Systems Realization Laboratory Workshop: Uncertainty Representation in Robust and Reliability-Based Design Jason Aughenbaugh (Univ Texas, Austin) Zissimos Mourelatos (Oakland University) Chris Paredis (Georgia Tech) 2006 International Design Engineering Technical Conferences Philadelphia, PA, September 10, 2006

Systems Realization Laboratory Workshop Overview  Objective: Promote understanding and discussion of uncertainty representations Introduction to various uncertainty representations Comparison and evaluation of uncertainty representations for design  Focus on three different uncertainty representations Probability theory  Prof. Wei Chen, Northwestern University Possibility theory and evidence theory  Dr. Scott Ferson, Applied Biomathematics Imprecise probability theory  Prof. Zissimos Mourelatos, Oakland University 

Systems Realization Laboratory Schedule  1:15 – 1:25:Welcome and introductions Chris Paredis  1:25 – 1:45:Criteria for evaluating uncertainty representations Jason Aughenbaugh  1:45 – 2:15:Probability Theory Wei Chen  2:15 – 2:45:Imprecise Probability Theory Scott Ferson  2:45 – 3:00:Coffee Break  3:00 – 3:30:Possibility and Evidence Theory Zissimos Mourelatos  3:30 – 4:30:Discussion Chris Paredis

Systems Realization Laboratory Topics for Discussion  Representation  Inference / Computation  Decision Making  Other issues: validation, sensitivity analysis, … If time permits  Final Conclusions

Systems Realization Laboratory Representation  Expressivity Is there a need to go beyond probabilities? Is there a fundamental difference between reducible and irreducible uncertainty? What is the relationship between these different representations? Are they mutually exclusive?  Elicitation Do all the representations covered today have an unambiguous definition and elicitation process? How does one aggregate expert knowledge?

Systems Realization Laboratory Inference & Computation  Fundamental Issues How should one evaluate whether a certain inference mechanism that leads to valid conclusions? Do the inference mechanisms presented today lead to valid conclusions?  Computational Issues To what extent do computational issues play a role in selecting a formalism What are the limitations and assumptions that exist in the current inference algorithms? What is the maturity level? Can these formalisms be applied to real- world problems?

Systems Realization Laboratory Decision Making  Fundamental Issues How should one compare different decision making methodologies? Which representation should be used when? Is there one representation that should always be used?  Decision Making in Engineering Design Do decisions in engineering design have characteristics that make some uncertainty representations better suited than others? How should engineering design decisions be framed within each of these methodologies? Are there differences?

Systems Realization Laboratory Final Conclusions  What have we learned today?