Lectures and appropriate assignments on fundamental or applied topics of current interest in industrial engineering and operations research.

Computational Approach for Games and Incentive Design One of the fundamental challenges with engineering societal systems is managing information asymmetries. The classical approach has been to construct probabilistic models that capture different possibilities for the values of unobserved information, and to then design solutions under the assumed probabilities. However, the past two decades have seen the emergence of mobile computing and sensing technologies. These technologies suggest the opportunity for a new paradigm in engineering societal systems where mobile sensing (IoT technologies) and computation and advances in AI/ML can reduce monitoring costs and thereby reduce information asymmetries. This course will introduce key ideas in the topic of computational games and incentive design, including mathematical and computational tools in game theory, incentive design, estimation, and learning. Practical examples will be drawn from a number of socio-technical systems such as those found in healthcare, transportation, and energy, which are being digitally transformed.

Prerequisites: Course on optimization; course on statistics or stochastic processes

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