Sensitivity analysis is becoming an essential tool for systems modeling and policy support, but its full potential is yet to be realized. The paper identifies key challenges and opportunities, including structuring sensitivity analysis as a discipline, addressing computational burdens, integrating with machine learning, and clarifying its relationship with uncertainty quantification and decision-making.

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This paper provides a valuable overview of the current state and future directions of sensitivity analysis, highlighting both theoretical and practical challenges. It offers insightful discussions on various aspects of sensitivity analysis, including its role in mathematical modeling, computational aspects, and connections with machine learning and uncertainty quantification. While the paper lacks extensive empirical validation and detailed practical applications, it presents a well-structured framework for advancing the field of sensitivity analysis and promoting its use in diverse scientific disciplines. The multidisciplinary expertise of the authors adds to the comprehensiveness of the perspective presented.