Data-Driven Decision-Making

On completion of the course, students will be able to:

1. describe and analyse the role of intuition in decision-making in complex environments
2. explain and critically analyse how asymmetric information affects collective decisions and actions
3. explain and critically reflect on the role of cultural factors in the design of institutions and incentives, and analyse alternative strategies to formal decision models
4. formulate and implement formal, data-driven models for decision-making situations, including underlying assumptions, limitations, and computational strategies
5. analyse and evaluate investment and action alternatives in situations characterised by uncertainty, incomplete data, and a combination of monetary and non-monetary values
6. analyse and assess trade-offs between exploration and exploitation in data strategies for decision-making, with attention to efficiency, risk, and reliability
7. apply and reflect on their own decision-making and their ability to communicate and work in a group. This includes independently deepening their knowledge in a relevant field, communicating complex concepts in decision-making, and giving and receiving constructive feedback in academic discussions
8. apply and reflect on their decision-making methods and their ability to communicate and work in a group in decision-making processes
9. identify and reflect on the challenges of research ethics when making decisions, including integrity, accountability, responsibility, and potential impact on individuals, organisations, and society.

The course addresses analytical models and tools that are used to understand and solve decision-making problems, both for the individual and within various types of institutions. Here, “institutions” refers to different forms of organisations, such as companies, government authorities and other collective structures, where motivation and information are required to coordinate the actions of individual actors. The course builds on theories from various fields, including game theory, normative decision theory and behavioural science theory models. Focus is placed on incentive issues, asymmetric information, and cultural and organisational factors that affect decision-making environments. A special section covers the modelling of decision situations using data-driven tools. Uncertainty, risk and situations with incomplete information are addressed, as well as how different types of utilities (monetary and non-monetary) can be weighed up in practice. The course includes research ethics perspectives related to decision-making, such as integrity, transparency, and societal impact. Course participation involves an experience-based simulation of collective decision-making, where experiences form the basis for reflection supported by the concepts, methods and materials presented in the course.

• Seminars
• Written examination

The Swedish grades U–G.

Replaces FMI2222.