Role of Recovery in Evolving Protection against Systemic Risk: A Mechanical Perspective in Network-Agent Dynamics

Abstract

We propose a model of evolving protection against systemic risk related to recovery. Using the failure potential in network-agent dynamics, we present a process-based simulation that provides insights into alternative interventions and their mechanical uniqueness. The fundamental operating principle of this model is that computation allows greater emphasis on optimizing the recovery within the general regularity of random network dynamics. The rules and processes that are used here could be regarded as useful techniques in systemic risk measurement relative to numerical failure reduction analyses.

Potential Consequence of Interconnected Intervention against Systemic Risk (COVID‐19) via a Model‐Driven Network‐Agent Dynamic

“…Tus, fnding a global response strategy for the pandemic is key. Breakthroughs in the treatment and prevention of symptoms can also make viruses far less dangerous while requiring fewer extreme containment measures [72]. Ultimately, the breakthroughs, social services, and systems we develop can be used for a better future for everyone.…”

Network Characteristic Control of Social Dilemmas in a Public Good Game: Numerical Simulation of Agent-Based Nonlinear Dynamics

“…In the earlier discussion on PGGs, many references were made to observations of situations in which social dilemmas, such as cooperation and defection, depend on the scale of interconnection between participants [35]. However, most reported data are on the global level (macro scale) over many individuals with different parameter settings (i.e., participation cost, incentive, and mutation) rather than reflecting the changes within individuals (micro scale) over a series of rounds [36]. In addition to the general responses, the evolutionary variability (i.e., imitation and exploration) of individuals is another indicator of their behavioral integrity; from these concepts, it is indeed clear that autonomous behaviors (i.e., bias) and features (i.e., utility) differ in accordance with the relative weights (i.e., network-agent dynamics) of their micro-variable potentials [37].…”

Centrality-Based Heuristics in Risk Management: A Network-agent Dynamics for Analyzing Systemic Failure

“…This dynamic was glaringly illustrated during the 2008 global financial crisis, where the failure of a single institution (Lehman Brothers) triggered a cascading effect, ultimately destabilizing the world economy [10] . More recently, the global spread of COVID-19 showcased the devastating effects of a systemic risk event within the realm of public health [11,12] . The virus's ability to propagate rapidly due to global interconnectedness resulted in unprecedented health and economic challenges, further underscoring the critical importance of understanding and managing systemic risk [13] .…”