In today’s business environment, procedural decision-making is a routine daily activity for managers. Decisions must be made in dynamic and complex environments and factor in many risks and uncertainties. It is therefore crucial for managers and policy makers to understand the nature of decision-making processes and to develop strategies for choosing best alternatives among all possible options. In this module, you will learn about the theories and motivations behind decision-making processes, individual and group decision-making, and descriptive and prescriptive approaches. Moreover, decision analysis will be conducted via modelling the uncertainty and risks on daily examples and solution approaches using machine learning techniques such as Bayesian Statistics, Decision Trees, Game Theory, Monte Carlo simulation. Further, theories and techniques for multi-criteria decision-making processes will be demonstrated. Finally, you will explore applied decision support systems through case study analyses.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
SYLLABUS PLAN - summary of the structure and academic content of the module
