An official website of the United States government
Many contagion processes evolving on populations do so simultaneously, interacting over time. Examples are co-evolution of human social processes and diseases, such as the uptake of mask wearing and disease spreading. Commensurately, multi-contagion agent-based simulations (ABSs) that represent populations as networks in order to capture interactions between pairs of nodes are becoming more popular. In this work, we present a new ABS system that simulates any number of contagions co-evolving on any number of networked populations. Individual (interacting) contagion models and individual networks are specified, and the system computes multi-contagion dynamics over time. This is a significant improvement over simulation frameworks that require union graphs to handle multiple networks, and/or additional code to orchestrate the computations of multiple contagions. We provide a formal model for the simulation system, an overview of the software, and case studies that illustrate applications of interacting contagions.
more »
« less
Multi-layer Default Risk Contagion in Inter-banking Networks
Default risk spreading processes in inter-banking networks are commonly viewed as contagion processes, with inter-bank loans as a direct spreading channel and overlapping investment portfolios as an indirect channel. In this paper, we propose a multi-layer network default risk contagion model to incorporate additional panic contagions in the networks of depositors as a novel augmentation of previous models, allowing for the direct characterization of the “bank run” phenomenon, where many depositors simultaneously issue withdrawal requests. Our model is calibrated with post-COVID pandemic data, accounting for macroeconomic factors such as fluctuating interest rates and asset bubbles. Using system identification methods, we analyze relationships between federal interest rates and market prices, and formulate an optimal control problem to mitigate default risk via liquidity ratio requirements in stress tests. Long-term simulation results are presented to reveal threshold structures under varying contagion parameters.
more »
« less
- Award ID(s):
- 2032321
- PAR ID:
- 10673744
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-1633-9
- Page Range / eLocation ID:
- 5286 to 5291
- Subject(s) / Keyword(s):
- spreading processes inter-bank networks risk contagion
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Many contagion processes evolving on populations do so simultaneously, interacting over time. Examples are co-evolution of human social processes and diseases, such as the uptake of mask wearing and disease spreading. Commensurately, multi-contagion agent-based simulations (ABSs) that represent populations as networks in order to capture interactions between pairs of nodes are becoming more popular. In this work, we present a new ABS system that simulates any number of contagions co-evolving on any number of networked populations. Individual (interacting) contagion models and individual networks are speci ed, and the system computes multi-contagion dynamics over time. This is a signi cant improvement over simulation frameworks that require union graphs to handle multiple networks, and/or additional code to orchestrate the computations of multiple contagions. We provide a formal model for the simulation system, an overview of the software, and case studies that illustrate applications of interacting contagions.more » « less
-
In an increasingly interconnected world, a key scientific challenge is to examine mechanisms that lead to the widespread propagation of contagions and identify risk factors that can trigger large-scale outbreaks. Underlying both the spread of disease and information epidemics is the evolution of the contagion as it propagates, leading to the emergence of different strains, e.g., through genetic mutations in pathogens and alterations in the information content. Recent studies have revealed that models that do not account for heterogeneity in transmission risks associated with different strains of the circulating contagion can lead to inaccurate predictions. However, existing results on multi-strain spreading assume that the network has a vanishingly small clustering coefficient, whereas clustering is widely known to be a fundamental property of real-world social networks. In this work, we investigate spreading processes that entail evolutionary adaptations on random graphs with tunable clustering and arbitrary degree distributions. We derive a mathematical framework to quantify the epidemic characteristics of a contagion that evolves as it spreads, with the structure of the underlying network as given via arbitrary joint degree distributions of single-edges and triangles. We supplement our theoretical findings with numerical simulations and case studies, shedding light on the impact of clustering on contagion spread.more » « less
-
Researchers have modeled contagion processes on social networks for wide ranging applications, including spreading of epidemics, financial defaults, actions such as joining social media sites, and rumors. So, too, researchers have developed a host of intervention methods to control harmful contagions on networks; among these approaches are node and edge removal, separating network communities, altering contagion properties, and introducing a second competing contagion. In this work, minimum dominating sets are used to identify blocking nodes—nodes that do not contract a contagion and therefore also do not assist in transmitting it. A novel, generalized method that utilizes integer linear programming to determine exact minimum dominating sets (which is an NP-hard problem) has been developed for a subgraph of any social network for any combination of covering distance and coverage requirement. Three social networks are used to understand and evaluate (i) the method itself and (ii) the efficacy of the blocking nodes that the method produces to stop contagion spread.more » « less
-
We consider a dynamic model of interconnected banks. New banks can emerge, and existing banks can default, creating a birth-and-death setup. Microscopically, banks evolve as independent geometric Brownian motions. Systemic effects are captured through default contagion: as one bank defaults, reserves of other banks are reduced by a random proportion. After examining the long-term stability of this system, we investigate mean-field limits as the number of banks tends to infinity. Our main results concern the measure-valued scaling limit which is governed by a McKean–Vlasov jump-diffusion. The default impact creates a mean-field drift, while the births and defaults introduce jump terms tied to the current distribution of the process. Individual dynamics in the limit is described by the propagation of chaos phenomenon. In certain cases, we explicitly characterize the limiting average reserves.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/CDC56724.2024.10885838
