We are witnessing a huge conceptual change in how almost all data (personal, commercial, scientific and government data) is being processed, managed and stored. Nowadays, almost every information is being processed by computer - even plants are being watered by automatic systems. However, reality is not perfect we need to base machine computation on uncertain data. For example, we have no perfect knowledge on future sun exposure of plants nor on the amount of rain. This shift of computational paradigms poses new challenges for algorithms and system managing this uncertain data.

In classical view on computations it is assumed that one has complete knowledge of problem instance where it is handed to an algorithm and that the problem instance can be as pessimistic as possible. However, in most practical applications, this assumptions rarely hold. On one hand, problem instances that we usually solve in practice have significant structure, e.g., graphs arising from real world data almost always follow power-law distribution. On the other hand, when the instance of the problem realizes we often do not know all its details, i.e., some data might be still missing or it might have stochastic nature. Examples include jobs with unknown runtimes, stochasticity in robot motion planning, and unknown agent types in mechanism design.

As a consequence, many results from theoretical computer science are of limited importance, as they can be too pessimistic (e.g., givingstrong hardness of approximation for problems where heuristic methods have significant success) or too optimistic (e.g., producing algorithms that cannot be used due to their brittleness to the instance specification). Both of these issues are fundamentally related, with a strong and vital interplay that incorporates topics such as smoothed analysis, semi-random models, instance-stability notions, and stochastic and robust optimization.

The research programs in these directions have gained considerable momentum recently, and it seems to be time for a mayor research effort related to these areas that could lead to a new and unified view on algorithms under uncertainty. Such algorithms will find applications in: stochastic modelling in biology, manufacturing modern materials that often include uncertainly in the process ; finance and economics ; system security ; resource management ; weather prediction ; food production ; modern plant cultivation systems ; stainability where there is uncertainty in production of energy, water or food. Moreover, this vast area of applications calls for an interdisciplinary approach to development of algorithms under uncertainty that would be only possible under focussed research support given by FET Proactive programme.