Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out Bayesian inference for these models via Markov chain Monte Carlo (MCMC). Each iteration of the MCMC algorithm is computationally expensive due to costly matrix operations. In addition, the MCMC algorithm needs to be run for more iterations because the strong cross-correlations among the spatial latent variables result in slow mixing Markov chains. To address these computational challenges, we propose a projection-based intrinsic conditional autoregression (PICAR) approach, which is a discretized and dimension-reduced representation of the underlying spatial random field using empirical basis functions on a triangular mesh. Our approach exhibits fast mixing as well as a considerable reduction in computational cost per iteration. PICAR is computationally efficient and scales well to high dimensions. It is also automated and easy to implement for a wide array of user-specified hierarchical spatial models. We show, via simulation studies, that our approach performs well in terms of parameter inference and prediction. We provide several examples to illustrate the applicability of our method, including (i) a high-dimensional cloud cover dataset that showcases its computational efficiency, (ii) a spatially varying coefficient model that demonstrates the ease of implementation of PICAR in the probabilistic programming languages stan and nimble, and (iii) a watershed survey example that illustrates how PICAR applies to models that are not amenable to efficient inference via existing methods.

BACKGROUND: Black heart transplant patients are at higher risk of acute rejection (AR) and death than White patients. We hypothesized that this risk may be associated with higher levels of donor-derived cell-free DNA (dd-cfDNA) and cell-free mitochondrial DNA. METHODS: The Genomic Research Alliance for Transplantation is a multicenter, prospective, longitudinal cohort study. Sequencing was used to quantitate dd-cfDNA and polymerase chain reaction to quantitate cell-free mitochondrial DNA in plasma. AR was defined as ≥2R cellular rejection or ≥1 antibody-mediated rejection. The primary composite outcome was AR, graft dysfunction (left ventricular ejection fraction <50% and decrease by ≥10%), or death. RESULTS: We included 148 patients (65 Black patients and 83 White patients), median age was 56 years and 30% female sex. The incidence of AR was higher in Black patients compared with White patients (43% versus 19%; P =0.002). Antibody-mediated rejection occurred predominantly in Black patients with a prevalence of 20% versus 2% ( P <0.001). After transplant, Black patients had higher levels of dd-cfDNA, 0.09% (interquartile range, 0.001–0.30) compared with White patients, 0.05% (interquartile range, 0.001–0.23; P =0.003). Beyond 6 months, Black patients showed a persistent rise in dd-cfDNA with higher levels compared with White patients. Cell-free mitochondrial DNA was higher in Black patients (185 788 copies/mL; interquartile range, 101 252–422 133) compared with White patients (133 841 copies/mL; interquartile range, 75 346–337 990; P <0.001). The primary composite outcome occurred in 43% and 55% of Black patients at 1 and 2 years, compared with 23% and 27% in White patients, P <0.001. In a multivariable model, Black patient race (hazard ratio, 2.61 [95% CI, 1.35–5.04]; P =0.004) and %dd-cfDNA (hazard ratio, 1.15 [95% CI, 1.03–1.28]; P =0.010) were associated with the primary composite outcome. CONCLUSIONS: Elevated dd-cfDNA and cell-free mitochondrial DNA after heart transplant may mechanistically be implicated in the higher incidence of AR and worse clinical outcomes in Black transplant recipients. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02423070.

Fluvial floods drive severe risk to riverine communities. There is a strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in extreme flood stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.