Contemporary Decision Sciences: an Integrated Perspective

2024/2025

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

International Laboratory for Applied Network Research

Course type:

Compulsory course

When:

1 year, 3, 4 module

Instructor

Yakovleva, Dina

Course Syllabus

Abstract

This course is a required foundational course for masters’ students in applied statistics and data analytics program, designed to familiarize them with the most recent developments in interdisciplinary decision sciences. This course covers many approaches to solving real-life problems from the mathematical point of view – in other words, we are using available mathematical tools to make good decisions. Various optimization techniques are surveyed with an emphasis on the why and how of these types of models as opposed to a detailed theoretical approach. Students develop optimization models which relate to their areas of interest. Spread-sheets are used extensively to accomplish the mathematical manipulations. Emphasis is placed on input requirements and interpretation of results.

Learning Objectives

• The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.

Expected Learning Outcomes

• Have a working knowledge of different ways of using software programs for data analysis.
• Be able to criticize constructively and determine existing issues with the use of statistical methods in published work
• Be able to explain how and why modeling is used in the support system environment.
• Be able to identify and differentiate different model components.
• Be able to understand and explain in your own words ways in which model-based support systems are needed and can be utilized in managerial decision processes.
• Have a working knowledge of mathematics of decision sciences.
• Have an ability to use model-based management solution using a variety of software packages.
• Know different decision-structuring techniques.
• Know model-building and model validation techniques.
• Know the role of the modeling in decision-making and different model components.

Course Contents

• Topic 1. Introduction to Contemporary Decision Sciences
• Topic 2. Intro to simulation model
• Topic 3. Intro to Queueing Theory
• Topic 4. Intro to Meta-Analysis
• Topic 5. Linear programming methods
• Topic 6. Intro to database management

Assessment Elements

• Homework 1 Simulation modeling
• Homework 2 Queuing modeling
• Homework 3 Meta-Analysis
• Homework 4 Linear programming

Interim Assessment

• 2024/2025 4th module
  0.25 * Homework 1 Simulation modeling + 0.25 * Homework 2 Queuing modeling + 0.25 * Homework 3 Meta-Analysis + 0.25 * Homework 4 Linear programming

Bibliography

Recommended Core Bibliography

• Kleinman, G., & Lawrence, K. D. (2015). Applications of Management Science. Bingley, U.K.: Emerald Group Publishing Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=948292
• Ravindran, A. (2008). Operations Research and Management Science Handbook. Boca Raton: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=209433
• Rudall, B. H. (2007). Management Science : Current Researches and Developments. [Bradford, England]: Emerald Group Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=201937

Recommended Additional Bibliography

• Mingers, J. (2006). Realising Systems Thinking: Knowledge and Action in Management Science. New York, NY: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=170755
• Taudes, A. (2005). Adaptive Information Systems and Modelling in Economics and Management Science. Wien: Springer Science & Business Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=255803

Authors

• Pavlova Irina Anatolevna
• YAKOVLEVA DINA SERGEEVNA