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Classification and moral evaluation of uncertainties in engineering modeling.
Colleen Murphy1, Paolo Gardoni, Charles E Harris
1Department of Philosophy, Texas A&M University, College Station, TX 77843-4237, USA. cmmurphy@philosophy.tamu.edu
Science and Engineering Ethics
|November 3, 2010
Summary
Engineering models are essential but contain inherent uncertainties. This paper classifies uncertainty sources in engineering modeling and proposes nine guidelines for managing them, differentiating from scientific hypothesis evaluation.
Area of Science:
- Engineering
- Modeling and Simulation
- Risk Management
Background:
- Engineers routinely confront risks and uncertainties in their professional practice.
- Engineering models, crucial for understanding system performance, inherently contain uncertainties.
- Models abstract and idealize mathematical properties of real-world targets.
Purpose of the Study:
- To define stages of the engineering modeling process.
- To classify sources and categories of uncertainty in engineering models.
- To differentiate engineering modeling uncertainty treatment from scientific hypothesis evaluation.
Main Methods:
- Definition and classification of engineering modeling stages.
- Identification and categorization of uncertainty sources within each stage.
- Comparative analysis of uncertainty treatment in engineering versus scientific contexts.
Main Results:
- Detailed breakdown of the engineering modeling lifecycle.
- Comprehensive classification of various uncertainty types and their origins.
- Highlighting distinct criteria for engineering model development versus scientific hypothesis testing.
Conclusions:
- Engineering models are indispensable tools for performance prediction.
- Understanding and categorizing model uncertainties is critical for reliable engineering.
- Nine guidelines are proposed for effective uncertainty management in engineering modeling.
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