- Risk Communication
- Visualisation for communication of risk assessments
- Parameters Elicitation in Bayesian Networks
- Imprecise Probability
- Constraint Programming and Stochastic Optimization
- Risk Models from Text
Making rigorous decision making under uncertainty scalable within a business environment
Our project investigates methods and algorithms to improve the effectiveness in the use of risk models in decision making.
A team of researchers from IBM is collaborating with faculty and students from Irish universities (University College Cork, Dublin City University and University College Dublin) along four research axes:
- Natural Language Processing: Simplify the creation of risk models by extracting risk information from structured and unstructured data, with a focus on text.
- Imprecise Probability: Lessen the input requirements for risk models by develop efficient decision theoretic algorithms to work with imprecise probabilistic information such as ranges or orders of magnitude.
- Constraint Programming applied to Stochastic Optimization: Find effective way of solving complex stochastic problems by combining constraint programming with stochastic programming and address fundamental challenges in risk-based optimization.
- Risk Communication Experiment with novel ways for users to communicate about and interact with risk information through the development of novel risk visualization support and the refactoring of a web-based expert elicitation tool.
Curious? Take our survey about the communication of side effects of prescription medicine.
We describe the research objectives and activities for each sub-project in a separate section (see left).