Prof. Dr. Sandra Ludwig
Institute of Economics
Helmholtzstraße 18, Room 1.07
89069 Ulm
Tel: +49 (0)731/50-23549
Email: sandra-ludwig(at)uni-ulm.de
web: Sandra Ludwig
ULESS (Ulm Laboratory for Economics and Social Sciences) – Computer laboratory for experiments in economics
ULESS (Ulm Laboratory for Economics and Social Sciences) – Computer laboratory for experiments in economics
In the computer laboratory ULESS (https://www.uni-ulm.de/mawi/uless/ ) we conduct research in the field of experimental economics.
Experimental economics is a method that allows us to analyse individual decision making in economic situations. Also other disciplines use experiments in their research. Experiments contribute to interdisciplinary and innovative research.
We investigated how individuals’ self-assessments depend on whether the accuracy of the self-assessment is observable to others and discovered significant gender differences. In our study participants solve arithmetic problems and based on their performance we determine a ranking. Participants then estimate their rank in the ranking. We find that women downgrade
their self-assessment given observability, while men do not.
Women seem to avoid the shame they may have if others observe that they overestimated themselves.
For real life situations one can imagine a job interview in which one has to assess one’s (relative) performance in the new job and one’s true performance later becomes apparent. Shame aversion of women may then induce women to make less confident statements about their abilities (compared to men) to avoid overestimating their abilities. Thus, shame aversion may shed light on why women are less successful on the labor market.
Ludwig, S., Fellner-Röhling, G. and C. Thoma (2017). „Do women have more shame than men? An experiment on self-assessment and the shame of overestimating oneself,” European Economic Review 92, 31-46. https://www.sciencedirect.com/science/article/abs/pii/S0014292116302197
In another project we investigate the effect of social information on self-assessment. People often fail to correctly self-asses their ability or performance. Specifically, individuals tend to be overconfident concerning their absolute and relative performance. Biased self-assessments can have severe consequences when they inform individual decision making. Thus, it is important to find effective debiasing strategies. We focus on the effect of informational nudges, more precisely, on the responsiveness of individual’s relative self-assessments to social information.
Our study is the first approach to investigate the effect of social information on self-assessments. Social information is pervasive in everyday life and has become increasingly available in a digitized and connected economy.
In our study we compare the effectiveness of different types of social information: participants either learn their close peers' average absolute performance, average self-assessment or average bias of self-assessments. Our results suggest that social information can help debiasing self-assessments, but not all types of information are equally effective. Only learning about the average bias of peers improves own self-assessments.
If participants can choose which kind of social information they want to receive, they mostly prefer (self-relevant) information about their peers' absolute performance, which is the least helpful type of social information. Consequently, social information may not improve self-assessments.
Other Topics from the faculty of Mathematics and Economics
American mathematicians are calling on their colleagues to refrain from collaborating with the police. They say it lends a scientific appearance to a racist system.
Mathematics is clarity. It can describe aspects of our ambiguous world, but it is itself unambiguous. In mathematics, only that which can be proven is true. It seems detached from the agitated reality we are currently seeing on the streets of the United States, where George Floyd’s death first triggered demonstrations and then eventually the toppling of monuments. But now a group of mathematicians has brought their sedate science into this tempest.
IThe ever-increasing presence of cars in cities leads to more and more congestion as well as air and noise pollution, among other things. It also takes up a lot of space. It is therefore vital to strengthen local public transport as a means for more sustainable urban mobility. The Institute of Sustainable Corporate Management at Ulm University is thus investigating whether temporarily free local public transport can be an effective measure in this regard. read more
Experience shows that the expectations people have of the standard of living they will able to achieve in old age often deviate from reality. There are numerous arguments in favour of an early, comprehensible and realistic presentation of the retirement income that can be expected. This requires an easily accessible source of relevant information on the status of one’s own individual pension coverage – summarized across all three pillars (statutory, company and private pensions). This information should be as complete, as comprehensible, as reliable and as comparable as possible. In addition, it is crucial that cross-pillar pension information also be efficiently usable and cost effective, to ensure it is both accepted by the people and supported by pension institutions.
The study is available as a complete document and as a summary.
Developing alternatives for sustainable textile consumption among young people
A joint project of Ulm University and the Technical University of Berlin, BNTextillabor aims to conduct research on more sustainable fashion consumption among young people. The consumption behaviour of young people was investigated and concepts for a more sustainable textile consumption were developed. Can the changed behaviour of young people subsequently be transferred to other areas of consumption? Based on the results, teaching and learning formats for different types of schools will be developed with the objective of influencing the behaviour of young people in a sustainable manner.
As part of an advanced CSE project, the use of a leap motion controller (LMC) in the field of biomechanics was to be reviewed. The LMC is a computer hardware sensor device, which, like a mouse, supports hand and finger movements as input, but does not require hand contact or touch. This sensor is designed to help record a hand movement that is then transferred to the AnyBody Modeling System™ (AMS). The AMS can be used to determine muscle and joint loads for a given body motion in an inverse dynamic simulation that will help optimise the healing processes of a fracture.
The analysis of fracture healing processes and the optimisation of patient treatment is an ongoing area of research. In addition to experimental methods, computational modelling also plays an important role in understanding the process of bone growth. Mechanical stimulation is an essential factor for simulating the healing process.
How social and mobile technologies can help
The risk of long-term unemployment is particularly high for unemployed persons over the age of 50. Researchers at the Institute for Business Analytics at Ulm University have developed a new, innovative approach to support this target group in their job search using social media: digital peer group counselling (DIGIPEG). Unemployed individuals over 50 support each other digitally, anonymously and voluntarily. DIGIPEG was introduced throughout Baden-Württemberg as part of a two-year application-oriented research project. Results show that digital peer groups significantly improve the job search – as measured by application activity, application skills and return to employment.
Those who lose their jobs at 50+ generally have a harder time getting back into the workforce than younger unemployed individuals. In addition to the job search, the psychological burden is also a challenge for those affected. Along with the risk of long-term unemployment, the threat of poverty in old age increases. Particularly in these times of demographic change, solutions to this problem are needed more than ever.
Discrimination by algorithms is increasingly perceived as a societal and legal problem. In response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. However, some of them are known to contradict each other, both philosophically and/or mathematically. In recently published work, BCCP Senior Fellow Philipp Hacker, together with co-authors Meike Zehlike and Emil Wiedemann, propose the continuous fairness algorithm (CFAθ), which enables a continuous interpolation between two contradictory fairness definitions, namely individual and group fairness. Individual fairness is commonly understood as "similar individuals should be treated similarly." Group fairness posits that the chance of receiving a positive outcome should be equal across all protected demographic groups. read more
The tomographic reconstruction of the 3D microstructure of real materials is very time-consuming and cost-intensive. Therefore, mathematical models are needed to systematically investigate the influence of the microstructure on mechanical material properties. They allow the simulation of virtual, but realistic 3D microstructures on the computer. The mechanical properties of these structures are then determined by numerical simulations. In particular, the use of AI methods to accelerate the numerical simulation of the fracture behaviour of titanium aluminides is a promising approach. read more