Ongoing projects

When deciding between multiple choice alternatives, individuals often make irrational decisions that do not necessarily align with their long-term preferences and good intentions. They are, thereby, often influenced by the environments in which they make their decisions – also called the choice architecture. This susceptibility to environmental influences can also be leveraged by behavioral interventions aiming to support individuals in their decision-making.

In this project, we investigate how such behavioral interventions can be implemented in digital choice environments to support consumers in making healthier and more sustainable online decisions. We, therefore, combine the latest results of behavioral economic and behavioral policy research with state-of-the-art technology discussed in information systems research. Methodologically, we build on experimental studies in online, laboratory, and field contexts.

Contact: Leonard Michels

Throughout the past years, applications that rely on algorithmic processes and artificial intelligence to support humans in making decisions have become increasingly available. Typical examples are recommendations in e-commerce or movie and music streaming platforms. Despite the widespread proliferation of our everyday lives with AI-based technology, there is no clear academic consensus on how and when humans are willing to incorporate AI-based information and recommendations in their decision-making processes.

This project aims to investigate how the use of AI in decision-making can be promoted and optimized based on psychological and behavioral economic research. We, for example, investigate how central or peripheral persuasion appeals affect the acceptance of AI-based recommendations and how the decision-maker’s level of expertise moderates these effects.

Contact: Leonard Michels

Battery Electric Vehicles (BEVs) are promising candidates to provide flexibility to the electric grid by adjusting their charging and discharging processes. However, the magnitude of their contribution, inclusive of human mobility behaviour, remains unclear. In this work, a real-world GPS-labelled mobility dataset measured on 1000 cars across 2 years, comprising over 4 million trips is used to simulate BEV charging and quantify the unidirectional flexibility potential provided by delaying charging. Flexibility is modelled as tuples of power and time for different durations at different times of the day under varying parameters of BEV battery size, charging power, and home, work and public charging locations. Eight distinct driver groups identified by clustering techniques on driving behaviour provide insights to access the quantified flexibility from the right groups at the right times. The inclusion of BEV user range anxiety as a parameter through BEV user surveys lends a realistic perspective to the quantified flexibility potential. The results help grid operators assess the quantity of available realistic flexibility potential from BEVs for power system operation. Furthermore, to energy regulators and policymakers, the results provide a basis for the design of new business models and energy market products for flexibility provision.

Contact: Prakhar Mehta

How does the configuration of an energy community impact its key performance metrics (prosumer profitability, self-sufficiency rate of the community, and grid impact)? Through a deterministic techno-economic analysis based on high-resolution real-world demand data from 3,594 households, this project analyses how the configuration of a solar energy community (EC) impacts its economic and technical performance. EC planners, policymakers and grid operators can build on these results as rules of thumb to incentivize EC formation while maximizing welfare for all stakeholders.

Contact: Prakhar Mehta