040047 SE Topics in Evidence-Based Decision Making (MA) (2018W)

Content: Digital services permeate almost every aspect of life, reshaping business transactions and social interactions alike. This course investigates how data-driven approaches of decision making and decision analysis can shed light on old and new questions in economics and business. The purpose of this course is aimed to introduce students to recent research trends, both in terms of methods and content, and to discuss critically how they can be applied for decision making in firms and in organizations.
The course consists of two parts. In part 1, we review basic concepts of causal inference through randomized experiments, requirements for successful digital experimentation, and statistical concepts of prediction. In part 2, we investigate how these concepts can help generate insights regarding behavior for a number of applied economic and business questions. The fields of research from which applications are selected include behavioral economics, game theory, organizational behavior, strategic management, and others (see reading list).
The course consists of two parts. In part 1, we review basic concepts of causal inference through randomized experiments, concepts of prediction, and requirements for successful digital experimentation. In part 2, we investigate how these concepts can help generate insights regarding behavior for a number of applied economic questions. The fields of research from which applications are selected include behavioral economics, game theory, organizational theory, management, and others (see reading list).

Organization of the course: In part 1, each student presents the solution to one homework exercise in front of the class (about 20’) using a live demonstration in ‘R’. In part 2, we focus on applications. Each student selects one or two papers from the reading list on which s/he acts as the ‘expert’. The reading list is handed out in the first session. It is therefore imperative to participate in the first session. Students who cannot (for a good reason) participate in the first session should send me an e-mail one week before the first session.
Classroom discussion in part 2 of the course is organized as follows: The topic/paper is introduced in a very concise manner (extended abstract; max 5’) by one student. The student who acts as ‘expert’ then provides a concise presentation of the paper (presentation; about 20’) using slides. We then discuss questions of technical detail as well as questions on context and interpretation.
A successful ‘expert’ is able to summarize each section/paragraph of her paper in his or her own words at any time during the discussion, guides the discussion and is able to answer most questions from fellow students and the instructor. Participants are expected to read all papers, prepare questions, and to contribute their own thoughts and views on the paper. Participation is essential. Critical thought, controversy and debate is welcome (once we are clear about what the paper says).