| Statuss(Aktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | Computer modeling of economic processes |
| Course code | EkonD121 |
| Credit points (ECTS) | 6 |
| Total Hours in Course | 162 |
| Number of hours for seminars and practical classes | 64 |
| Independent study hours | 98 |
| Date of course confirmation | 23/11/2022 |
| Responsible Unit | Institute of Computer Systems and Data Science |
| Course developers | |
| Dr. habil. sc. ing., prof. Pēteris Rivža |
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| There is no prerequisite knowledge required for this course | |
| Course abstract | |
| The aim of the study course is to introduce doctoral students to economic process modeling methods (optimization, dynamic modeling, clustering, forecasting, decision-making). The study course is focused on learning the principles of choosing modeling methods, comparing methods and using them for big data analytics. Real examples related to economics, marketing, ecology, agriculture and forestry are used to learn the methods. Several computer programs are used for modeling: MS Excel, SPSS, Powersim Studio, Matlab, Simulink and Super Decisions | |
| Learning outcomes and their assessment | |
| As a result of studying the study course, doctoral students:
• knows the basic principles and methods of modeling economic systems • knows how to practically use the methods of data preparation and modeling of economic systems • is able to use the learned computer modeling methods in the development of a doctoral thesis |
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| Course Content(Calendar) | |
| 1. Basic elements of systemic thinking. Basic principles of systems modeling. (lectures-1h, practical works-2h).
2. Basic principles of computer modeling of economic processes. The role of models in economic research. Model classification and use. (lectures-1h, practical works-2h). 3. General principles of building optimization models. Classification of optimization models. Linear optimization models and their application in the optimization of economic processes. Optimization model selection problems. Methods of solving linear optimization problems. Dual model and its analysis. Sensitivity analysis of direct and dual problem solutions. (lectures-2h, practical works-4h). 4. The classic transport task and its modifications. Solving and analyzing the transport task. The task of the traveling salesman. Transport flow in networks and their optimization (shortest path problem, maximum flow problem, minimum spanning tree problem). (lectures-2h, practical works-2h). 5. Integral linear programming tasks. Scarce backpack task and its modifications. Optimal object placement problems and their solution. Nonlinear optimization. Data envelopment analysis method. Introduction to multicriteria optimization. Optimization software. Optimization models in doctoral theses of ESAF. (lectures-2h, practical works-2h). 6. Introduction to dynamic models. Modeling in Powersim Studio 10 environment. Modeling the structure and behavior of systems. Archetypes of systems. Dynamic models with arrays and indexes. (lectures-2h, practical works-4h). 7. Testing of dynamic models (verification, validation, sensitivity testing), calibration and preparation for use. Dynamic models in doctoral theses of ESAF. (lectures-2h, practical works-2h). 8. Dynamic models in workforce forecasting, environmental system modeling and other applications. (lectures-1h, practical works-2h). 9. Dynamic models in biology, ecology and forestry. (lectures-1h, practical work-1h). 10. Time series analysis and forecasting. Time series stationarity test. Separation of time series trend and seasonality components. Seasonally adjusted time series. (lectures-2h, practical works-4h). 11. Box-Jenkin methodology. Forecasting capabilities. Modeling and forecasting with single variable time series. ARMA and ARIMA models. Forecasting with ARIMA models. (lectures-1h, practical works-2h). 12. Classification of decision-making methods. Heuristic methods. Multi-attribute decision making methods. Prioritization and ranking based methods. Decision support software. (lectures-2h, practical works-2h). 13.The Analytic Hierarchy Process (AHP). The Analytic Network Process (ANP)). Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). (lectures-2h, practical works-4h). 14. Multivariate statistical models and their use in modeling economic processes. (lectures-1h, practical works-2h). 15. Risk analysis and its tasks. Risk assessment in computer models. (lectures-1h, practical works-2h). 16. Preparation of results of computer modeling of economic systems for publication and problems of writing a scientific article. (lectures-1h, practical works-2h). |
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| Requirements for awarding credit points | |
| The independent work was developed and presented. | |
| Description of the organization and tasks of students’ independent work | |
| The doctoral student prepares data related to the thesis and creates, calibrates and validates the model. Modeling results are used to prepare a scientific article. | |
| Criteria for Evaluating Learning Outcomes | |
| The model of the economic process and the prepared scientific article are evaluated. | |
| Compulsory reading | |
| 1. Dunbar S. R. Mathematical Modeling in Economics and Finance: Probability, Stochastic Processes, and Differential Equations (AMS/MAA Textbooks). American Mathematical Society. 2019. 232 p.
2. Bandeviča L. Matemātiskā modelēšana ekonomikā un menedžmentā (Teorija un prakse): Mācību grāmata augstskolām. 3.izd. Rīga: SIA Izglītības soļi, 2009. 443 lpp. (ESAF bibliotēka) 3. Saaty T. L. Mathematical Principles of Decision Making (Principia Mathematica Decernendi). First Edition. RWS Publications, 2009. 531 p. 4. Sistēmdinamika vides inženierzinātņu studentiem. A. Blumbergas redakcijā. Rīga: RTU, 2010. 318 lpp. |
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| Further reading | |
| 1. Hannon B., Ruth M. Dynamic Modeling. 2nd edition. Springer, 2001. 428 p.
2. Hannon B., Ruth M. Modeling Dynamic Biological Systems. New York: Springer 2008. 395 p. 3. Pituch K.A., James P. Stevens Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM's SPSS. Taylor & Francis, 2016. 4. Härdle W. K., Simar L. Applied Multivariate Statistical Analysis. Switzerland AG.: Springer Nature, 2020. 4. Mittelbach A, Fischlin M. The Theory of Hash Functions and Random Oracles An Approach to Modern Cryptography. Cham:Springer, 2021. 788 p. 5. van Oorschot P. C. Computer Security and the Internet Tools and Jewels from Malware to Bitcoin. Cham: Springer, 2021. 446 p. 6. Beaver K. Hacking For Dummies. 6th Edition. 2018. 416 p.(DSK bibliotēka) |
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| Periodicals and other sources | |
| 1. European Journal of Operational Research. ISSN: 0377-2217.
2. Journal of Economic Dynamics and Control. ISSN: 0165-1889. 3. Mathematical and Computer Modelling of Dynamical Systems. ISSN: 1387-3954. 4. SPSS Statistics for Dummies. 3rd Edition. pdf. Pieejams: https://itbook.download/topic/f1604953eaaf43ce9f6033047f8a8556 5. Multivariate Analysis. Pieejams: https://www.stat.auckland.ac.nz/~balemi/Multivariate%2520Analysis.ppt+&cd=1&hl=lv&ct=clnk&gl=lv |
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| Notes | |
| Elective study course in Economics, forest economics doctoral program. | |