Robust network design takes the very successful framework of robust optimization and applies it to the area of network design, motivated by applications in communication networks. The main premise is that demands across the network are not fixed, but are variable or uncertain. However, they are known to fall within a prescribed uncertainty set. Our solution must have sufficient capacity to rout...

Robustness of conversational systems is a multifaceted issue that involves factors such as the quality and robustness of the ASR module, but also the capabilities of the dialog manager and the interaction design. The techniques for output rendering also play a major role. Furthermore, “design“ per se has at least two different meanings, viz. the resulting system, but also the process that was u...

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We consider the optimal contract between an entrepreneur and investors in a single-period model when both parties have limited liability, are risk-neutral toward cash flow risk, and are ambiguity-averse. Ambiguity aversion is modeled by multiplier preferences for robustness toward model uncertainty, as in Hansen and Sargent (2001). Efficient ambiguity-sharing implies that the first-best contrac...

A crucial assumption in many network design problems is that of knowing the traffic demands in advance. Unfortunately, measuring and predicting traffic demands are difficult problems. Moreover, often communication patterns change over time, and therefore we are not given a single static traffic demand, but instead a set of non-simultaneous traffic demands. The network should be able to support ...

Computer Science is in deep trouble. Structured design is a failure. Systems, as currently engineered, are brittle and fragile. They cannot be easily adapted to new situations. Small changes in requirements entail large changes in the structure and configuration. Small errors in the programs that prescribe the behavior of the system can lead to large errors in the desired behavior. Indeed, curr...

Within the robust design optimization, the statistical variability of the design parameter is considered. The most general method for solving optimization problems under uncertainties is the well established Monte Carlo simulation method. However, the major shortcoming of this approach is its vast need of computational resources (the number of solver runs required), and these cannot be presumed...

The paper is devoted to the robust control algorithms design for the nonlinear subsystems with uncertainties. The described approach is based on the state variables aggregation and uses of the Lyapunov function. It allows to reduce the dimension of the optimal tracking control problem and its relatively easy solution.

In situations in which the social planner has to make several decisions over time, before agents have observed all the relevant information, static mechanisms may not su¢ ce to induce agents to reveal their information truthfully. This paper focuses on questions of partial and full implementation in dynamic mechanisms, when agents’ beliefs are unknown to the designer (hence the term “robust”). ...

In a robust parameter design (RPD) problem, the experimenter is interested to determine the values of con-trol factors such that responses will be robust or insensitive to variability of the noise factors. Response sur-face methodology (RSM) is one of the effective methods that can be employed for this purpose. Since quality of products or processes is usually evaluated through several quality ...