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Modelling Complexity

Modelling Complexity for Decision-Making - How can New Modelling and Simulation Approaches Improve Data-Driven Decisions?

“We are buried beneath the weight of information, which is being confused with knowledge.” Tom Waits

 In today’s world of data overload, the search for usable information can be likened to taking a sip from a fire hose.

The term VUCA – volatile, uncertain, complex and ambiguous – was coined in the late ‘80s; since then, the world has truly become all these things for decision-makers. New model approaches are needed to address these challenges. At the same time, countless ideas for new, progressive analysis and modeling approaches have been created to address complex issues, and their number is growing rapidly.

This gives rise to new research questions that are equally relevant for scientists and practitioners:

Which new data science approaches are best suited for the various marketing and decision-making questions? How can their performance be measured? What are the benefits and drawbacks of the various approaches?

  • What role does human expertise play in a world of data that is increasingly controlled by intelligent machines? How will humans and artificial intelligence interact in the future to make data-driven decisions? Under which circumstances is the delegation of decisions supported, and what impact does this have?
  • How can machine learning approaches be made more understandable and explainable? What are the benefits of greater transparency in machine learning and AI models?
  • How can complex and volatile data be visualized? Which visualizations require analysis of data and lead to findings that can really contribute to better decisions?

Our research projects: