Course 2021-2022 a.y.

The course offers an overview of the concepts and methods of decision analysis and modelling, and discuss their growing range of applications within firms and organisations. The objectives of the course are to go through and familiarise with the following topics: 

• Present various modelling approaches used for policy decision making (decision trees, markov models, population dynamic model and agent based models).
• Understand their theoretical foundation and how they can be developed and implemented.
• Get familiar with applications of the modelling framework in firms and organistions to study competition, diffusion processes, cost-effective allocation of resourses.
• Explore possible complications that take into account realistic scenarios, heterogeneities of agents and realistic network structures.
• Stages of the model building process (formulation and assumptions, implementation and parameterisation, simulation and prediction).
• Uncertainties and robustness of model result.
• Uses and limitations of these methods in decision making in government, within health care organizations, in private industry, and even at the individual level.

• Describe pro and cons of the different modelling techniques, their characteristics and data requirements.
• Identify relevant data sources to parameterise models.
• Implement, parameterise and calibrate models.
• Evaluate the impact of individual heterogeneities on model outcomes.
• Use models to understand mechanisms and to make projections.
• Estimate the effects of parameters uncertainties on model outcomes.
• Critically evaluate published decision analysis modelling studies.

• Apply the acquired knowledge in mathematical modelling in order to: study diffusion processes, individuals and firms interaction, competition and cooperation behaviours and estimate the middle and long term effects of selected actions at the firm or organizational level.
• Apply the acquired knowledge to help firms and organizations make strategic decisions on the basis of model results.
• Assess how changes in the price of a product or on the availability can affect the market.
• Simulate changes in the strategic behaviour of a company and assess their impact.
• Evaluate the costs and benefits of introducing a new product or technlogy.

• Face-to-face lectures
• Guest speaker's talks (in class or in distance)
• Exercises (exercises, database, software etc.)
• Individual assignments
• Group assignments
• Interactive class activities (role playing, business game, simulation, online forum, instant polls)

The learning experience of this course includes face-to-face lectures accompanied by hands-on computer classes, individual and/or group assignments and interactions with guest speakers. 

• During the course students are engaged in a semester-long project where students have to identify a research question, develop and implement a mathematical model, identify relevant data sources to parameterise the model and produce and discuss results. Students then prepare a power-point presentation summarizing the evidences of their assessment. These presentations are used for the student assessment as well as a basis for a discussion of the cases in class, during which students are encouraged to bring their own views and to share insights, comments and conclusions. 
• Attendance: due to this teaching methodology, heavily based on computer work and class participation, attending is strongly recommended.

With the purpose of measuring the acquisition of the learning outcomes the student assessment is based on two main components:

One individual assignment (30% of the final grade):  will be distributed before the mid-term break. Other assignments may be distributed in class although these won’t count for your final grade.

Research project (40% of the final grade): an empirical research brief on a relevant topic of your choice. You could replicate existing studies, test new ideas, develop a new programming code that is relevant for your own research, etc. You could use existing data or collect your own. I encourage you to be adventurous. The style and sophistication of analysis depend on the student's background. The research project can be done individually or as a small group (max 3 people) and will have to be submitted by the date of the first exam session. More details about the term paper will be provided in due course. To be handed in by the date of the written exam,

Final written exam (30%): based on open questions related to the reference materials, which aim to assess the student’s learning level of the main concepts, methods and tools detailed in the teaching material as well as the ability to analyze some implications related to them through their application to a specific case study.

With the purpose of measuring the acquisition of the learning outcomes the student assessment is based on two main components:

Final written exam (60% of the finale grade): based on open questions related to the reference materials, which aim to assess the student’s learning level of the main concepts, methods and tools detailed in the teaching material as well as the ability to analyze some implications related to them through their application to a specific case study. To be handed in by the date of the written exam,

Research project (40% of your final grade): The research project is an empirical research brief on a relevant topic of your choice. You could replicate existing studies, test new ideas, develop a new programming code that is relevant for your own research, etc. You could use existing data or collect your own. I encourage you to be adventurous. The style and sophistication of analysis depend on the student's background. In terms of format and length, more information will be provided during the course. The research project will have to be submitted by the date of the written exam. More details about the term paper will be provided in due course.

Programming codes, reading materials and selected chapters of relevant books are uploaded on the e-learning platform.