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Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: 1 to 4 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.

Prerequisites: CS 101, IE 300, and IE 411 or equivalent courses. This course focuses on decision-making in the presence of multiple agents with preferences. This would cover topics on optimized democracy (voting rules, axioms, liquid democracy), embedded ethics (biases in algorithms, societal effects of algorithms, algorithmic fairness), similar paradigms in ML (federated learning), and related topics from economics (fair division, general equilibrium theory, and stable matching). The course is highly-interdisciplinary, and the main takeaway is to familiarize students with a class of problems that are of interest to operations research, computer science, economics, and law, and how the combined literature can help us design efficient solutions.