Naïve Learning in Social Networks and the Wisdom of Crowds

Golub, Benjamin; Jackson, Matthew O.

doi:10.1257/mic.2.1.112

Cited by 846 publications

(772 citation statements)

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“…There are many examples of loose social networks, such as friends, online networks, neighborhoods, ethnic groups, classrooms, clubs, and professional networks (e.g., close work colleagues). The size of loose social networks implies that the potential for strategic interaction is small (Golub and Jackson 2010).…”

Section: State Of the Artmentioning

confidence: 99%

Incorporating social interaction into hybrid choice models

2014

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The aim of this paper is to develop a methodological framework for the incorporation of social interaction effects into choice models. The developed method provides insights for modeling the effect of social interaction on the formation of psychological factors (latent variables) and on the decision-making process. The assumption is based on the fact that the way the decision maker anticipates and processes the information regarding the behavior and the choices exhibited in her/his social environment, affects her/ his attitudes and perceptions, which in turn affect her/his choices. The proposed method integrates choice models with decision makers' psychological factors and latent social interaction. The model structure is simultaneously estimated providing an improvement over sequential methods as it provides consistent and efficient estimates of the parameters. The methodology is tested within the context of a household aiming to identify the social interaction effects between teenagers and their parents regarding walking-loving behavior and then the effect of this on mode to school choice behavior. The sample consists of 9,714 participants aged from 12 to 18 years old, representing 21 % of the adolescent population of Cyprus. The findings from the case study indicate that if the teenagers anticipate that their parents are walking lovers, then this increases the probability of teenagers to be walking-lovers too and in turn to choose walking to school. Generally, the findings from the application result in: (a) improvements in the explanatory power of choice models, Transportation (2014) 41:1263-1285 DOI 10.1007 (b) latent variables that are statistically significant, and (c) a real-world behavioral representation that includes the social interaction effect.

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Section: State Of the Artmentioning

confidence: 99%

Incorporating social interaction into hybrid choice models

2014

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“…described in work by French (1956) and Harary (1959), and was more completely specified and developed by DeGroot (1974). It has been used and extended by Besag (1974), Krause (2000), Friedkin and Johnsen (1997), DeMarzo, Vayanos, and Zwiebel (2003), Lorenz (2005), and Golub andJackson (2008, 2010), among many others. I will refer to it as the "DeGroot model."…”

Section: Learningmentioning

confidence: 99%

“…The divergence from Bayesian behavior comes from the fact that agents do not adjust their updating rule over time to account for the network structure: some friends may be talking to more people over time than others, and so forth. Despite this boundedly rational behavior, there are still many situations where the society eventually reaches a consensus that correctly approximates the unknown µ, as shown by Golub and Jackson (2010). Whether or not the DeGroot process converges to accurate estimate (and hence the Bayesian estimate) depends on how well balanced the relative weights are that different groups of agents place on each other.…”

Section: Learningmentioning

confidence: 99%

An Overview of Social Networks and Economic Applications

Jackson¹

2011

Handbook of Social Economics

216

147

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In this chapter I provide an overview of research on social networks and their role in shaping behavior and economic outcomes. I include discussion of empirical and theoretical analyses of the role of social networks in markets and exchange, learning and diffusion, and network games. I also include some background on social network characteristics and measurements, models of network formation, models for the statistical analysis of social networks, as well as community detection.

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“…When social influence leads to correlated errors, both independence and diversity are reduced, which has been argued to compromise the reliability of the group judgment (9,(11)(12)(13)(14)(15)(16)(17)(18). In direct contrast with these results, however, theoretical models of social learning (19-21) have suggested that the effects of social influence on collective decisions vary based on the structure of the interaction network, predicting that, under the right conditions, social learning can lead a group's median judgment to improve (20)(21)(22)(23)(24).This prediction derives from the assumption that, when people learn about the beliefs of others, they revise their own beliefs to become more similar to their social referents (11, 12, 25, 26). Following the DeGroot model of social learning, this theory suggests that each individual's revisions are based on a weighted average of their own belief and the beliefs of their social referents (19).…”

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confidence: 99%

Network dynamics of social influence in the wisdom of crowds

Becker

Brackbill

Centola

2017

Proc. Natl. Acad. Sci. U.S.A.

325

258

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A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907 social networks | collective intelligence | social learning | wisdom of crowds | experimental social science S ince Galton's discovery of the "wisdom of crowds" over 100 years ago (1), results on crowdsourcing (1, 2), prediction markets (3), and financial forecasting (4, 5) have shown that the aggregated judgment of many individuals can be more accurate than the judgments of individual experts (2, 4, 6-8). Statistical explanations for this phenomenon argue that group accuracy relies on estimates taken from groups where individuals' errors are either uncorrelated or negatively correlated, thereby preserving the diversity of opinions in a population (9). Thus, although individuals may have estimates both far above and far below the true value, in aggregate these errors cancel out, leaving an accurate group judgment (2, 9, 10).Recent experimental evidence has suggested that the wisdom of crowds may be undermined by processes of social influence, in which people exchange information about their estimates and revise their judgments to align with one another (11-13). When social influence leads to correlated errors, both independence and diversity are reduced, which has been argued to compromise the reliability of the group judgment (9,(11)(12)(13)(14)(15)(16)(17)(18). In direct contrast with these results, however, theoretical models of social learning (19-21) have suggested that the effects of social influence on collective decisions vary based on the structure of the interaction network, predicting that, under the right conditions, social learning can lead a group's median judgment to improve (20)(21)(22)(23)(24).This prediction derives from the assumption that, when people learn about the beliefs of others, they revise their own beliefs to become more similar to their social referents (11, 12, 25, 26). Following the DeGroot model of social learning, this theory suggests that each individual's revisions are based on a weighted average of their own belief and the beliefs of their social referents (19). Thus, an individual's revision is determined in part by the amount of weight they place on their own belief relative to social information. When this "self-weight" is independently and identically distributed (i.i.d.) throughout a population, and the population is embedded in a decentralized social network (i.e., one in which everyone is equally connected), this model predicts that belief distributions will converge on the statistical mean of the initial, independent beliefs (SI Appendix). Thus, if the initial group mean is accurate, exposure to social influence will lead individuals in the group to become more accurate, improving the accuracy of the group's median, even as the group mean remains fixed (SI Appendix).We build on the DeGroot model to generate theoretical predictions ...

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Naïve Learning in Social Networks and the Wisdom of Crowds

Cited by 846 publications

References 30 publications

Incorporating social interaction into hybrid choice models

Incorporating social interaction into hybrid choice models

An Overview of Social Networks and Economic Applications

Network dynamics of social influence in the wisdom of crowds

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