Decision analytics utilizing causal models and multiobjective Optimization (DEMO)


Decision analytics utilizing causal models and multiobjective optimization (DEMO) is a thematic research area of the University of Jyvaskyla. The goal of DEMO is creating means to make the most of the data and support data-driven decision making.

The international advisory board recognizes DEMO's unique position at the forefront of international research on data-driven decision support. Our research can be characterized as explainable artificial intelligence (XAI) for human decision making. 

DEMO focuses on explicit, concrete decision problems that can be presented with mathematical formalism. Predictive analytics, statistical modelling, causal inference, prescriptive analytics and multiobjective optimization are the key elements needed to create a seamless chain from data to decision. We refer to this as decision analytics. Thanks to method and software development, decision analytics will be applied to support e.g. other profiling areas of the University of Jyväskylä, especially related to education and health in dealing with their decision problems.

DEMO is one of the research areas prioritized by the Operational Agenda of the University of Jyväskylä (2016-2020). The project of strengthening this area is supported by the Academy of Finland

The project is implemented jointly by the Faculty of Information Technology and the Department of Mathematics and Statistics in collaboration with other departments of JYU. The Principal Investigators:

  • Kaisa Miettinen, Director of DEMO, Professor of Industrial Optimization, Faculty of IT, Field: multiobjective optimization and multiple criteria decision making (around 10K citations in Google Scholar and 2K in Scopus). She is the Immediate-Past-President of the International Society on Multiple Criteria Decision Making.
  • Juha Karvanen, Vice-director of DEMO, Professor of Statistics, Department of Mathematics and Statistics, Field: causality, study design and missing data (more than a thousand citations in Google Scholar).
  • Tommi Kärkkäinen, Professor of Mathematical Information Technology, Faculty of IT, Field: development of novel and scalable neural computing and data mining methods and algorithms (h-index 19 in Google Scholar).

The core concept is the seamless chain from data to decision making:


In DEMO, we work with various application fields including health and well-being, learning and teaching, energy and forest treatment planning.