Advancing the Frontiers of Simulation
A Festschrift in Honor of George Samuel Fishman
Herausgegeben: Alexopoulos, Christos; Goldsman, David; Wilson, James R.
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This Festschrift honors George Samuel Fishman, one of the founders of the field of computer simulation and a leader in the disciplines of operations research and management science for the past five decades. The papers in this volume span the theory, methodology, and application of computer simulation. Renowned researcher, teacher, mentor, and author, Fishman has made fundamental contributions to both the theory and practice of simulation, the dissemination of knowledge, service to the profession, and the advancement of the status of the field. Among his many honors and awards are the Distinguished Service Award from TIMS/College on Simulation; the Frederick W. Lanchester Prize from INFORMS; and the INFORMS Simulation Society's Lifetime Professional Achievement Award.
This Festschrift honors George Samuel Fishman, one of the founders of the eld of computer simulation and a leader of the disciplines of operations research and the management sciences for the past ve decades, on the occasion of his seventieth birthday. The papers in this volume span the theory, methodology, and application of computer simulation. The lead article is appropriately titled "George Fishman's Professional Career." In this article we discuss George's contributions to operations research and the m- agement sciences, with special emphasis on his role in the advancement of the eld of simulation since the 1960s. We also include a brief personal biography together with comments by several individuals about the extraordinary effect that George has had on all his students, colleagues, and friends. Thesecondarticle,titled"AConversationwithGeorgeFishman,"isthetranscript of an extended interview with George that we conducted in October 2007. In the article titled "Computer Intensive Statistical Model Building," Russell Cheng studies resampling methods for building parsimonious multiple linear regr- sion models so as to represent accurately the behavior of the dependent variable in terms of the smallest possible subset of explanatory (independent) variables. The author shows how bootstrap resampling can be used not only for rapid identi cation of good models but also for ef cient comparison of competing models.
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