65 research outputs found
Reducing complexity of multiagent systems with symmetry breaking: an application to opinion dynamics with polls
In this paper we investigate the possibility of reducing the complexity of a
system composed of a large number of interacting agents, whose dynamics feature
a symmetry breaking. We consider first order stochastic differential equations
describing the behavior of the system at the particle (i.e., Lagrangian) level
and we get its continuous (i.e., Eulerian) counterpart via a kinetic
description. However, the resulting continuous model alone fails to describe
adequately the evolution of the system, due to the loss of granularity which
prevents it from reproducing the symmetry breaking of the particle system. By
suitably coupling the two models we are able to reduce considerably the
necessary number of particles while still keeping the symmetry breaking and
some of its large-scale statistical properties. We describe such a multiscale
technique in the context of opinion dynamics, where the symmetry breaking is
induced by the results of some opinion polls reported by the media
Initialization of the Shooting Method via the Hamilton-Jacobi-Bellman Approach
The aim of this paper is to investigate from the numerical point of view the
possibility of coupling the Hamilton-Jacobi-Bellman (HJB) equation and
Pontryagin's Minimum Principle (PMP) to solve some control problems. A rough
approximation of the value function computed by the HJB method is used to
obtain an initial guess for the PMP method. The advantage of our approach over
other initialization techniques (such as continuation or direct methods) is to
provide an initial guess close to the global minimum. Numerical tests involving
multiple minima, discontinuous control, singular arcs and state constraints are
considered. The CPU time for the proposed method is less than four minutes up
to dimension four, without code parallelization
A destination-preserving model for simulating Wardrop equilibria in traffic flow on networks
In this paper we propose a LWR-like model for traffic flow on networks which
allows one to track several groups of drivers, each of them being characterized
only by their destination in the network. The path actually followed to reach
the destination is not assigned a priori, and can be chosen by the drivers
during the journey, taking decisions at junctions.
The model is then used to describe three possible behaviors of drivers,
associated to three different ways to solve the route choice problem: 1.
Drivers ignore the presence of the other vehicles; 2. Drivers react to the
current distribution of traffic, but they do not forecast what will happen at
later times; 3. Drivers take into account the current and future distribution
of vehicles. Notice that, in the latter case, we enter the field of
differential games, and, if a solution exists, it likely represents a global
equilibrium among drivers.
Numerical simulations highlight the differences between the three behaviors
and suggest the existence of multiple Wardrop equilibria
How can macroscopic models reveal self-organization in traffic flow?
In this paper we propose a new modeling technique for vehicular traffic flow,
designed for capturing at a macroscopic level some effects, due to the
microscopic granularity of the flow of cars, which would be lost with a purely
continuous approach. The starting point is a multiscale method for pedestrian
modeling, recently introduced in Cristiani et al., Multiscale Model. Simul.,
2011, in which measure-theoretic tools are used to manage the microscopic and
the macroscopic scales under a unique framework. In the resulting coupled model
the two scales coexist and share information, in the sense that the same system
is simultaneously described from both a discrete (microscopic) and a continuous
(macroscopic) perspective. This way it is possible to perform numerical
simulations in which the single trajectories and the average density of the
moving agents affect each other. Such a method is here revisited in order to
deal with multi-population traffic flow on networks. For illustrative purposes,
we focus on the simple case of the intersection of two roads. By exploiting one
of the main features of the multiscale method, namely its
dimension-independence, we treat one-dimensional roads and two-dimensional
junctions in a natural way, without referring to classical network theory.
Furthermore, thanks to the coupling between the microscopic and the macroscopic
scales, we model the continuous flow of cars without losing the right amount of
granularity, which characterizes the real physical system and triggers
self-organization effects, such as, for example, the oscillatory patterns
visible at jammed uncontrolled crossroads.Comment: 7 pages, 7 figure
Blended numerical schemes for the advection equation and conservation laws
In this paper we propose a method to couple two or more explicit numerical
schemes approximating the same time-dependent PDE, aiming at creating new
schemes which inherit advantages of the original ones. We consider both
advection equations and nonlinear conservation laws. By coupling a macroscopic
(Eulerian) scheme with a microscopic (Lagrangian) scheme, we get a new kind of
multiscale numerical method
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