110 research outputs found
Structural permeability of complex networks to control signals
Many biological, social and technological systems can be described as complex networks. The goal of affecting their behaviour has motivated recent work focusing on the relationship between the network structure and its propensity to be controlled. While this work has provided insight into several relevant problems, a comprehensive approach to address partial and complete controllability of networks is still lacking. Here, we bridge this gap by developing a framework to maximize the diffusion of the control signals through a network, while taking into account physical and economic constraints that inevitably arise in applications. This approach allows us to introduce the network permeability, a unified metric of the propensity of a network to be controllable. The analysis of the permeability of several synthetic and real networks enables us to extract some structural features that deepen our quantitative understanding of the ease with which specific controllability requirements can be met
Wealth distribution across communities of adaptive financial agents
This paper studies the trading volumes and wealth distribution of a novel
agent-based model of an artificial financial market. In this model,
heterogeneous agents, behaving according to the Von Neumann and Morgenstern
utility theory, may mutually interact. A Tobin-like tax (TT) on successful
investments and a flat tax are compared to assess the effects on the agents'
wealth distribution. We carry out extensive numerical simulations in two
alternative scenarios: i) a reference scenario, where the agents keep their
utility function fixed, and ii) a focal scenario, where the agents are adaptive
and self-organize in communities, emulating their neighbours by updating their
own utility function. Specifically, the interactions among the agents are
modelled through a directed scale-free network to account for the presence of
community leaders, and the herding-like effect is tested against the reference
scenario. We observe that our model is capable of replicating the benefits and
drawbacks of the two taxation systems and that the interactions among the
agents strongly affect the wealth distribution across the communities.
Remarkably, the communities benefit from the presence of leaders with
successful trading strategies, and are more likely to increase their average
wealth. Moreover, this emulation mechanism mitigates the decrease in trading
volumes, which is a typical drawback of TTs.Comment: 18 pages, 7 figures, published in New Journal of Physic
Partial containment control over signed graphs
In this paper, we deal with the containment control problem in presence of
antagonistic interactions. In particular, we focus on the cases in which it is
not possible to contain the entire network due to a constrained number of
control signals. In this scenario, we study the problem of selecting the nodes
where control signals have to be injected to maximize the number of contained
nodes. Leveraging graph condensations, we find a suboptimal and computationally
efficient solution to this problem, which can be implemented by solving an
integer linear problem. The effectiveness of the selection strategy is
illustrated through representative simulations.Comment: 6 pages, 3 figures, accepted for presentation at the 2019 European
Control Conference (ECC19), Naples, Ital
Driver and Sensor Node Selection Strategies Optimizing the Controllability Properties of Complex Dynamical Networks
In recent years, complex networks have attracted the attention of researchers throughout the fields of science due to their ubiquity in natural and artificial settings. While the spontaneous emergence of collective behavior has been thoroughly studied, and has inspired researchers in the design of control strategies able to reproduce it in artificial scenarios, our ability to arbitrarily affect the behavior of complex networks is still limited. To start filling this void, in the past five years, researchers have focused on the preliminary condition of selecting the nodes where input signals have to be injected so to ensure complete controllability of complex networks. Unfortunately, the scale of complex networks is such that more often than not too many input signals are required to arbitrarily modify the behavior of all the nodes of a network. Departing from the idea that achieving complete controllability of complex networks is a chimera, in this thesis, we present a comprehensive toolbox of input selection algorithms so to ensure controllability of the largest number of nodes of a network. Then, we complement this toolbox with algorithms for sensor placement so to also guarantee, when possible, observability of these nodes, thus allowing the implementation of feedback control strategies. Finally, an outlook on the topics that are currently being investigated by researchers working on controllability of complex networks is provided
Formation control on Jordan curves based on noisy proximity measurements
The paradigmatic formation control problem of steering a multi-agent system
towards a balanced circular formation has been the subject of extensive studies
in the control engineering community. Indeed, this is due to the fact that it
shares several features with relevant applications such as distributed
environmental monitoring or fence-patrolling. However, these applications may
also present some relevant differences from the ideal setting such as the curve
on which the formation must be achieved not being a circle, or the measurements
being neither ideal nor as a continuous information flow. In this work, we
attempt to fill this gap between theory and applications by considering the
problem of steering a multi-agent system towards a balanced formation on a
generic closed curve and under very restrictive assumptions on the information
flow amongst the agents. We tackle this problem through an estimation and
control strategy that borrows tools from interval analysis to guarantee the
robustness that is required in the considered scenario
Overconfident agents and evolving financial networks
In this paper, we investigate the impact of agent personality on the complex dynamics taking place in financial markets. Leveraging recent findings, we model the artificial financial market as a complex evolving network: we consider discrete dynamics for the node state variables, which are updated at each trading session, while the edge state variables, which define a network of mutual influence, evolve continuously with time. This evolution depends on the way the agents rank their trading abilities in the network. By means of extensive numerical simulations in selected scenarios, we shed light on the role of overconfident agents in shaping the emerging network topology, thus impacting on the overall market dynamics
Partial containment control over signed graphs
In this paper, we deal with the containment control problem in presence of
antagonistic interactions. In particular, we focus on the cases in which it is
not possible to contain the entire network due to a constrained number of
control signals. In this scenario, we study the problem of selecting the nodes
where control signals have to be injected to maximize the number of contained
nodes. Leveraging graph condensations, we find a suboptimal and computationally
efficient solution to this problem, which can be implemented by solving an
integer linear problem. The effectiveness of the selection strategy is
illustrated through representative simulations
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