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Prof. Anand Deo wins ISIM Outstanding Simulation Award 2024 for black-box simulation innovation

The study is co-authored with Prof. Karthyek Murthy, SUTD

26 December, 2024, Bengaluru: Prof. Anand Deo, from the Decision Sciences area at IIM Bangalore, was recently honored with ISIM Outstanding Simulation Publication Award 2024. The prestigious recognition, awarded by the INFORMS Simulation Society, acknowledges exceptional contributions to simulation literature in the form of articles, books, book chapters and monographs, copyrighted between 2021 and 2023. Prof. Deo shares this accolade with co-author Prof. Karthyek Murthy, Singapore University of Technology and Design (SUTD).

The study, titled ‘Achieving Efficiency in Black-Box Simulation of Distribution Tails with Self-Structuring Importance Samplers’, has been celebrated for its innovative contributions and broad applicability across diverse domains, including operations, financial engineering, analytics, and management sciences.

Synopsis of the paper: In their award-winning paper, the authors present a novel importance sampling (IS) scheme for estimating distribution tails of performance measures modeled with a rich set of tools, such as linear programs, integer linear programs, piecewise linear/quadratic objectives, feature maps specified with deep neural networks, etc. The conventional approach of explicitly identifying efficient changes of measure suffers from feasibility and scalability concerns beyond highly stylized models because of their need to be tailored intricately to the objective and the underlying probability distribution. This bottleneck is overcome in the proposed scheme with an elementary transformation that is capable of implicitly inducing an effective IS distribution in a variety of models by replicating the concentration properties observed in less rare samples.  The proposed sampler is the first to attain asymptotically optimal variance reduction across a spectrum of multivariate distributions despite being oblivious to the specifics of the underlying model. Its applicability is illustrated with contextual shortest-path and portfolio credit risk models informed by neural networks.