piwik-script

Intern
    Chair of Labour Economics

    Summer School 2021

    Lecturer

    Corinna Birner, M.Sc.

    Syllabus

    Content:

    Psychology and Economics seem to be two opposing disciplines as each one of them has its own research methods, its own questions and its own self-perception. Nevertheless, both disciplines have one thing in common: they want to know more about human behaviour and understand why we act like we do in our daily life. In this course, we will compare the two disciplines and look at different approaches to determine human behaviour, focussing on different psychological constructs that have been introduced into Economics. We will look at intelligence, personality, the influence of society and briefly dive into the field of Neuroeconomics. The course wants to provide insight on psychological variables and constructs and how Economists can use these in order to obtain an interdisciplinary holistic view on human behaviour.

    The lecture will cover the following topics:

    I. Introduction to psychological concepts
    II. Intelligence in psychology and economics
    III. Personality and emotions in psychology and economics
    IV. Behavioural measurements in psychology and economics
    V. Social identity and norms in psychology and economics
    VI. Introduction to neuroeconomics

    Literature:

    • Schram, A. & Ule, A. (Eds.) (2019): Handbook of Research Methods and Applications in Experimental Economics. Edward Elgar Publishing.
    • Lewis, A. (2018): The Cambridge Handbook of Psychology and Economic Behaviour. Cambridge University Press.
    • Smith, E., Mackie, D. &Claypool, H. (2014): Social Psychology. Taylor & Francis.
    • Kim, N.S. (2018): Judgment and decision-making in the lab and in the world. PALGRAVE.
    • Additional material will be announced during the first session.

    Organization

    Structure:
    Due to the Covid-19 Pandemic, the course will be taught online from July 5th – July 16th, 2021 via live lectures. Time for live lectures, including group work: 2 p.m. – 5 p.m. (Berlin time).

    Objective:
    Participants should get familiar with the core concepts and theories of Psychology and how they can be implemented and combined for interdisciplinary research.

    Examination:
    Online exam
    Further information on the exam will be given in the first course session


    Prerequisites:
    Basic knowledge in microeconomics and econometrics.

    Lecturer

    Prof. Anthony Strittmatter, Ph.D.

    Contact:
    Anthony.strittmatter@ensae.fr

    Syllabus

    Contents & Objectives:

    The course provides an introduction to machine learning methods. Supervised and unsupervised machine learning methods as well as reinforcement learning algorithms are covered. The focus of the lecture is on supervised machine learning methods, which include penalised regression methods, tree-based methods and neural networks. The unsupervised machine learning methods that are discussed include clustering and principal component analysis. Bandit algorithms are an example of reinforcement learning algorithms. The lectures are accompanied by coding sessions in which the machine learning methods are applied to real-life economic and business problems (using the open source software R).

    Learning objectives / competences: 1) Students will be familiar with the principles of prediction. 2) Students will be able to distinguish between supervised and unsupervised machine learning methods. 3) Students will deploy machine learning methods to economic and business prediction problems. 4) Students will know how reinforcement learning algorithms can be used for decision-making.

    Topics:

    1 Penalized Regression (Lasso, Ridge, Elastic Net)
    2 Tree and Random Forest
    3 Neural Net
    4 Unsupervised Machine Learning
    5 Reinforcement Learning

    Literature:

    Organization

    Course period:
    July 19th - July 23th, 15:15 - 16:45 h and 17:15 - 18:45 h CEST

    Target group:
    Bachelor Students

    Prerequisites:
    Statistics

    Assessment:
    Participation, home assignment

    ECTS:
    5