Reaching a Consensus

By Morris H. Degroot. 1974

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Citations in the corpus

Peer-to-peer Federated Learning on Graphs (2019, background): “Social learning on a graph has long been studied in statistics, economics, and operations research and encompasses canonical problems of consensus DeGroot (1974), belief propagation (Olfati-Saber et al., 2005), and distributed hypothesis testing (Jad-babaie et al., 2013; Nedi ´ c et al., 2015;…”
An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, background): “[270] B. J. Moore and K. M. Passino, “Decentralized redistribution for cooperative patrol,”International Journal of Robust and Nonlinear Control, vol. 18, no. 2, pp. 165–195, January 2008.”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, methodology): “…unknown value of some parameter; a model which describes how the group reaches agreement by pooling their individual opinions was described by DeGroot [91] and was used decades later for the aggregation of information with uncertainty obtained from multiple sensors [92] and medical experts [93].”
Convergence Speed in Distributed Consensus and Averaging (2009, unknown): “It was introduced in [10] for the time-invariant case, and in [30, 31] for the case of “asynchronous” and time-1 varying environments.”
Leader-Based Optimal Coordination Control for the Consensus Problem of Multiagent Differential Games via Fuzzy Adaptive Dynamic Programming (2015, background): “2310238 Subsequently, these problems were developed for management science and statistics [11].”
Privacy preserving average consensus (2014, background): “Early researches use consensus to model and analyze phenomena such as agreement of opinions by a group of individuals [1] and decision making by decentralized processors [2].”
A review on trust propagation and opinion dynamics in social networks and group decision making frameworks (2019, methodology): “Opinion dynamics can be regarded, on the context of social networks, as the way in which the network is created and evolves provoking individuals’ attitudes, opinions and behaviours toward particular objects to change taking into consideration the displayed attitudes, opinions, and behaviours of oth”
Coordination in multiagent systems and Laplacian spectra of digraphs (2009, methodology): “In addition to the works discussed in [1], one can remember the theory of statistical consensus by DeGroot [2], methods of stepby-step coordination of expert judgements, such as Delphi methodology developed at the RAND Corporation in the 1950s [3], works on distributed networked computations and dis”
The Limits of Popularity-Based Recommendations, and the Role of Social Ties (2016, unknown): “Two earlier studies, [9] on consensus reaching and [13] on opinion formation, investigated the convergence of personal beliefs of users interacting in a social group, similarly to how we investigate convergence in a market with social ties.”
Agreeing Asynchronously (2008, background): “Various mathematically similar versions of Vicsek’s problem have been addressed in the literature [6]–[11] some it turns out well before Vicsek’s own paper was published [3], [12]–[15].”
How Homophily Affects the Speed of Learning and Best Response Dynamics (2012, methodology): “Another updating process in this class is the classic DeGroot (1974) model of learning and consensus in which one’s opinion tomorrow about some unknown quantity is an average of the opinions of one’s friends today.”
Opinion dynamics in social networks with stubborn agents: Equilibrium and convergence rate (2014, background): “Key words: Multi-agent systems, Markov models, Equilibrium, Eigenvalues”
Opinion Dynamics and Learning in Social Networks (2011, methodology): “We ﬁrst present a classical model introduced in [28], which is a simple model of belief and consensus formation over social networks (see also [30, 50]).”
Distributed Finite-Time Average Consensus in Digraphs in the Presence of Time Delays (2015, background): “When the agents have reached an agreement, we say that the distributed system has reached consensus.”
The echo chamber effect on social media (2021, background): “For instance, if opinions are assumed to be embedded in a onedimensional space (38), x ∈ [−1,+1] without loss of generality, as usual for controversial topics, then polarization is characterized by two well-separated peaks in P(x ), for positive and negative opinions.”
Game On: Social Networks and Markets (2021, methodology): “The network is therefore characterized by W , which is called the transition matrix or weight matrix in the DeGroot (1974) model. The i’th row, Wi, can have many zeros, representing all the people that i do not “follow.” The non-zero elements represent the list of people that i “follows” and the amo”