Ongoing research projects at the School of Business and Economics
Research at the School of Business and Economics is largely driven by cooperation within collaborative research projects which are predominantly funded Deutsche Forschungsgemeinschaft. Within these collaborative research consortia the School cooperates with partner instituations in Berlin, Germany and beyond.
Collaborative Research Centres and Transregios
The CRC TRR 190 “Rationality and Competition” combines methods and insights from “behavioral” and “neoclassical” economics to better understand important applied economic problems and to reach more robust economic policy conclusions.The CRC addresses two sets of questions. First, questions about method: How can we use the existing, often rather conceptual insights about behavioral deviations from standard theory and apply them to the traditional fields of economic analysis? Are the existing empirical or experimental demonstrations of behavioral biases – such as reference-dependent preferences, over-confidence, or social preferences – ready to use? Do they capture relevant aspects of economic behavior in real world settings, in particular in competitive environments, where market forces punish irrational behavior?Second, questions about substance: In what areas of economic research is it necessary to engage with a richer model of decision making? In what areas are traditional concepts sufficient? Is the nature of the transmission mechanism the same in both analyses? Do the existing empirical demonstrations of behavioral biases have implications of large economic significance?In the first funding period, the CRC has made important contributions to this research agenda, documented in hundreds of publications and research papers in both basic and applied research. Our work program in the second funding period builds on these results – with a few adjustments: First, we identified several highly promising fields that require more attention, including innovation, inequality, and gender economics. Second, the CRC will also highlight several important new methodical questions, in particular the analysis and measurement of biased beliefs and how to operationalize the notion of “identity”.
Funding period: since 2017
Participating members of the School: Georg Weizsäcker (speaker), Anastasia Danilov, Dirk Engelmann, Sonja Greven, Daniel Klapper, Anja Schöttner, Alexandra Spitz-Oener, Roland Strausz, Nikolaus Wolf
Further participating institutions: Ludwig-Maximilians-Universität Munich
Further information: Website
The Collaborative Research Center/Transregio "Accounting for Transparency" explores how accounting influences transparency and how transparency affects society. Calls for increased transparency are pervasive, particularly after the recent series of financial crises. Many transparency-related regulations in the area of taxation as well as financial and managerial accounting significantly increased the reporting burden of firms. However, public trust in the business sector tends to be very low, particularly in Germany. The historical role of accounting is to aggregate firm-level information into quantitative data. These data are communicated to insiders and outsiders of the firm, who use it to levy taxes and to support investment as well as other economic decisions. The digital revolution has drastically changed the information environment: Information has become ubiquitous, is being generated by diverse senders and distributed via a variety of channels to heterogeneous receivers who process and analyze the data and base various decisions on it. It is unclear how the traditional methods of accounting, which focus on numbers, facts, and verified judgments, can continue to create transparency in this transformed landscape.The researchers of the TRR address this topic. They collect field data to analyze the perception, processing and handling of accounting information. They study how regulations, behavior, and preferences of economic agents shape accounting information and its effects on the transparency of firms. One focus in this regard lies on the transparency of the business taxation system. Furthermore, they assess the consequences of transparency for firms, their stakeholders and the general public. The insights from these activities will allow a well-founded assessment of regulatory reforms in the area of business taxation and financial reporting. Thus, the TRR will contribute to evidence-based policy making and to establishing a transparent tax system. Consequently, the results of the TRR 266 will contribute not only to the academic debate but also to the transparency of the economic system.
Funding period: since 2019
Participating members of the School: Joachim Gassen (vice speaker), Ulf Brüggemann, Ralf Maiterth, Anja Schöttner
Further participating institutions: Universität Paderborn, Universität Mannheim
Further information: Website
Research Units
High-throughput measurements in the biomedical sciences such as stacks of images, genome sequences or time-series constitute structured data that are characterized by their inherent dependencies between measurements, often non-vectorial nature and the presence of confounding influences and sampling biases. For example, population structure, systematic measurement artifacts, non-independent sampling or different group age distributions can lead to spurious results if not accounted for. Deep learning excels in many applications on structured data due to the ability to capture complex dependencies within and between inputs and outputs, allowing for accurate prediction. Despite recent advances in explainable artificial intelligence and Bayesian neural networks, deep learning still has limitations with respect to its assessment of uncertainty, interpretability, and validation. These, however, are important components in order to go beyond prediction towards understanding the underlying biology. To this end, statistics has traditionally been used in the biomedical sciences due to interpretable model output and statistical inference, which i.a. provides quantification of uncertainty, corrections for confounding and testing of hypotheses with statistical error control. Methods from classical statistics, however, have limitations in their modelling flexibility for structured data and their ability to capture complex non-linearities in a data-driven way.In this research unit we bring together experts from machine learning and statistics with a track record in biomedical applications to address the following overarching objectives:(O1) to integrate deep learning and statistics to improve interpretability, uncertainty quantification and statistical inference for deep learning, and to improve modeling flexibility of statistical methods for structured data. In particular, we will develop methods that provide statistical inference for structured data by quantification of uncertainty, testing of hypotheses and conditioning on confounders, and that improve explanations of structured data through hybrid statistical and deep learning models, population- and distribution-level explanations, and robust sparse explanations.(O2) to create a feedback loop between this methods development and biomedical applications, where we account for the needs in the analysis of the data when developing new methods and generate biomedical insights from applications of the developed methods to the data. Applications include analysis of MRI, fMRI and microscopy images, longitudinal disease progression modeling, DNA sequence analysis, and genetic association studies.
Funding period: since 2022
Participating members of the School: Sonja Greven (speaker)
Further information: Website
Leibniz Science Campus
Promoting consumers’ rights, prosperity, and wellbeing are core values of the European Union. A wide array of laws, institutions, and regulations – which can be generally termed as consumer policies – aim at protecting consumers by ensuring adequate and truthful information in the marketplace as well as preventing firms from engaging in unfair and competition-impairing practices.
While some of these policies directly affect consumers, for instance consumer protection and dissuasive taxation, others only indirectly benefit consumers by governing market functions through regulation and competition policies. The interactions between these different policies are not yet fully understood.
The aim of the Berlin Centre for Consumer Policies (BCCP) is to create an enduring international platform in the broad area of competition and consumer policies, where excellent interdisciplinary research can actively and effectively inform policy makers on issues that are highly relevant to the current policy debate.
The strong focus of the partner institutions in industrial organization, behavioral economics and competition law, as well as a strong policy focus makes Berlin the perfect location for a ScienceCampus focused on consumer policies. The main goal of BCCP is to fully exploit, reinforce, and institutionalize this exceptional environment to answer specific questions on the optimal design of consumer policies.
Funding period: since 2015
Participating members of the School: Dirk Engelmann, Wolfgang Härdle, Daniel Klapper, Roland Strausz, Georg Weizsäcker
Further participating institutions: Freie Universität Berlin, Technische Universität Berlin, ESMT Berlin, Hertie School, German Institute for Economic Research (DIW), Berlin Social Science Center (WZB)
Further information: Website