Optimization and Decision-Making Under Uncertainty

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Investigations of algorithms and computational complexity in theoretical computer science typically assume that the function to be computed and the inputs to that function are completely specified and known in advance. In most realistic cases, however, the computation proceeds in stages, and knowledge of the function and the inputs becomes known progressively at different stages of this process. In addition, these data may be drawn from a probability distribution, which may be known or may have to be discovered by sampling. The workshop will be devoted to exploring particular settings in which such uncertainties arise, and developing suitable paradigms for evaluating algorithms in such settings.The workshop will enhance communication among the mathematics, computer science, statistics and operations research communities. It will be open to all potential participants, and the workshop findings (including video recordings of presentations) will be distributed to the public for comment and engagement. The organizers will encourage students to attend the workshop, and will actively recruit scientists from a diversity of backgrounds to contribute to a wide range of applications.