assessment methods and criteria
|
Student performance, Systematic Observation of Student, Seminar Work
Attendance - max. two absences are acceptable. Active work in seminars. Seminar paper - a solution of a decision making problem that utilizes the methods discussed within the course + its presentation.
|
|
Recommended literature
|
-
D. Kahneman, A. Tversky. Choices, Values, and Frames. Cambridge University Press, 2000..
-
D. Kahneman. (2011). Thinking fast and slow. New York.
-
Dostál, P., Rais, K., & Sojka, Z. (2005). Pokročilé metody manažerského rozhodování. Praha: Grada Publishing.
-
Fotr, J., & Švecová, L. (2010). Manažerské rozhodování: postupy, metody a nástroje. Praha: Ekopress.
-
Fotr, J., Hrůzová, H., & Dědina, J. (2003). Manažerské rozhodování. Praha: Ekopress.
-
G. Bojadziev, M. Bojadziev. Fuzzy logic for business, finance and management. World Scientific, Singapore, New Jersey, London, Hong Kong, 2007..
-
G. Mengov. (2015). Decision Science: A Human-Oriented Perspective. Berlin Heidelberg.
-
J. von Neumann, O. Morgenstern. (2004). Theory of Games and Economic Behavior. New Jersey.
-
on-line katalogy knihoven. on-line katalogy knihoven.
-
P. C. Fishburn. (1970). Utility Theory for Decision Making. J. Willey, New York.
-
Stoklasa, J. Linguistic models for decision support.
-
Talašová, J. (2003). Fuzzy metody vícekriteriálního hodnocení a rozhodování. Olomouc: Univerzita Palackého.
|