In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models -providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation of, and results from, various methods.
In modern science, deterministic computer models are often used to understand complex phenomena, and a thriving statistical community has grown around effectively analysing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models -providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation and results of various methods.
Despite decades of effort to improve information technology (IT) transition, the success rate remains low with each failure representing substantial losses of time, money, and resources. When technologies do transition, they often are ignored and worked around. Worse, they may disrupt work or interfere with work-system responses to challenges. We posit that a systems-centered approach to transition grounded in complex-systems science and resilience engineering can significantly improve technology adoption rates. To serve as the foundation for this approach, we developed a framework that specifies resilience characteristics of high-consequence work systems. The framework, called the Transform with Resilience during Upgrades to Socio-Technical Systems (TRUSTS) Framework, is the basis for IT development tools and guidance that enable resilience-aware development and transition. The tools and guidance will help development teams design and evaluate technologies that contribute to work system performance during escalated and nonroutine demands and conditions, i.e., to work system resilience.
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