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Found 8 papers, 4 papers with code
Group Decision-Making among Privacy-Aware Agents
How can individuals exchange information to learn from each other despite their privacy needs and security concerns?
Differentially Private Distributed Estimation and Learning
We show that the noise that minimizes the convergence time to the best estimates is the Laplace noise, with parameters corresponding to each agent's sensitivity to their signal and network characteristics.
When social influence promotes the wisdom of crowds
By adopting a framework that integrates both the structure of the social influence and the distribution of the initial estimates, we bring previously conflicting results under one theoretical framework and clarify the effects of social influence on the wisdom of crowds.
Social and Information Networks Physics and Society Applications
Seeding with Costly Network Information
We study the task of selecting $k$ nodes, in a social network of size $n$, to seed a diffusion with maximum expected spread size, under the independent cascade model with cascade probability $p$.
Social and Information Networks Computational Complexity Probability Physics and Society
Long ties accelerate noisy threshold-based contagions
To model the trade-off between long and short edges we analyze the rate of spread over networks that are the union of circular lattices and random graphs on $n$ nodes.
Social and Information Networks Probability Physics and Society 91D30, 05C80
Bayesian Decision Making in Groups is Hard
We study the computations that Bayesian agents undertake when exchanging opinions over a network.
Learning without recall in directed circles and rooted trees
While such repeated applications of the Bayes' rule in networks can become computationally intractable, in this paper, we show that in the canonical cases of directed star, circle or path networks and their combinations, one can derive a class of memoryless update rules that replicate that of a single Bayesian agent but replace the self beliefs with the beliefs of the neighbors.
Distributed Estimation and Learning over Heterogeneous Networks
In each case we rely on an aggregation scheme to combine the observations of all agents; moreover, when the agents receive streams of data over time, we modify the update rules to accommodate the most recent observations.
