Price-based mean-field models of battery storage coordination usually assume that each agent responds to the true population-average charging power. Under that assumption, communication topology is irrelevant because the broadcast price already carries the coupling that matters. We study a nearby regime in which agents respond to a shared noisy forecast of the average, with correlation rho between agents'forecast errors. Analytically and in simulation, we find that topology remains undetectable in the effective-dimensional response of the fleet, even when neighbour observation is the only explicit communication signal. The mechanism is structural: the correlated forecast error projects onto the graph-invariant consensus mode, while topology acts through transverse modes. As rho N grows, the consensus-mode variance dominates and the spectral participation ratio approaches one independently of graph topology. Simulations on linear, star, and small-world graphs confirm that topology-induced variation is below the variation caused by redrawing the forecast noise. The result is not a claim that topology has no dynamical effect, but that shared stochastic forcing can mask topology-dependent modes in decentralized storage fleets.
This work forms a correction-aware network model that tracks susceptible, exposed, infectious, and corrected agents and derive its early-invasion condition for heterogeneous communication networks, and couple this propagation model to an analytic majority-vote benchmark in which a clean-task reliability target imposes a minimum connectivity requirement.
The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!\propto\!N$ (Sarnoff), fully-connected meshes as $N^2$ (Metcalfe), and group-forming networks as $2^{N}$ (Reed). We ask the analogous question for networks of AI agents. We model the net value of connection as a function of coordination-group size, derive from it the properties an optimal collaboration protocol must have, and introduce ANet Patu-1 -- a self-organizing consensus protocol in which the network continuously re-forms its own coalitions, adaptively riding the upper envelope of all three regimes at $O(1)$ parallel consensus rounds. To measure value without opinion-grading, we score an emergent protocol by formally specifying it and deriving its complexity, the way distributed algorithms are analyzed. Two results follow. (i)~Emergence -- a crowd of the \emph{cheapest} model, when heterogeneous, starts weak but its collective value compounds with $N$ and \emph{overtakes} a crowd of a far \emph{stronger} model that is homogeneous: a crossover that marks a scaling law for collaboration rather than for scale. (ii)~Reflexivity -- a heterogeneous network, given only its own problem and no design hints, converges on ANet Patu-1 itself, reconstructing the high-dimensional law that governs its own connective value.
Mu Yuan, Jinke Song, Zhaomeng Zhou et al.· 0 citations
Experimental design under network interference is challenging because outcomes may depend on the treatment assignments of neighboring units. Existing approaches account for network structure but are typically assessed on small or simplified networks, limiting their applicability to complex real-world settings. We propose a network-aware treatment allocation framework that jointly accounts for allocation balance and network topology via an optimality criterion based on the Fisher information matrix. To address the resulting combinatorial optimization problem, we develop an efficient local search algorithm that scales to large networks. We further study the causal properties of the resulting designs by examining the estimation of total, direct, and indirect treatment effects in the presence of interference. Simulation studies across a range of random graph models, including Erd\H{o}s--R\'enyi, geometric random graphs, preferential attachment, and stochastic block models, illustrate how network topology influences optimal treatment allocations. Applications to college housing and ego-Facebook networks demonstrate the practical advantages of topology-aware experimental designs.
Monitoring structural changes in large-scale networks is important in many applications, including communication, social, collaboration, and transportation systems. In this paper, we apply the optimized parametric spectrum-reduced bounds for Laplacian energy developed in our previous theoretical work to the problem of structural network monitoring. The proposed framework evaluates optimized lower and upper bounds together with spectral variance, the third central spectral moment, and algebraic connectivity using only a reduced set of spectral quantities and graph invariants. A voting-based decision mechanism is employed to classify the structural state of a network during its evolution. The framework is evaluated on six benchmark datasets representing different classes of real-world networks under four structural modification scenarios: line addition, line removal, targeted hub failure and rewiring. The experimental results show that gradual edge modifications produce relatively small changes in the monitoring indicators, whereas targeted hub failures lead to larger variations in the optimized bounds and the optimization gap. The study demonstrates the practical use of the proposed spectrum-reduced framework for monitoring structural changes without repeated computation of the complete Laplacian spectrum.
Anil Kumar, Anil Kumar Hanspal, Amit Kaura· Journal of Intelligent Decis...· 0 citations
This paper studies how network structures affect the efficiency of information aggregation in social learning environments. We consider a model in which rational agents sequentially choose actions based on private signals and observations of their neighbors'actions in a network. Focusing on comparisons of expected payoffs at a given finite period, we show that there exists an information structure under which the star network achieves a strictly higher expected payoff than any other network, and another information structure under which the complete network achieves a strictly higher expected payoff than any other network. Taken together, these results imply that no network is uniformly optimal across all information structures. Our analysis highlights a trade-off between the responsiveness effect and the overturning effect: disconnected networks preserve responsiveness of actions to private signals, whereas highly connected networks facilitate the aggregation of extreme information that overturns public beliefs.