Naïve Learning in Social Networks and the Wisdom of Crowds

By Benjamin Golub, Matthew O. Jackson. 2010

1,159 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/43e04c5afbfa4e0e52bbe8b30aadcfd51e62c8c3

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

Opinion Dynamics and the Evolution of Social Power in Influence Networks (2015, background): “Much ongoing interest is focusing on dynamic models of structural change [1] [25] [43] [54] [70], and on a broad range of dynamic processes unfolding over static networks; examples include the study of social learning [2] [35], opinion formation [23] [32] and information propagation [25] [57] [60].”
Networks, Shocks, and Systemic Risk (2015, background): “In particular, Golub and Jackson (2010) show that if agents update their beliefs as a linear combination of their neighbors’ opinions (what is commonly known as DeGroot-style learning), the information available to those with higher centralities plays a more prominent role in the eventual beliefs…”
Opinion Dynamics and Learning in Social Networks (2011, background): “Necessary and sufficient conditions for reaching a consensus in this model are presented in [51].”
Manufacturing Consent (2015, background): “Convergence under repeated averaging has also been addressed in many papers: [13] investigates consensus, [9], [14] investigate whether individuals will learn the true value of a state of nature using (1) if they have noisy versions of it to start with.”
Social Networks, Reputation, and Commitment: Evidence From a Savings Monitors Experiment (2019, background): “See also DeMarzo et al. (2003),Golub and Jackson (2012), Golub and Jackson (2010), and Hagen and Kahng (1992). more correlated.”
Communication and influence (2015, background): “Golub and Jackson (2010) study a learning model where agents communicate on a network and process signals they receive naively.”
The Economic Consequences of Social Network Structure (2017, positive): “…been shown to have both theoretical and empirical importance in various settings including whether a society efficiently aggregates information (Golub and Jackson (2010)), how much criminal activity a society engages in (Calvó-Armengol and Zenou (2004), Ballester, Calvó-Armengol, and Zenou…”
Heterogeneous peer effects in education (2014, neutral): “lim→∞P b where 1 =  ∞ 2 =  ∞ 3 = 1 3  (0) 1 + 4 9  (0) 2 + 2 9  (0) 3 (27) This example shows that the beliefs converge over time and that agents reach a consensus but it also shows that agent 2 is the most influential individual in the network over the limiting beliefs.”
Information Heterogeneity and the Speed of Learning in Social Networks (2013, background): “Golub and Jackson (2010), for example, provide conditions on the structures of a growing sequence of social networks under which asymptotic opinions of all agents converge to the truth.”
Characterizing and Detecting Hateful Users on Twitter (2018, methodology): “Run a diffusion process on the graph based on DeGroot’s Learning Model (Golub and Jackson 2010), assigning an initial belief p 0 i = 1 to each user u i who employed the words in the lexicon; This prevents our sample from being excessively small or biased towards some vocabulary.”
Evaluation on crowdsourcing research: Current status and future direction (2012, methodology): “However, we suggest that more work should be done to prove whether this method is valid and effective due to the ‘ information cascading ’ which arises when individual ’ s opinion about the merit of a given product or service are influenced by those of others (Johnson 2007).”
Moderating with the Mob: Evaluating the Efficacy of Real-Time Crowdsourced Fact-Checking (2021, background): “…intelligence suggests that aggregated estimates from groups of ordinary people converge on accurate judgements, even if the average individual estimate in the group is not especially accurate (Golub and Jackson 2010; Surowiecki 2005; Woolley et al. 2010; Woolley, Aggarwal, and Malone 2015).”
Binary Opinion Dynamics with Stubborn Agents (2013, methodology): “Because the updates of the DeGroot model has the exact same form as the synchronous average consensus algorithms (see [Tsitsiklis 1984]), its analysis is relatively straightforward (see, e.g., [Golub and Jackson 2010]).”
Presidential Address: Social Transmission Bias in Economics and Finance (2020, neutral): “Biased: Ellison and Fudenberg (1993), DeMarzo, Vayanos, and Zwiebel (2001, 2003), Eyster and Rabin (2010), Golub and Jackson (2010), and Bohren (2016).”
Opinion Fluctuations and Disagreement in Social Networks (2010, neutral): “Existing models of communication and learning, based on Bayesian or non-Bayesian updating mechanisms, typically lead to consensus provided that communication takes place over a strongly connected network (e.g., Smith and Sorensen [46], Banerjee and Fudenberg [7], Acemoglu, Dahleh, Lobel and Ozdaglar”