Chair of Operations, Economics & Strategy

The mission of OES within the MTEI is to conduct world-class research and teaching at the intersection of operations, economics and strategy, as it relates to organizations and their interactions.

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© 2022 EPFL

Minimizing the Cost of Matched Components in Precision Manufacturing

— Prof. Weber’s research on the “Optimal Matching of Random Parts,” featured in the current issue of the Journal of Mathematical Economics, investigates a firm’s optimal policy of ordering parts with randomly distributed characteristics when these inputs need to be assembled with other components whose characteristics are also uncertain.

© 2022 EPFL

Determining the Endogeneity of Markets for Cryptocurrencies

— Dr. Michael Mark and Prof. Thomas Weber’s research on “Quantifying Endogeneity of Cryptocurrency Markets,” published in the current issue of the European Journal of Finance, examines the volatility of markets for cryptocurrencies, which is well-known to far exceed that of markets for traditional asset classes such as stocks and bonds. To investigate this, the authors determine the branching ratios associated with Bitcoin mid-price dynamics, when these are modeled using self-exciting point processes with different parametric kernels. They further address the issue of regime changes and the concomitant optimal length of an observation horizon for the validity of a model specification. The paper was co-authored with Jan Sila, a doctoral candidate in Finance and Capital Markets at Charles University, Prague.

© 2022 EPFL

New Method of Machine Learning with Interpretability Constraints

— Dr. Michael Mark and Prof. Thomas Weber’s most recent paper, on “Optimal Recovery of Unsecured Debt via Interpretable Machine Learning,” published in Machine Learning with Applications, develops a method to incorporate domain knowledge directly into a reinforcement-learning agent. The new tool is tested on the problem of credit collections, for which an optimal solution is known, so that the augmented learning performance can be compared to an established benchmark. A key observation is that interpretability in the form of monotonicity constraints may be imposed without significant loss in performance. The paper was also co-authored with Prof. Naveed Chehrazi (Olin Business School) and Huanxi Liu who participated in an Excellence Research Internship at EPFL, hosted by the Chair of Operations, Economics and Strategy, while pursuing his bachelor’s degree at UC San Diego.

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