## 2 Background Knowledge
### Decision Science Support Mechanism and/or Tools
Building UR strategies may strive to alleviate the consequences of catastrophes or avoid them from happening. A significant aspect of asset or risk management systems is their decision-making function, by ensuring that activities are taken in a methodical and precise way and lead to intended results. The decision making role for classifying and assessing risks is the most essential aspect of risk management or the most significant control function in risk management, and is frequently emphasized in the discussion on risk management decisions [87]. The concept of asset management and how it can be integrated with risk management to improve decision making for urban resilience has been previously explored. There is an increasing awareness that asset management can be aligned with risk management strategies to improve decision making for UR [88]. The process of decision-making in asset management is a highly intricate undertaking that encompasses not only technical elements such as modeling and data analysis, but also human factors such as bias, uncertainty, and perception. In an era of Big Data, artificial intelligence, IoT, and machine learning, it is essential to recognize and factor in the effects of these human factors in order to make sound and effective decisions [89]. It is necessary to combine'soft' and 'hard' issues in the decision-making process [90]. Ensuring that organizations adopt consistent approaches based on established best practices, rather than relying on disparate individual methods or a lack of auditable methods, poses a significant challenge. This is particularly true for the multitude of smaller decisions that can have a significant impact on asset management. Technical solutions that are highly advanced can often be difficult to comprehend and explain, resulting in the \"black box\" syndrome where the complexity of the model obscures the rationale behind the decision. Risk-Informed Decision-Making (RIDM) is a methodology that provides a formalized, rational, and systematic approach to identifying, assessing, and communicating the various factors that support making a risk-informed decision [91, 92]. Developed in collaboration between IREQ/Hydro-Quebec and the University of Quebec (UQTR), the RIDM process involves considering, appropriately weighting, and integrating a range of often complex inputs and insights into decision making [89, 91]. In order to arrive at an appropriate decision, high-quality engineering analyses are necessary but not sufficient. It is crucial to adopt a comprehensive approach that integrates the outcomes of various quantitative analyses and other relevant, intangible and hardly quantifiable influence factors. Methods of Multi-Attribute Decision-Making (MADM) such as AHP, Fuzzy AHP, PROMETHE, TOPSIS, ELECTRE, and MAUT, can be considered to support the final decision-making. In this process, the decision maker, supported by subject matter experts, analysts, and stakeholders, must engage in a high-level analysis and deliberation, taking into account all relevant insights for a satisfactory decision-making [89]. The decision-making process in asset management is a multifaceted endeavor that necessitates a structured methodology for balancing various competing priorities, managing external and internal factors, and achieving a harmonious equilibrium between short-term needs and long-term benefits. Organizations can accomplish this by implementing a well-designed asset management system in accordance with the ISO 55000 family of standards [17, 93]. However, organizations must also be prepared to address the risks and uncertainties associated with extreme and large-scale disruptive events in their strategic and asset management decisions. As such, it is crucial to integrate the concepts of resilience and asset management to achieve sustainable development, optimal service levels, and economic sustainability [94].