Intrinsic Uncertainties in Modeling Complex Systems.

Technical Report · Mon Sep 01 00:00:00 EDT 2014

DOI:https://doi.org/10.2172/1156599· OSTI ID:1156599

Cooper, Curtis S. ^[1]; Bramson, Aaron L. ^[1]; Ames, Arlo L. ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Models are built to understand and predict the behaviors of both natural and artificial systems. Because it is always necessary to abstract away aspects of any non-trivial system being modeled, we know models can potentially leave out important, even critical elements. This reality of the modeling enterprise forces us to consider the prospective impacts of those effects completely left out of a model – either intentionally or unconsidered. Insensitivity to new structure is an indication of diminishing returns. In this work, we represent a hypothetical unknown effect on a validated model as a finite perturbation whose amplitude is constrained within a control region. We find robustly that without further constraints, no meaningful bounds can be placed on the amplitude of a perturbation outside of the control region. Thus, forecasting into unsampled regions is a very risky proposition. We also present inherent difficulties with proper time discretization of models and representing inherently discrete quantities. We point out potentially worrisome uncertainties, arising from mathematical formulation alone, which modelers can inadvertently introduce into models of complex systems.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1156599
• Report Number(s): SAND--2014-17382; 537269
• Country of Publication: United States
• Language: English