Predictive Science - Bryant McGill

# Predictive Science **Entity class:** Concept or analytic term **Domain:** Austin Research / Concepts **Doc Type:** Corpus analysis **Source basis:** Supplied research cluster with linked references; see the [[wiki/Austin Research Evidence Map|evidence map]] for verification scope. **Predictive Science** develops computational representations capable of making useful predictions with evidence about their reliability. In the Austin cluster it means more than generating a numerical output: observations, physical models, algorithms, uncertainty, and intended decisions must be connected. [[wiki/PECOS|PECOS]] supplies the institutional case. Experimental measurements constrain a model; verification and validation assess its calculation and physical adequacy; uncertainty qualifies the prediction. Revised experiments can then target what is still poorly known. This discipline underlies the master document’s idea of executable epistemology. Prediction becomes operationally consequential when it can arrive in time, expose limitations, and guide a decision whose result becomes the next observation. ## Connected entries [[wiki/PECOS|PECOS]] · [[wiki/Oden Institute|Oden Institute]] · [[wiki/Verification Validation and Uncertainty Quantification|Verification Validation and Uncertainty Quantification]] · [[wiki/Reduced-Order Modeling|Reduced-Order Modeling]] · [[wiki/State Estimation|State Estimation]] · [[wiki/Data Assimilation|Data Assimilation]] · [[wiki/Predictive Science Academic Alliance Program|Predictive Science Academic Alliance Program]] ## Sources and corpus context - [[research/The Austin Executable Loop|The Austin Executable Loop — master document]] - [Predictive Science Research Gets Major Boost Thanks to the Department of Energy](https://news.utexas.edu/2020/10/05/predictive-science-research-gets-major-boost-thanks-to-the-department-of-energy/) - [PECOS — Center for Predictive Engineering and Computational Sciences](https://pecos.oden.utexas.edu/about.html) - [Predictive Engineering and Computational Sciences](https://oden.utexas.edu/research/centers-and-groups/predictive-engineering-and-computational-sciences/) **Cluster:** [[wiki/Austin Executable Loop|Austin Executable Loop]] · [[wiki/Austin Research Evidence Map|Austin Research Evidence Map]] ## Relationships  This entry's documented connections are expressed in its definition and related-work routes, with provenance retained in the source-linked material.  ## Simple Reminders, Quotations, and Thoughts From [[wiki/The Coming Technological Singularity|The Coming Technological Singularity]]: The passage describes a limit of present models as a projected intelligence transition approaches. > "It is a point where our models must be discarded and a new reality rules. As we move closer and closer to this point, it will loom vaster and vaster over human affairs till the notion becomes a commonplace. Yet when it finally happens it may still be a great surprise and a greater unknown." > **— Vernor Vinge**, *1993, “The Coming Technological Singularity,” VISION-21 Symposium* [[reminders/Machine Succession/The Future Could Surprise Us Even When We Expect It by Vernor Vinge|The Future Could Surprise Us Even When We Expect It by Vernor Vinge]] > “I now believe that in the not too distant future, the best forecasters will not be people, but machines: ever more capable "prediction engines" probing ever deeper into stochastic spaces.” > **— Paul Saffo**, *“The Best Forecasters Will Be Computers,” Edge Annual Question, 2008* [[reminders/Machine Succession/Machines May Become Better Forecasters Than People by Paul Saffo|Machines May Become Better Forecasters Than People by Paul Saffo]] > “How will predictive models in the social sciences achieve the accuracy and precision of those in the natural sciences?” > **— Robert Kurzban**, *2018 Edge Annual Question, question* [[reminders/Information/Can Social Science Predict Like Natural Science by Robert Kurzban|Can Social Science Predict Like Natural Science by Robert Kurzban]]