Network Of Heterogeneous Agents Research Articles

A General Framework for Distributed Inference With Uncertain Models

This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations. Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network. Most existing approaches assume that agents have access to exact statistical models for each hypothesis. However, in many practical applications, agents learn the likelihood models based on limited data, which induces uncertainty in the likelihood function parameters. In this work, we build upon the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uncertain models</i> to incorporate the agents’ uncertainty in the likelihoods by identifying a broad set of parametric distribution that allows the agents’ beliefs to converge to the same result as a centralized approach. Furthermore, we empirically explore extensions to non-parametric models to provide a generalized framework of uncertain models in non-Bayesian social learning.

Adaptive Susceptibility and Heterogeneity in Contagion Models on Networks

Contagious processes, such as spread of infectious diseases, social behaviors, or computer viruses, affect biological, social, and technological systems. Epidemic models for large populations and finite populations on networks have been used to understand and control both transient and steady-state behaviors. Typically it is assumed that after recovery from an infection, every agent will either return to its original susceptible state or acquire full immunity to reinfection. We study the network SIRI (Susceptible-Infected-Recovered-Infected) model, an epidemic model for the spread of contagious processes on a network of heterogeneous agents that can adapt their susceptibility to reinfection. The model generalizes existing models to accommodate realistic conditions in which agents acquire partial or compromised immunity after first exposure to an infection. We prove necessary and sufficient conditions on model parameters and network structure that distinguish four dynamic regimes: infection-free, epidemic, endemic, and bistable. For the bistable regime, which is not accounted for in traditional models, we show how there can be a rapid resurgent epidemic after what looks like convergence to an infection-free population. We use the model and its predictive capability to show how control strategies can be designed to mitigate problematic contagious behaviors.

Contests over joint production on networks

AbstractWe consider a network of heterogeneous agents where each edge represents a two‐player contest between the respective nodes. In these bilateral contests, agents compete over an endogenous prize jointly produced using their own contest efforts. We provide a necessary and sufficient condition for the existence of Nash equilibrium and characterize the equilibrium total effort for every agent. Our model has insightful results regarding the network type, that is, depending on whether the network is bipartite or nonbipartite. Finally, considering the sum of all expected utilities as an efficiency notion, we investigate the optimal network structure.

Gossip-Based Distributed Tracking in Networks of Heterogeneous Agents

We consider the distributed tracking problem in networks of heterogeneous agents with limited sensing and communication ranges. A gossip-based distributed Kalman filter (GDKF) is proposed, where an average consensus on predictions of different agents is achieved by randomized, asynchronous gossip algorithms in a totally distributed way. The error dynamics of GDKF is proved to be a globally asymptotically stable system and the error reduction rate is provided. To demonstrate the improved performance of GDKF, we compare it with an alternative distributed estimation strategy termed Kalman-Consensus Filter (KCF) by implementing them to track a maneuvering target collectively with heterogeneous agents.

Synchronization of Networks of Heterogeneous Agents With Common Nominal Behavior

This paper deals with the problem of synchronization in networks of heterogeneous agents with common nominal behavior. The agents are modeled as (possibly nonlinear) perturbed versions of a common SISO nominal LTI operator, and they are interconnected via a sparse memoryless interconnection operator, coherent with the communication graph underlying the network. The network is said to synchronize if the outputs of the agents tend to align along given directions, the most important case being consensus, or agreement. The paper provides a general result, based on IQCs, that ensures synchronization of the network with robustness w.r.t. uncertainties in the interconnection and in each agent's dynamics. Scalability issues are discussed in the scenario where the interconnection operator is a constant normal matrix, yielding the generalization of the very popular linear consensus algorithm. The wide range of applicability of the proposed criterion is shown by providing synchronization conditions in some important examples. Whenever possible, graphical criteria are proposed for checking the required conditions.

Strong random correlations in networks of heterogeneous agents

Correlations and other collective phenomena are considered in a schematic model of pairwise interacting, competing and collaborating agents facing a binary choice and situated at the nodes of the complete graph and a 2-dimensional regular lattice, respectively. The agents may be subjected to an idiosyncratic or common external influence and also some random noise. The system’s stochastic dynamics is studied by numerical simulations. It displays the characteristics of punctuated, multiple equilibria, sensitivity to small details, and path dependence. The dynamics is so slow that one can meaningfully speak of quasi-equilibrium states. Performing measurements of correlations between the agents choices we find that they are random both as to their sign and absolute value, but their distribution is very broad when the interaction dominates both the noise and the external field. This means that random but strong correlations appear with large probability. In the two dimensional model this also implies that the correlations on average fall off with distance very slowly: the system is essentially non-local, small changes at one end may have a strong impact at the other. The strong, random correlations tend to organize a large fraction of the agents into strongly correlated clusters that act together. If we think of this model as a metaphor of social or economic agents or bank networks, the systemic risk implications of this tendency are clear: any impact on even a single strongly correlated agent will not be confined to a small set but will spread, in an unforeseeable manner, to the whole system via the strong random correlations.