Recent political events around the world have led some to advocate replacing democratic institutions with an "epistocracy" (rule by the competent). Offering a historical perspective on this debate, ...this article explores the neglected constitutional device of indirect elections and its use as an epistocratic mechanism. These are elections where representatives are selected by intermediary electors, rather than directly by voters. Drawing on the United States and India as case studies, I argue that such elections were historically defended as epistocratic mechanisms, aimed at securing the selection of representatives with superior virtue or ability. The epistocratic case for indirect election, however, attracted critics in both countries. While such critics presented a compelling case against the reliance on indirect election to select superior legislators, their arguments generated a further dilemma, opening representative democracy itself-direct and indirect-to challenge.
In this article, I focus on arguments which suggest that disenfranchising persons on the grounds of incompetence is likely to produce epistemically sub-optimal decisions. I suggest three ways in ...which such arguments can be strengthened. First, I argue that they can be untethered from the controversial 'best judge' principle, according to which each person is the best judge of his or her own interests. Second, I suggest that epistemic arguments against epistocracy are currently insensitive to the nature of the groups that would be excluded on the grounds of incompetence. Such arguments would remain unchanged were epistocracy to disenfranchise privileged persons rather than already disadvantaged persons. I argue that a stronger critique of epistocracy ought to focus on distinctive epistemic obstacles faced by socially privileged persons. Third, I argue that current epistemic critics of epistocracy ignore how its basis for exclusion entails consequences that are relevant to our assessment of its justifiability. Their criticisms would, for instance, remain the same had this exclusion been brought about in a random manner. Instead, I emphasise the deliberative costs that follow from the exclusion of disadvantaged groups qua incompetent.
The evolving international economic instability and international trade relationship demand a nation to move towards a self-reliant integrated system at a sub-national scale to address the growing ...human needs. Given India’s role in the global trade network, it is critical to explore the underlying extensive complex trade network at the domestic scale. The potential advantages of complex interaction among the different commodities remain unexplored despite the known importance of trade networks in maintaining food security and industrial sustainability. Here we perform a comprehensive analysis of agricultural flows in contrast with non-agricultural commodities across Indian states. The spatio-temporal evolution of the networks from 2010–2018 was studied by evaluating topological network characteristics of consistent spatially disaggregated trade data. Our results show an increase in average annual trade value by 23.3% and 15.4% for agriculture and non-agriculture commodities, respectively, with no significant increase in connectivity observed in both networks. However, they depict contrasting behavior concerning the spatio-temporal changes, with non-agriculture trade becoming more dependent on production hubs and the agriculture trade progressing toward self-reliance, which signifies the evolution of the diversification in the existing agrarian trade network. Our findings could serve as an important element in deepening the knowledge of practical applications like resilience and recovery by devising design appropriate policy interventions for sustainable development.
Concurrency in extreme precipitation-induced events including flooding, landslides and associated debris flow result in massive loss of life, damage to infrastructures, and widespread disruption to ...socioeconomic activities. Despite recent advances in field of risk hazard modeling, we lack a systematic framework to model and assess the impact of extreme precipitation induced concurrent hazards on infrastructure lifelines including road networks. Here we develop an integrated framework to study the effect of concurrent hazards i.e. landslide, debris flow, and flood on regional road networks. Our spatiotemporal 1D simulations of shallow landslides and debris flows in combination with the 2D hydrodynamic model for floods indicate that even highly localized concurrent events have potential to induce widespread and prolonged disruptions to the regional road networks. We illustrate the proposed framework’s application to assess the functionality loss from the individual and concurrent events induced by extreme precipitation. Our results show that not accounting for concurrence in these correlated hazards could result in underestimation of functionality losses by 71\(\%\), which in turn can undermine the pre-disaster preparedness and post-disaster recovery efforts.
The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined ...through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.
Natural hazards, such as hurricanes and winter storms, computer glitches and technical flaws, and man-made terror or cyber-physical attacks, can lead to localized perturbations of the U.S. national ...airspace system airport network (NASAN), which can in turn percolate across the interconnected system. Here we develop and demonstrate an approach to quantitatively characterize the robustness of NASAN, defined as loss of critical functions owing to perturbations, and a quantitative framework to select the most efficient and effective post-hazard recovery strategies. The system-level robustness and recovery strategies rely on network science methods and associated attributes. New insights include the central role of network attributes to robustness and optimal recovery sequences. Characterizations of robustness and fragility can inform what-if plans and proactive design, while recovery strategies developed in advance can support systematic, reliable, and timely bounce-back from hazard-related perturbations. The framework can serve as a baseline over which local information or cost optimization can be superposed.
Abstract
The evacuation of the population from flood-affected regions is a non-structural measure to mitigate flood hazards. Shelters used for this purpose usually accommodate a large number of flood ...evacuees for a temporary period. Floods during a pandemic result in a compound hazard. Evacuations under such situations are difficult to plan as social distancing is nearly impossible in the highly crowded shelters. This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter’s stay. To the best of our knowledge, such a problem is yet to be explored in literature. Here we develop a simulation-optimization framework, where multiple objectives are handled with a max–min approach. The simulation model consists of an extended Susceptible—Exposed—Infectious—Recovered—Susceptible model. We apply the proposed model to the flood-prone Jagatsinghpur district in the state of Odisha, India. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. At the same time, this does not result in an uncontrolled number of new infections. The proposed approach can generalize to different regions and can provide a framework to stakeholders to manage conflicting objectives in disaster management planning and to handle compound hazards.
Abstract
Earth System Models (ESMs) are the state of the art for projecting the effects of climate change. However, longstanding uncertainties in their ability to simulate regional and local ...precipitation extremes and related processes inhibit decision making. Existing state-of-the art approaches for uncertainty quantification use Bayesian methods to weight ESMs based on a balance of historical skills and future consensus. Here we propose an empirical Bayesian model that extends an existing skill and consensus based weighting framework and examine the hypothesis that nontrivial, physics-guided measures of ESM skill can help produce reliable probabilistic characterization of climate extremes. Specifically, the model leverages knowledge of physical relationships between temperature, atmospheric moisture capacity, and extreme precipitation intensity to iteratively weight and combine ESMs and estimate probability distributions of return levels. Out-of-sample validation suggests that the proposed Bayesian method, which incorporates physics-guidance, has the potential to derive reliable precipitation projections, although caveats remain and the gain is not uniform across all cases.
Abstract Concurrent extreme rainfall events, or synchronous extremes, during Indian Summer Monsoon Rainfall (ISMR), cause significant damage, but their spatiotemporal evolution remains unclear. Using ...the event synchronization approach to examine the synchronicity of extreme rainfall events from 1901 to 2019, we find that Central India consistently hosts strongly connected synchronous extreme hubs with localized connections, indicating the geographical trapping of these concurrent events in the region. We observe a moderate positive correlation between network cohesiveness and El Niño Southern Oscillations (ENSO), and a negative correlation between ENSO and link lengths, suggesting localized synchronicity during El Niño dominant decades and opposite patterns in La Niña periods. Despite increasing ISMR variability and spatial nonuniformity, the persistence of hubs and network attributes could offer insights for predicting synchronous extremes, informing effective adaptation and risk management strategies during the monsoon season.
Plain Language Summary Synchronous extreme rainfall events during the India monsoon season have been persistent over the past century, tending to concentrate in Central India and remaining highly localized. These events are correlated with El Niño Southern Oscillations (ENSO), with more synchronization during strong El Niño periods and less during La Niña conditions. The networks representing these synchronous extremes at various levels of severity exhibit key attributes that are time‐invariant with minimal divergence. These properties demonstrate a significant correlation with recurring climate patterns such as ENSO and Tropical Sea‐Surface Temperature Anomalies. Understanding these time‐invariant characteristics and their links to larger climate patterns is essential for infrastructure planning and design, given the persistent challenges posed by these synchronous extremes.
Key Points Despite growing spatial non‐uniformity, synchronous monsoon rainfall extremes confine to nearly the same geographical region Highly connected locations persist in Central India and synchronize locally, indicating a stable network of extreme rainfall events Time‐invariant network properties linked to ocean‐atmosphere processes could serve as key covariates for predicting synchronous extremes
Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, ...including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider the information at a coarse-scale to make predictions at local scales. Complex and non-linear interdependence among local climate variables (e.g., temperature and precipitation) and large-scale predictors (e.g., pressure fields) motivate the use of neural network-based super-resolution architectures. In this work, we present auxiliary variables informed spatio-temporal neural architecture for statistical downscaling. The current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 1/4 degrees (25 km) over one of the most climatically diversified countries, India. We showcase significant improvement gain against two popular state-of-the-art baselines with a better ability to predict statistics of extreme events. To facilitate reproducible research, we make available all the codes, processed datasets, and trained models in the public domain.
PDF
