| Learning objectives |
The goal of this course is to relate our
knowledge of how decisions are made to a number of techniques and
strategies for improving decision making. This will enable
participants to support and improve their own decision making as
well as to understand the decision making of others. We view the
decision maker as a socially, economically, historically, and
materially situated human - who increasingly uses algorithms for
decision making and struggles with unrealistic demands and
therefore has developed (individually and socially) heuristics,
habits, routines, practices, and conventions which sometimes lead
to algorithm aversion.
By the end of the course, students will be able to reflect on the
complexities of decision making in organizations, their own
decision styles and personal dispositions. They will be able to
make decisions more deliberately and systematically and will be
able to use decision analysis techniques, intuition and group
processes, integrate their values into their decisions.
- Understand and apply models and mechanisms for decision making
in strategic decisions
- Identify unconscious biases when making decisions and solving
problems and reflect on common decision making traps that lead to
fallacious reasoning and unfavorable outcomes
- Identify criteria for when to trust intuition and when to push
for analysis and evidence-based decisions
- Reflect on how to make strategic decisions involving multiple
(and changing) goals and stakeholders
|
| Course prerequisites |
| The course targets different levels of managers,
specialists, and analysts who are involved in organizational
decision making. The course is also relevant for professionals who
would like to understand the challenges and opportunities of
data-driven decision making in organizational settings. |
| Examination |
|
Data-driven
Decision Making:
|
| Exam
ECTS |
3 |
| Examination form |
Home assignment - written product |
| Individual or group exam |
Individual exam |
| Size of written product |
Max. 5 pages |
| Assignment type |
Essay |
| Duration |
Written product to be submitted on specified date
and time. |
| Grading scale |
7-point grading scale |
| Examiner(s) |
Internal examiner and second internal
examiner |
| Exam period |
Summer |
| Make-up exam/re-exam |
Same examination form as the ordinary
exam
|
|
| Course content, structure and pedagogical
approach |
|
In the current competitive business environment, decision makers
need to make decisions quickly and effectively based on abundant
data. Data-driven decision making refers to organizations
systematically collecting and analyzing various types of data,
including input, process, outcome and satisfaction data, to guide,
inform and/or automate a range of decisions from operational to
strategic decisions. Typical examples are recommender systems that
drive product recommendation decisions, credit scoring that drive
lending decisions, employee analytics that drive hiring or
promotion decisions etc. Making decisions includes many
considerations such as weighing risk, understanding the specific
situation encountered, identifying available options as well as
considering long-range implications for the organization.
This course is about understanding and applying data-driven
decision making while taking into consideration the decision
maker’s experience and expertise. By knowing how data-driven
decisions are actually made the students can learn how various
decision techniques and strategies improve the quality of
decisions. Some of these techniques and strategies are founded on
mathematical models or computer software like algorithmic decision
making; others build on theories about awareness and mindfulness.
The course presents a wide range of such techniques covering the
different theoretical approaches to decision making.
|
| Description of the teaching methods |
| Case studies, lectures presenting readings, guest
lecturers from industry and active student involvement in
discussions and reflections. Teaching is based on that students
have read teaching material prior to class |
| Feedback during the teaching period |
| Feedback will be given during lessons, excersizes
and exam |
| Student workload |
| Lectures |
20 hours |
| Preparation and exam |
70 hours |
|
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