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What happens when you slow down part of an ultrafast network that is operating quicker than the blink of an eye, e.g. electronic exchange network, navigational systems in driverless vehicles, or even neuronal processes in the brain? This question just adopted immediate commercial, legal and political importance following U.S. financial regulators decision to allow a new network node to intentionally introduce delays of microseconds. Though similar requests are set to follow, there is still no scientific understanding available to policymakers of the likely system-wide impact of such delays. Giving academic researchers access to (so far prohibitively expensive) microsecond exchange data would help rectify this situation. As a by-product, the lessons learned would deepen understanding of instabilities across myriad other networks, e.g. impact of millisecond delays on brain function and safety of driverless vehicle navigation systems beyond human response times.
We review some aspects, especially those we can tackle analytically, of a minimal model of closed economy analogous to the kinetic theory model of ideal gases where the agents exchange wealth amongst themselves such that the total wealth is conserved
Analytical non-perturbative study of the three-dimensional nonlinear stochastic partial differential equation with additive thermal noise, analogous to that proposed by V.N. Nikolaevskii [1]-[5]to describe longitudinal seismic waves, is presented. Th
The classical Chapman-Enskog procedure admits a substantial geometrical generalization known as slow manifold reduction. This generalization provides a paradigm for deriving and understanding most reduced models in plasma physics that are based on co
In the framework of the evolutionary dynamics of the Prisoners Dilemma game on complex networks, we investigate the possibility that the average level of cooperation shows hysteresis under quasi-static variations of a model parameter (the temptation
The preferential attachment (PA) process is a popular theory for explaining network power-law degree distributions. In PA, the probability that a new vertex adds an edge to an existing vertex depends on the connectivity of the target vertex. In real-