AIM Seminar: Applications of Stochastic Optimal Control to Pandemic Management, Optimal Energy Production, and Decentralized Finance
April Nellis (Johns Hopkins University Applied Physics Laboratory)
Abstract: This talk discusses three applied problems in the field of stochastic optimal control which have significant implications in their application fields. We first discuss an optimal control problem in pandemic modeling and mitigation which incorporates novel macroeconomic elements. By modeling the reactions of individuals to current infection levels through personal protective measures which alter disease transmission, improvements in public health outcomes can be achieved with minimal macroeconomic sacrifices. We then present an efficient and accurate machine learning algorithm to solve the high-dimensional optimal switching problem faced by a power plant operator under uncertain production costs and profits. The algorithm is able to perform accurately on high-dimensional models without suffering from extremely long run times arising from the curse of dimensionality. Finally, we combine models of competitive games with empirical data to investigate competition between liquidity providers in a decentralized cryptocurrency exchange. Comparison of simulated and observed price dynamics indicates that the model is accurately capturing pool dynamics and has potential for use in DeFi investment.
Contact: Silas Alben
Contact: Silas Alben
Building: | East Hall |
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Event Type: | Lecture / Discussion |
Tags: | Mathematics |
Source: | Happening @ Michigan from Applied Interdisciplinary Mathematics (AIM) Seminar - Department of Mathematics, Department of Mathematics |