Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science

Ross Whitaker, William Thompson, James Berger, Baruch Fischhof, Michael Goodchild, Mary Hegarty, Christopher Jermaine, Kathryn S. McKinley, Alex Pang, Joanne Wendelberger

Modern science, technology, and politics are all permeated by data that comes from people, measurements, or computational processes. While this data is often incomplete, corrupt, or lacking in sufficient accuracy and precision, explicit consideration of uncertainty is rarely part of the computational and decision making pipeline. The CCC Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science explored this problem, identifying significant shortcomings in the ways we currently process, present, and interpret uncertain data. Specific recommendations on a research agenda for the future were made in four areas: uncertainty quantification in large-scale computational simulations, uncertainty quantification in data science, software support for uncertainty computation, and better integration of uncertainty quantification and communication to stakeholders.

Knowledge Graph

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