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The focus of this paper is to quantify measures of aggregate fluctuations fora class of consensus-seeking multiagent networks subject to exogenous noisewith alpha-stable distributions. This type of noise is generated by a class ofrandom measures with heavy-tailed probability distributions. We define acumulative scale parameter using scale parameters of probability distributionsof the output variables, as a measure of aggregate fluctuation. Although thisclass of measures can be characterized implicitly in closed-form insteady-state, finding their explicit forms in terms of network parameters is,in general, almost impossible. We obtain several tractable upper bounds interms of Laplacian spectrum and statistics of the input noise. Our resultssuggest that relying on Gaussian-based optimal design algorithms will result innon-optimal solutions for networks that are driven by non-Gaussian noise inputswith alpha-stable distributions.  The manuscript has been submitted for publication to IEEE Transactions onControl of Network Systems. It is the extended version of preliminary paperincluded in the proceedings of the 2018 American Control Conference.

Aggregate Fluctuations in Networks with Drift-Diffusion Models Driven by Stable Non-Gaussian Disturbances