Bachelor 2023/2024

Management Decisions

Type: Compulsory course (International Business)

Area of studies: Management

Delivered by: Department of Strategic and International Management

Where: Graduate School of Business

When: 2 year, 3, 4 module

Mode of studies: offline

Open to: students of one campus

Instructors: Сельская Татьяна Константиновна, Nikolay Filinov

Language: English

ECTS credits: 4

Contact hours: 48

Course Syllabus

Abstract

Decision-making processes related to different business functions and different levels of management reveal common patterns. Knowledge of these patterns, the ability to structure the process of developing and making decisions in the most rational way, the skills of individual and group decision-making are extremely important for a manager. Therefore, along with other academic disciplines, training programs for managers and researchers of management problems include a special course managerial decision-making. The course is instrumental and methodological in nature. This means that it examines methods, tools (mathematical, informational), procedures, individual and group technologies for making management decisions. Unlike other management disciplines, which answer the question of what management decision should be made in a given situation in marketing, personnel management, finance, etc., the course answers the question of how to organize development and decision-making in a given situation. The relevance of the course as an interdisciplinary one is ensured by the use of the results of modern research in the field of psychology, behavioral economics, organizational behavior and applied mathematics aimed at solving current problems of business practice. The tasks and problem situations discussed in the course are aimed at developing students’ skills in making ethical, socially responsible decisions that contribute to the sustainable development of society. The course uses numerous domestic and foreign sources, including publications and video materials. It is envisaged that students will complete individual and group assignments, based on Russian and foreign business practices. Current control in the discipline includes: individual homework, a group project and a written exam.

Learning Objectives

• The purpose of the discipline is to develop students’ competencies, necessary for analyzing, developing and improving procedures and processes for making management decisions in organizations.

Expected Learning Outcomes

• Diagnoses decision-making processes in the organization and develops recommendations for the improvement thereof
• Applies methods of decisions’ analysis and optimization under conditions of multi-criteria and risk
• Uses the principles and methods of evidence-based management
• Has the skills to prepare and make decisions in a team

Course Contents

• Decision-Making Process Models
• Biases and Noise in Decision-Making Processes
• Multi-criteria Decision-Making
• Evidence-Based Management
• Decision-Making under Risk and Uncertainty
• Group Problem Solving and Decision-Making
• Individual Decision-Making Style
• Graphical Methods of Decision-Making
• Artificial Intelligence and Business Decision-Making

Assessment Elements

• Assignment #1
  Students have to choose a managerial decision-making process, which occurs on a regular basis in an organization familiar to them, and to analyze it using the H.Mintzberg's three-fold model "Thinknig first", "Seeing first", and "Doing first". The process should be identified as a certain superposition of the three models, or the subset thereof. Based on the results of this analysis students develop recommendations on how the efficiency of the process could be increased. These recommendations may include either the change of the nature of the process in question (ex. moving from trial and error mode of "Doing first" towards routinized procedure of "Thinknig first", or developing and strengthening the existing nature of the process via implementing steps and policies, discussed in the course.
• Assignment #2
  Individual assignment on the topic “Decision-making under Risk and Uncertainty”. Analyze an organization you are familiar with, that deals with substantial risks in its regular business: select one of those risks and apply the 6 generic risk management strategies to it: i.e. propose specific measures to manage this risk. Summarize your findings in a table that covers: 1) Generic risk management strategy, 2) detailed description of how it could be applied to this specific risk in this particular organization (or why it cannot be applied)
• Assignment #3
  Group project on the topic “Evidence based management”. Form a team of 4-5 people. Each team will be given a real problem to solve from a real client – an external organization that acts as a partner for this assignment. You would need to propose solutions for this problem by using evidence-based management tools and concepts, and present your work to your client.
• Exam
  Exam consists of three parts. The first part is a mini-case to be analyzed using decision making models, discussed in the course. The second part requires reflection on the theoretical question, providing explanations, interpretations and examples. The third part is a computational problem in the field of MCDM, voting and decision making under risk and uncertainty.

Interim Assessment

• 2023/2024 4th module
  0.2 * Assignment #1 + 0.2 * Assignment #2 + 0.2 * Assignment #3 + 0.4 * Exam

Bibliography

Recommended Core Bibliography

• Alex Mintz, & Dmitry (Dima) Adamsky. (2019). How Do Leaders Make Decisions? : Evidence From the East and West, Part A. Bingley: Emerald Publishing Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2107620
• Barends, E., & Rousseau, D. M. (2018). Evidence-Based Management : How to Use Evidence to Make Better Organizational Decisions (Vol. First edition). New York: Kogan Page. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1881467
• Daniel, K. (2016). Heuristics and Biases. Cambridge University Press. https://doi.org/10.1017/cbo9781316422250.038
• George Wu, & Kathleen L. McGinn. (2017). Decision Analysis. HBP Education Case Study Collection.
• Gregory S. Parnell, Terry Bresnick, M., Steven N. Tani, P., & Eric R. Johnson, P. (2012). Handbook of Decision Analysis. Wiley.
• Harvard Business Review Press. (2019). Artificial Intelligence : The Insights You Need From Harvard Business Review. La Vergne: Harvard Business Review Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2003692
• Kenneth D. Lawrence, & Gary Kleinman. (2010). Applications in Multi-criteria Decision Making, Data Envelopment Analysis, and Finance: Vol. 1st ed. Emerald Group Publishing Limited.
• Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human Decisions and Machine Predictions. Quarterly Journal of Economics, 133(1), 237–293. https://doi.org/10.1093/qje/qjx032
• Moulin, H. (1988). Condorcet’s principle implies the no show paradox. Journal of Economic Theory, (1), 53. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.eee.jetheo.v45y1988i1p53.64
• Osondu, O. (2021). A First Course in Artificial Intelligence. Bentham Science Publishers Ltd.
• Pressman, A. (2019). Design Thinking : A Guide to Creative Problem Solving for Everyone. New York: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1923524
• Rastogi, C., Zhang, Y., Wei, D., Varshney, K. R., Dhurandhar, A., & Tomsett, R. (2020). Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making.

Recommended Additional Bibliography

• Adizes, I., & Solmo, R. (1994). How to convert a committee into a team. Successful Meetings, 43(2), 115. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=f5h&AN=9502214313
• Alexis Bogroff, & Dominique Guégan. (2019). Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation. Documents de Travail Du Centre d’Economie de La Sorbonne.
• Chapman, C. B., & Ward, S. (2012). How to Manage Project Opportunity and Risk : Why Uncertainty Management Can Be a Much Better Approach Than Risk Management (Vol. 3rd ed). Chichester, West Sussex: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=509606
• Chris Starmer. (2000). Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk. Journal of Economic Literature, (2), 332. https://doi.org/10.1257/jel.38.2.332
• Daniel Kahneman, Jack L. Knetsch, & Richard H. Thaler. (1991). Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias. Journal of Economic Perspectives, 1, 193. https://doi.org/10.1257/jep.5.1.193
• Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.31568410
• Goodman, B., & Flaxman, S. (2016). European Union regulations on algorithmic decision-making and a “right to explanation.” Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.670CE3E5
• Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
• Matteo Cristofaro, & Pier Luigi Giardino. (2020). Core Self-Evaluations, Self-Leadership, and the Self-Serving Bias in Managerial Decision Making: A Laboratory Experiment. Administrative Sciences, 10(64), 64. https://doi.org/10.3390/admsci10030064
• Merna, T., & Al-Thani, F. F. (2008). Corporate Risk Management (Vol. 2nd ed). Chichester, England: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=323307
• Norris, P. (2004). Electoral Engineering : Voting Rules and Political Behavior. Cambridge, UK: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=157946
• Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
• Wright, A. L., Zammuto, R. F., Liesch, P. W., Middleton, S., Hibbert, P., Burke, J., & Brazil, V. (2016). Evidence-based Management in Practice: Opening up the Decision Process, Decision-maker and Context. British Journal of Management, 27(1), 161–178. https://doi.org/10.1111/1467-8551.12123