References

Research Papers and Books

Papers and conferences

  • Nyberg, E. P., Nicholson, A. E., Korb, K. B., Wybrow, M., Zukerman, I., Mascaro, S., Thakur, S., Oshni Alvandi, A., Riley, J., Pearson, R., Morris, S., Herrmann, M., Azad, A. K. M., Bolger, F., Hahn, U., & Lagnado, D. (2022). BARD: a structured technique for group elicitation of Bayesian networks to support analytic reasoning. Risk Analysis, 42(6), 1155-1178. https://doi.org/10.1111/risa.13759

Books

  • “Making Good Decisions” by Reidar B Bratvold and Steve H Begg: Society of Petroleum Engineers, vol. 207, 2010. ISBN 9781555632588, https://doi.org/10.2118/9781555632588.

  • “Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis” by Uffe B. Kjærulff, Anders L. Madsen: Information Science and Statistics, Springer New York, NY, 2nd edition, 2013. https://doi.org/10.1007/978-1-4614-5104-4

Web sites

  • https://en.wikipedia.org/wiki/Decision_model

  • https://en.wikipedia.org/wiki/Decision-making_models

External Libraries and Tools

  • FastAPI: A modern, fast (high-performance) web framework for building APIs with Python 3.6+.

  • Pydantic: Data validation and settings management using Python type annotations.

  • Apache TinkerPop™: Apache TinkerPop™ is a graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP). Gremlin is the graph traversal language of Apache TinkerPop.

  • React: A JavaScript library for building user interfaces.