Syllabus

LEARNING OUTCOMES

Management Science uses analytical models to help make better business decisions. This course focuses on models for supporting decision making under uncertainty, risk and multiple objectives. After taking the course the student can (i) recognize the types of real-life business problems where the use of models adds value, (ii) interpret results of these models to derive defensible decision recommendations, and (iii) build and solve these models computationally to support business decision making.

Credits: 6

Schedule: 09.01.2023 - 21.02.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Eeva Vilkkumaa, Ilkka Leppänen

Contact information for the course (applies in this implementation):

Dr Ilkka Leppänen

ilkka.j.leppanen@aalto.fi

I am available for feedback and consultation during lecture times, for other times please message/email me to book a Teams meeting

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

• valid for whole curriculum period:

  Simulation, decision trees, value of information, expected utility theory, risk attitudes, stochastic dominance, risk measures, multi-attribute utility/value theory, modelling uncertainty and multiple objectives in optimization problems.

• applies in this implementation

  Uncertainty and multiple objectives are inescapable features of many decision problems. We focus on management science methods that support decision making under uncertainty and/or multiple objectives. Please see schedule for a detailed list of topics.

  Some necessary background concepts from applied probability are also covered.

  
  

Assessment Methods and Criteria

• valid for whole curriculum period:

  Coursework and exam

• applies in this implementation

  Final points, which consist of exam points (50%) and assignment points (50%), determine the final grade:  >50p ->1; >60p -> 2; >70p -> 3; >80p -> 4; >90p -> 5. These bounds may be relaxed during final grading. To pass the course you need to attain at least half of the exam points.

  There are three assignments with deadlines during the course. Each assignment consists of several problems or cases, which usually require the use of spreadsheets, programming scripts, or other mathematical software to solve.

  Assignments are individual work. You may discuss the problems with your fellow students, but you must submit individual answers. In the exam you will need to individually solve equivalent problems. Submitting a copied answer or a solution is academic misconduct and is strictly forbidden.

  
  

Workload

• valid for whole curriculum period:

  Contact teaching, individual work, exam.

• applies in this implementation

  Contact teaching constitutes of lectures, assignment demonstrations, and assignment feedback.

  Individual work constitutes of lecture preparation (12h), work on the assignments (101h), and revising for the exam (8h).

DETAILS

Study Material

• applies in this implementation

  The textbook can be helpful but you will be able to perform well in the course without it.

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

SDG: Sustainable Development Goals

8 Decent Work and Economic Growth

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  Teaching Language : English

  Teaching Period : 2022-2023 Spring III
  2023-2024 Spring III

• applies in this implementation

  The course will be delivered in hybrid mode, i.e. lectures are in-person and will be streamed and recorded.
  

Details on the schedule

• applies in this implementation

  Two 3-hour teaching sessions per week (Monday and Wednesday mornings)

  In each session, approximately 2 hours are spent for the lecture and 1 hour for working towards the assignment submissions.

  Recordings of the teaching sessions will be available after each session. However, please attend all the sessions at their allocated time slots and preferably in-person. Attending the teaching sessions is highly recommended, as this is known to lead to superior learning outcomes and performance in the exam.

  
  

Description