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Course 2020-2021 a.y.

20599 - SIMULATION AND MODELING

DSBA
Department of Social and Political Sciences

Course taught in English


Go to class group/s: 31

DSBA (8 credits - II sem. - OP  |  SECS-S/04)
Course Director:
ALESSIA MELEGARO

Classes: 31 (II sem.)
Instructors:
Class 31: ALESSIA MELEGARO


Mission & Content Summary
MISSION

How can we make decisions under condition of uncertainties? Mathematical modelling has now become a quantitative systematic approach to deal with decision-making processes. These techniques are used in all areas of the public and private sector as they allow to explore the mechanisms underlying certain processes and to make projections on future scenarios under various alternative assumptions. The course provides students with new tools that derive from the demography and epidemiology areas of research and that allow to describe, assess and deal with complex choices and identify the optimal solution.

CONTENT SUMMARY

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.

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • 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.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • 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.

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

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.

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Group assignment (report, exercise, presentation, project work etc.)
  •   x  
  • Active class participation (virtual, attendance)
  •   x  

    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS

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