Corinna Birner, M.Sc.
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
- 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.
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).
Participants should get familiar with the core concepts and theories of Psychology and how they can be implemented and combined for interdisciplinary research.
Further information on the exam will be given in the first course session
Basic knowledge in microeconomics and econometrics.
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.
1 Penalized Regression (Lasso, Ridge, Elastic Net)
2 Tree and Random Forest
3 Neural Net
4 Unsupervised Machine Learning
5 Reinforcement Learning
- An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani). Download: http://www-bcf.usc.edu/~gareth/ISL/
- Reinforcement Learning: An Introduction (Richard Sutton, Andrew Barto). Download: https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
July 19th - July 23th, 15:15 - 16:45 h and 17:15 - 18:45 h CEST
Participation, home assignment