Leveraging Artificial Intelligence to Address Pervasive Ambiguity in Risk: A Fuzzy Logic Study of Risk Identification and Measurement in Plan Analysis, with Application to the City of Nottingham’s Plan to Attain Net-Zero

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Bailey, Alexander (2022) Leveraging Artificial Intelligence to Address Pervasive Ambiguity in Risk: A Fuzzy Logic Study of Risk Identification and Measurement in Plan Analysis, with Application to the City of Nottingham’s Plan to Attain Net-Zero. [Dissertation (University of Nottingham only)]

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Abstract

Climate Change is an unavoidable truth. With the threat of annihilation posing an existential threat to humanity, achieving net-zero has become the only viable option for limiting and averting humankind's potential extinction. With such high stakes, failure to reach net-zero might be catastrophic, making it vital that plans for transitioning to a net-zero society be robust; this means identifying, quantifying, and mitigating all risk types. While risk identification and measurement are valuable tools for evaluating plans, the study of risk management, particularly risk communication, is hampered by imprecise linguistic definitions. Human ignorance completely conceals such a lack of uniformity in word meanings. This leads to erroneous inferences, which at best cause minor bewilderment and at worst lead to damaging behaviours and judgements.

This dissertation aims to address both the scrutinization of the net-zero transition plans and tackling the pervasive vagueness in language, through leveraging the advantageous traits of Fuzzy Logic. Fuzzy Logic is an Artificial Intelligence logic that allows for the capture of non-binary partial truths, while simultaneously intertwining linguistic definitions in its’ membership functions and human interpretable rules. Fuzzy Logic will be leveraged with Boolean Logic to create a framework to carry out a Risk Identification and Measurement in the context of a net-zero transition strategy. What's more, the dissertation will not only create a framework in the theoretical sense but produce a tangible Fuzzy Logic Inference System to measure risks. This approach is not limited to only net-zero plans; rather, it is generically applicable to any plan.

Item Type: Dissertation (University of Nottingham only)
Depositing User: Bailey, Alexander
Date Deposited: 25 Apr 2023 14:57
Last Modified: 25 Apr 2023 14:57
URI: https://eprints.nottingham.ac.uk/id/eprint/67791

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