Research on decisions, models, and data (DMD) is an interdisciplinary field that examines how individuals and organizations use models to interpret information and make decisions in complex environments.
These models may be mathematical, cognitive, or conceptual frameworks that help uncover new possibilities and improve decision-making.
Why this research matters
The increasing availability of data and the growing use of intelligent systems raise important questions about the role of human judgment in organizations.
At the same time, recent global challenges have highlighted the limits of purely data-driven predictions and the need to make decisions under uncertainty.
Core idea
DMD research is based on the premise that human decision-makers are most effective when they use models to represent and understand the world.
These models allow individuals to go beyond available data, draw inferences, and identify new courses of action in ways that machines cannot.
The research explores how models can be developed and applied effectively, and how data-driven technologies can support—or constrain—human decision-making.
Research focus
This research area focuses on:
- Decision-making in data-rich environments
- Modeling under uncertainty and limited information
- Human judgment and cognitive biases
- Human–machine interaction in decision processes
Approach
The research draws on multiple disciplines, including:
- Data science and analytics
- Cognitive psychology
- Sociology
- Economics
This interdisciplinary perspective supports a deeper understanding of how data and human reasoning interact in real-world decision-making.
Connection to DEEP
This research area is part of the DEEP Institute for Deep Tech Innovation at ESMT Berlin.
DEEP connects research, entrepreneurship, and investment to support the development of deep-tech ventures and innovation ecosystems.
Publications
The publications below highlight research on decision-making, modeling, and data in complex environments.
Research leadership
