IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution

Authors

  • Mark Feblowitz IBM
  • Oktie Hassanzadeh IBM
  • Michael Katz IBM
  • Shirin Sohrabi IBM
  • Kavitha Srinivas IBM
  • Octavian Udrea IBM

Keywords:

Scenario Planning, Neuro-Symbolic Systems, AI Planning, Causal Extraction From Text

Abstract

Scenario Planning is a commonly used Enterprise Risk Management (ERM) technique to help decision makers with longterm plans by considering multiple alternative futures. It is typically a manual, highly labor intensive process involving dozens of experts and hundreds to thousands of person-hours. We previously introduced a Scenario Planning Advisor prototype (Sohrabi et al. 2018a,b) that focuses on generating scenarios quickly based on expert-developed models. We present the evolution of that prototype into a full-scale, cloud deployed ERM solution that: (i) can automatically (through NLP) create models from authoritative documents such as books, reports and articles, such that what typically took hundreds to thousands of person-hours can now be achieved in minutes to hours; (ii) can gather news and other feeds relevant to forces in the risk models and group them into storylines without any other user input; (iii) can generate scenarios at scale, starting with dozens of forces of interest from models with thousands of forces in seconds; (iv) provides interactive visualizations of scenario and force model graphs, including a full model editor in the browser. The SPA solution is deployed under a non-commercial use license at https://spa-service.draco.res.ibm.com and includes a user guide to help new users get started. A video demonstration is available at https://www.youtube.com/watch?v=IaX3d37NUl8.

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Published

2021-05-18

How to Cite

Feblowitz, M., Hassanzadeh, O., Katz, M., Sohrabi, S., Srinivas, K., & Udrea, O. (2021). IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16032-16034. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18003