Optimization and Decision-Making Under Uncertainty

理论计算机科学中对算法和计算复杂性的研究通常假设要计算的函数和该函数的输入是完全指定和事先已知的。然而，在大多数实际情况下，计算是分阶段进行的，对函数和输入的了解是在这个过程的不同阶段逐渐了解的。此外，这些数据可以从概率分布中提取，这可能是已知的，也可能必须通过抽样来发现。研讨会将致力于探索产生这种不确定性的特定环境，并为在这种环境中评估算法开发合适的范例。研讨会将加强数学、计算机科学、统计学和运筹学研究团体之间的交流。它将向所有潜在的参与者开放，研讨会的结果（包括演讲的视频记录）将分发给公众，以征求意见和参与。组织者将鼓励学生参加研讨会，并将积极招募来自不同背景的科学家，为广泛的应用做出贡献。

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.