Center for Empirical Studies - CEST - Ludwig-Maximilians-Universität München

Welcome to the website of the Center for Empirical Studies (CEST): Data Analysis, Modelling & Knowledge Discovery in Social Sciences, Economics and Humanities.

The Center of Empirical Studies is a research initiative linking empirical and methodological research groups from several different faculties. Methodological challenges include modeling unobservable heterogeneity or measuring latent traits, that recur in similar form in, for example, economic, sociological and psychological tasks. The aim of the Center of Empirical Studies is thus to enhance the explanatory power of empirical studies by means of new methodological developments. The initiative is organized in three interacting areas:

Statistical Learning, Data Mining & Knowledge Discovery

The broad goal of data mining and knowledge discovery is the investigation and discovery of specific associations from large scale databases by means of data processing approaches and algorithms. The methods are based on machine learning and artificial intelligence approaches, some of which are advancements of classical techniques from multivariate statistics. This expanding field of research has developed from the interaction of computer scientists and statisticians and is predestined for cooperations across these fields. Important application areas, that are represented in the projects, cover the modelling of risks and decisions.

Measurement and Evaluation

It is often hard to measure quantities in economics and social sciences, like individual consumer spendings or effectiveness, precisely. As data collection itself forms a social process, results are more or less biased - answers are for example affected by social norms. Projects falling into the category "Measurement and Evaluation" investigate biased measurements and their mechanisms, techniques to reduce the observed bias shall be developed - such that we meet the complex requirements of economics and social data.

Dynamic Modeling

A special methodological challenge is the modeling of time-dependent data. Here the main focus is on understanding the dynamics of time-dependent processes and the forecasts depending upon them. Application examples range from dynamic modeling of financial- and real-economic interactions to the circadian rhythms of humans.