IU:TestPage

Simulation modeling of financial and economic systems

• Course name: Simulation modeling of financial and economic systems
• Code discipline: P.1.1 Course Characteristics
• Subject area:

Short Description

This course covers the following concepts: Introduction to the basic concepts of modeling business processes and queuing systems; Development of skills in building models of queuing systems; Mastering the basic tools of simulation modeling.

Course Topics

Course Sections and Topics

Section	Topics within the section
Dynamic systems and discrete-event modeling	A block method for implementing models of dynamic systems. Modeling of discrete systems. Application of an event model to control discrete flows. Process approach.
Theory and methods of system dynamics	Theoretical foundations of system dynamics. Methodology for the development of system dynamic models. Archetypes of system dynamics. Continuous and discrete modeling. Compressed and real time. The concept of model time. The theory of feedbacks and lag dependencies. Implementation of the simulation model in the form of a system of simultaneous equations.
Multi-agent systems	The paradigm of agent modeling. Architecture of agent models. The concept of an agent's state map. Collective behavior of agents. Interaction of agents with the environment and with each other. Agents in space and in time.

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

The main purpose of this course is the formation of students' complex of theoretical knowledge and methodological foundations in the field of simulation modeling systems, as well as practical skills in the implementation and use of such systems within the framework of financial and economic subject areas

ILOs defined at three levels

Level 1: What concepts should a student know/remember/explain?

By the end of the course, the students should be able to ...

• explain the purpose of the sections of the conceptual description of the object of study;
• understand the basic classes and principles of building information systems used for the practical implementation of simulation methods;
• understand the basic methods of simulation modeling, including methods of system dynamics, agent modeling, discrete-event modeling, probabilistic modeling;
• know the stages of planning a simulation experiment;
• understand the characteristics of the simulation modeling (SM) systems market and the prospects for the development of SM systems.

Level 2: What basic practical skills should a student be able to perform?

By the end of the course, the students should be able to ...

• determine the required type of simulation model depending on the task;
• list and describe methods of calculating the main statistical indicators for evaluating the effectiveness of financial and economic systems;
• carry out statistical processing of the initial data for the simulation model;
• conduct a simulation experiment and process the results;
• interpret the results of a simulation experiment.

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

By the end of the course, the students should be able to ...

• carry out calculations of the main performance indicators for various classes of Markov-type queue models;
• develop simulation models based on the use of modern simulation methods and integrated with various data sources;
• solve the problem of choosing the input probability distribution for a specific simulation model;
• apply simulation modeling systems to solve forecasting problems, to conduct scenario (situational) modeling and analysis, intelligent data processing;
• apply simulation modeling systems to search for optimal management solutions, risk impact assessment.

Grading

Course grading range

Grade	Range	Description of performance
A. Excellent	90-100	-
B. Good	75-89	-
C. Satisfactory	60-74	-
D. Poor	0-59	-

Course activities and grading breakdown

Activity Type	Percentage of the overall course grade
Homework	50
Assessment	30
Final Exam	20

Resources, literature and reference materials

Open access resources

• Bungartz H.-J., Zimmer S., Buchholz M., Pflüger D. Modeling and Simulation, Berlin, Heidelberg: Springer Berlin Heidelberg, 2014.
• Yoav S., Leyton-Brown K. Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, 2009.
• Borshchev A., Grigoryev I. The Big Book of Simulation Modeling Multimethod Modeling with AnyLogic 8.
• Bossel H. Modeling and Simulation, Wiesbaden: Vieweg Teubner Verlag, 1994.
• Forrester J.W. World Dynamics, Wright-Allen Press, Inc., 1971.

Teaching Methodology: Methods, techniques, & activities

Activities and Teaching Methods

Activities within each section

Learning Activities	Section 1	Section 2	Section 3
Development of individual parts of software product code	1	1	1
Homework and group projects	1	1	1
Reports	1	1	1
Discussions	1	1	1
Testing (written or computer based)	0	0	1

Formative Assessment and Course Activities

Final assessment

Section 1

Section 2

Section 3

The retake exam

Section 1

Section 2

Section 3