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Using a Feasibility Study of Human Computation for Failure Scenario Identification

[+] Author Affiliations
Ryan Arlitt, Chris Hoyle, Robert Stone

Oregon State University, Corvallis, OR

Nikolaos Papakonstantinou

Aalto University, Espoo, Finland

Bryan O’Halloran

Raytheon Missile Systems, Tucson, AZ

Paper No. DETC2014-35363, pp. V01BT02A004; 11 pages
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1B: 34th Computers and Information in Engineering Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4629-2
  • Copyright © 2014 by ASME


Expert opinion is a common resource for identifying potential failure scenarios, but experts can miss novel failure combinations that have no historical precedent. A variety of computational techniques offer their own strengths for failure scenario identification, but can be limited by historical data availability or prescribed models. This paper examines the question of whether a distributed group of non-expert humans can outperform a brute force algorithm in a failure scenario prediction task. This approach uses human intuition to guide solution space exploration, and provides feedback through a system simulation. The results of the paper show that human non-experts outperformed a Monte Carlo simulation, but converge to relatively few critical failure scenarios. These results indicate that while non-expert reasoning may not be directly applicable to effective exploration of many possible failure modes, human computation has the potential to augment or compete with stochastic algorithms in a complex systems failure analysis context.

Copyright © 2014 by ASME
Topics: Computation , Failure



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