
A distributed (preconditioned) projectedreflectedgradient algorithm for stochastic generalized Nash equilibrium problems
We consider the stochastic generalized Nash equilibrium problem (SGNEP) ...
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Noidy Conmunixatipn: On the Convergence of the Averaging Population Protocol
We study a process of averaging in a distributed system with noisy commu...
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Stochastic generalized Nash equilibrium seeking under partialdecision information
We consider for the first time a stochastic generalized Nash equilibrium...
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Simple and Local Independent Set Approximation
We bound the performance guarantees that follow from Turánlike bounds f...
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Quantization Games on Social Networks and Language Evolution
We consider a strategic network quantizer design setting where agents mu...
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Intense Competition can Drive Selfish Explorers to Optimize Coverage
We consider a gametheoretic setting in which selfish individuals compet...
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Stochastic Alignment Processes
The tendency to align to others is inherent to social behavior, including in animal groups, and flocking in particular. Here we introduce the Stochastic Alignment Problem, aiming to study basic algorithmic aspects that govern alignment processes in unreliable stochastic environments. Consider n birds that aim to maintain a cohesive direction of flight. In each round, each bird receives a noisy measurement of the average direction of others in the group, and consequently updates its orientation. Then, before the next round begins, the orientation is perturbed by random drift (modelling, e.g., the affects of wind). We assume that both noise in measurements and drift follow Gaussian distributions. Upon receiving a measurement, what should be the orientation adjustment policy of birds if their goal is to minimize the average (or maximal) expected deviation of a bird's direction from the average direction? We prove that a distributed weightedaverage algorithm, termed W , that at each round balances between the current orientation of a bird and the measurement it receives, maximizes the social welfare. Interestingly, the optimality of this simple distributed algorithm holds even assuming that birds can freely communicate to share their gathered knowledge regarding their past and current measurements. We find this result surprising since it can be shown that birds other than a given i can collectively gather information that is relevant to bird i, yet not processed by it when running a weightedaverage algorithm. Intuitively, it seems that optimality is nevertheless achieved, since, when running W , the birds other than i somehow manage to collectively process the aforementioned information in a way that benefits bird i, by turning the average direction towards it. Finally, we also consider the gametheoretic framework, proving that W is the only weightedaverage algorithm that is at Nash equilibrium.
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