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Course 2022-2023 a.y.

30601 - COMPUTATIONAL APPLICATIONS IN MANAGEMENT

Department of Management and Technology

Course taught in English

Go to class group/s: 31

CLEAM (3 credits - I sem. - OP  |  SECS-P/07) - CLEF (3 credits - I sem. - OP  |  SECS-P/07) - CLEACC (3 credits - I sem. - OP  |  SECS-P/07) - BESS-CLES (3 credits - I sem. - OP  |  SECS-P/07) - WBB (3 credits - I sem. - OP  |  SECS-P/07) - BIEF (3 credits - I sem. - OP  |  SECS-P/07) - BIEM (3 credits - I sem. - OP  |  SECS-P/07) - BIG (3 credits - I sem. - OP  |  SECS-P/07) - BEMACS (3 credits - I sem. - OP  |  SECS-P/07) - BAI (3 credits - I sem. - OP  |  SECS-P/07)
Course Director:
DANILO MESSINESE

Classes: 31 (I sem.)
Instructors:
Class 31: DANILO MESSINESE


Mission & Content Summary
MISSION

We live in the era of information. A lot of data and, more in general, information is easily available to managers and, more broadly, to decision-makers. Therefore, it might seem that the life of decision-makers is easier nowadays, as they help the decision process with tons of data and insights. Is it true? Or is it how data and information are collected and interpreted that creates a source for competitive advantage? The purpose of this course is to provide an opportunity to learn a more rational and systematic approach to decision-making; to be able to achieve clarity of action in making any decision on which you focus your attention. The course provides a formal model of optimization of information acquisition and overview about the process of using data to inform the decision-making process of firms and validate a course of action before committing to it.

CONTENT SUMMARY

The course focuses on the best practices that can be adopted to deal with conditions of uncertainty, when facing decisions problems. Namel

  • A structured course of actions to make more efficient decisions
  • a language to describe decisions and distinguish strategies, scenarios, and outcomes
  • experimentation as a tool to solve critical uncertainties and to get feedback on innovation
  • the adoption of optimization and simulation techniques to solve decisions problems

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

 

  • Explain how to adopt a systematic approach to decision-making under uncertainty
  • Identify the sources of uncertainty behind strategic decisions and address them using experimentation
  • Illustrate how to integrate all steps by optimization and simulations to reduce the uncertainty
  • Collect data with the specific aim to rationally inform decisions
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...

 

  • Follow an optimal policy to acquire information when making a decision under uncertainty
  • Manage the cost of acquire information in a strategic way
  • adopt a proper language to describe decisions and distinguish strategies, scenarios, and outcomes
  • Assess how decisions problems can be managed by reducing the underlying uncertainty

Teaching methods
  • Face-to-face lectures
  • 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, in addition to face-to-face lectures, case studies discussions, group assignments, concrete examples of decisions problems in companies facing uncertainty. Aside real cases, theoretical insights will be provided.

One group work is assigned during the course: students are expected to discuss and analyse the assignment within their group and deliver a presentation about their findings.

The active participation in class is also considered for the overall student assessment.

Attendance: due to this teaching methodology, heavily based on interaction and class participation, attending is 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    
    ATTENDING STUDENTS

    With the purpose of measuring the acquisition of the above-mentioned learning outcomes the student assessment is based on three main components:

    1. In-class participation (10% of the final grade) aimed at testing the student ability to interact in a multicultural environment and to think critically through contribution given to the class discussion.

    2. One group assignments (40% of the final grade) designed for the purpose of verifying the student ability to:

    • Analyse decisions problems and identify the main elements, namely the possible strategies, outcomes and scenarios
    • Apply the appropriate approach and methodologies (e.g. experimentation) learnt in class to address decisions problems
    • Work on a team and individually and deliver a clear and articulated report about the relevant outcomes

     

    3. Final written exam (50% of the final grade), based including closed questions only related to the topics covered in class, which aims to assess the student’s learning level of the methodologies and concepts discussed during the course.

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

     

    The attendance are measured by the specific app available to all students. To take the exam as an attending student, an attendance rate equal to or higher than 75% must be reported.

    NOT ATTENDING STUDENTS

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

    Last change 17/06/2022 16:05