Computational Decision Making under Uncertainty (EMI/PMC 2020)

Message de Kostas Papakonstantinou <>
Dear Colleague,

We are sending this email to invite you to a mini-symposium on

“Computational Decision Making under Uncertainty”

at the 2020 Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference (EMI/PMC 2020) at Columbia University in New York, New York, on May 26-29, 2020 (

The scope of this mini-symposium is described in the abstract below. An abstract is required by January 15th through the online submission system at the conference website ( Please submit your abstract to MS 307 for this mini-symposium.

Best regards and looking forward to seeing you in New York.

The organizers:

Kostas Papakonstantinou, Penn State University

Daniel Straub, Technische Universität München
Matteo Pozzi, Carnegie Mellon University
Charalampos Andriotis, Penn State University

At the core of every engineering problem lies a decision-making quest, either directly or indirectly. Sophisticated UQ methods are essentially providing decision support through efficient quantification of selected metrics and quantities of interest, and sensitivity analysis. Nonetheless, despite significant progress in UQ methods and techniques, dedicated rigorous computational methodologies for engineering decisions under uncertainty have only recently begun to emerge. In this context, the focus of this mini-symposium is on the integration of stochastic models, data analytics, and artificial intelligence methodologies towards optimum computational decision-making under uncertainty and formal decision analysis frameworks, with emphasis on challenging sequential decision-making problems. Selection of metrics for UQ analysis based on decision-theoretic considerations are also of interest, such as the choice of appropriate objective functions and decision-theoretic sensitivity measures. Computational decision-making able to directly offer optimal actions to decision-makers is of increasing relevance in many fields of application and is also related to the science of autonomy. For instance, engineering and natural systems are exposed to a number of adverse operating conditions. Various deteriorating factors, emerging stressors of ever-changing environments, and numerous natural or manmade hazards necessitate decision-making approaches able to provide intelligent and effective management solutions, both for emergency response and preventive/corrective maintenance and inspection planning, for challenging settings featuring vast spaces and complex spatiotemporal dynamics. Other areas of interest, both in terms of methodologies and applications, include, but are not limited to:

  • Data analytics and data-driven modeling
  • Machine learning / artificial intelligence methods
  • Deep learning architectures
  • Uncertainty Quantification
  • Markov Decision Processes under perfect and partial information
  • Value of Information and structural health monitoring
  • Stochastic control
  • Multi-agent systems
  • Multi-objective optimization
  • Dynamic programming
  • Real-time inference
  • Autonomous systems
  • Structural identification methods
  • Bayesian networks
  • Bayesian modeling and inference
  • Renewal processes
  • Life-cycle infrastructure management and optimization
  • Inspection and maintenance planning
  • Resilient recovery and emergency response
  • Risk and reliability assessment
  • Probabilistic performance-based engineering


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