30601 - COMPUTATIONAL APPLICATIONS IN MANAGEMENT

Basic mathematics and calculus knowledge; basic knowledge of statistics.

Managers and decision-makers confront an environment that is becoming increasingly complex, with key strategic decisions in businesses being made under conditions of uncertainty. The mission of this course is to equip students with a comprehensive understanding of a novel framework that enables them to make better decisions within such contexts. This course places a strong emphasis on integrating theory-building, Bayesian principles, and experimentation as fundamental elements of a structured approach to decision-making. By incorporating these components, students will develop a more systematic and informed way of approaching decision-making processes. Upon completion of this course, students will possess the necessary knowledge and skills to apply this framework to real-world scenarios. This will empower them to make well-informed decisions across a wide range of professional and personal contexts.

The course centers around the comprehension and application of a novel framework designed to structure key strategic decision-making in the face of uncertainty.

It encompasses the following topics:

• An introductory overview of decision-making under conditions of uncertainty, featuring real-world case examples.
• An introduction to Bayesian statistics and the utilization of Direct Acyclic Graphs (DAGs) as tools to structure decision problems.
• Techniques for developing theories to address strategic problems in an uncertain environment, employing a structured methodology that incorporates causal and probabilistic reasoning.
• An introduction to conducting experiments within business contexts, including how to effectively utilize experimental results to update prior beliefs.

• Recognize situations where decisions are made under conditions of uncertainty.
• Interpret decisions under uncertainty under a systematic framework
• Understand and interpret Directed Acyclic Graphs (DAGs) and their associated probabilities
• Know how to design experiments and collect data in business contexts

• Utilize a systematic framework to guide decision-making processes in uncertain environments.
• Formulate their own theories of strategic problems by constructing Bayesian causal structures and calculating associated probabilities.
• Design custom experiments and implement data collection efforts to obtain pertinent information for making rational decisions on real-world problems.

• Face-to-face lectures
• Case studies /Incidents (traditional, online)
• Group assignments
• Interactive class activities on campus/online (role playing, business game, simulation, online forum, instant polls)

Case studies: Students will have the opportunity to analyze and discuss various case studies that showcase significant strategic decisions made by companies. These case studies serve as practical examples to reinforce the understanding of the concepts taught in class and provide a connection to real-world phenomena.

Group assignments: As part of the course, there will be a final group assignment that allows students to apply the techniques and framework learned throughout the course. This assignment promotes collaboration and the application of the decision-making framework in a practical context.

Interactive class activities: Students will engage in interactive class activities, including a novel simulation game, where they will be actively involved in applying the decision-making framework taught in class. These activities provide hands-on experience and enhance the students' comprehension and practical application of the framework.

The assessment of students' learning outcomes in this course is based on two main components

Group assignment (12 points): This assignment aims to evaluate the students' ability to:

• Analyze decision problems and identify key elements such as possible strategies, outcomes, and scenarios.
• Apply the framework and methodologies taught in class to address decision problems.
• Collaborate effectively within a team and individually, and deliver a concise but clear report highlighting relevant outcomes.

The evaluation of the group project will be valid for all exam sessions within the relevant academic year.

Final written exam (18 points): This exam is designed to assess students' knowledge of the topics covered in class and the relevant readings. It consists of a combination of closed and open questions.

Active class participation is encouraged, and students can earn an additional point to reach a total score of 31/30, provided they reach 30/30 from the above two assessment methods. 

However, to be eligible to take the exam as an attending student, an attendance rate of 75% or higher must be recorded using the specific app available to all students.

Non attending students will be evaluated based on a final exam only, which will include both closed and open questions.

Relevant papers and readings will be shared with the classroom through Blackboard.