Equitable resilience modeling

uncovering and mitigating bias

Beyond efficient and accurate resilience, I am interested in modeling infrastructure resilience in a manner that results in unbiased estimates. With this, I aim to translate decision-making into a more equitable practice. As it is a matter of utmost importance, I am currently investigating the implications of model rigidity, uncertainties that underlie the modeling process, and multi-scale error propagation to understand how these may affect the communities in which decisions may impact differently the communities we model (see Figure 1).

Figure 1. Modeling errors, data scarcity, or incorrect model definitions contribute to biased inferences, thus inequitable decision-making.

I investigate the different types of uncertainties involved in the modeling processes, remarking on the role of models in engineering practice. My work on coupling effects of uncertainties that underlie the modeling process (e.g., modeling fidelities presented Figure 2) with the final outcomes has demonstrated quantitively the impact of modeling choices on the simulated emergent properties of multi-scale systems. The methods I work on include time-dependent system reliability, uncertainty propagation in multi-scale models, system dynamics approaches, recursive Bayesian techniques, surrogate modeling, active learning, and network science algorithms.

Figure 2. The level of fidelity used for modeling single-scale processes may influence the final estimates (i.e., quantities of interest).

  • Padgett JE, Rincon R, Panakkal P. (2024). “Future Cities Demand Smart and Equitable Infrastructure Resilience Modeling Perspectives.” Npj Natural Hazards, In Review, April 2024

  • Rincon R, Padgett JE. (2024) “Fragility Modeling Practices and their Implications on Risk and Resilience Analysis: from the Structure to the Network Scale” Earthquake Spectra, 40(1), 647–673. DOI:10.1177/87552930231219220.

  • Rincon R, Padgett JE. (2024) “Coupling Effects of Fragility Fidelity and Network Resolution in Infrastructure Resilience.” Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference (EMI/PMC2024), Chicago, IL, May 28-31, 2024.

  • Rincon R, Padgett JE. (2023). “Exploration of biasedness and inequities in infrastructure resilience modeling” ASCE INSPIRE Conference 2023, Arlington, VA, November 16-18. https://doi.org/10.26226/m.65562ab611e6250019bbac80.