The effectiveness of Bayesian updating in dynamic and complex systems |
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| Authors: | Antonis Targoutzidis |
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| Institution: | 1. School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, PR China;2. School of Metallurgical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, PR China;1. Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA;2. Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94709, USA;3. Computational Research Division Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;4. Watson Group, IBM Almaden Research Centre, San Jose, CA 95210, USA;5. Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA;6. Department of Chemistry, Faculty of Science, King Abdulaziz University, Jeddah 22254, Saudi Arabia;7. Institut des Sciences et Ingénierie Chimiques, Valais, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rue de l’Industrie 17, CH-1951 Sion, Switzerland;1. Department of Chemical and Environmental Engineering/Centre of Excellence for Green Technologies, The University of Nottingham Malaysia Campus, Broga Road, 43500 Selangor D.E., Malaysia;2. Department of Chemical Engineering/Centre of Hydrogen Energy, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM, 81310 Johor Bahru, Johor, Malaysia;1. College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China;2. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, Nanjing Tech University, Nanjing 210009, China;3. Department of Fire Protection and Safety, Oklahoma State University, OK 74078, USA |
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| Abstract: | This paper investigates the effectiveness of Bayesian updating (i.e. the process of improving initial probability estimates by incorporating data from real operation) in complex and dynamic systems. A mathematical model including various types of dynamic input (i.e. variable time-dependent failure probability) was developed in order to test whether decision making based on Bayesian updating would provide better performance, by means of lower failure probabilities and/or lower cost.This investigation showed that using Bayesian updating (with the assumptions of uniform probability distribution and independent events) does not lead to better results, on the contrary in many cases in can lead to a much inferior performance, which is a result of certain deficiencies of this process in dynamic systems. |
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