Are crowds on the internet wiser than experts? The case of a stock prediction community

Nofer, Michael; Hinz, Oliver

doi:10.1007/s11573-014-0720-x

Cited by 50 publications

(39 citation statements)

References 89 publications

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“…However, only a few studies compare the performance of online innovation communities consisting of dedicated amateurs, such as in innovation contests, with highly paid experts in firms (L€ uttgens et al, 2014;Magnusson, W€ astlund, and Netz, 2014). Despite experts' having superior skills, Internet evidence suggests that a large number of people are keeping pace with experts (e.g., the case of Wikipedia [Nofer and Hinz, 2014]). Emphasizing the innovation potential of external individuals, studies indicate that an increased distance between a solver's field of expertise and the problem domain is associated with a higher likelihood of success from the solver's perspective (Jeppesen and Lakhani, 2010).…”

Section: Limitations and Future Researchmentioning

confidence: 99%

The Role of Innovation Intermediaries in Firm‐Innovation Community Collaboration: Navigating the Membership Paradox

Lauritzen¹

2017

J of Product Innov Manag

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Research on user innovation shows that tensions in collaborations between firms and innovation communities can hinder innovation, and that innovation intermediaries can help resolve these tensions by bridging opposing interests. Despite the compelling role of innovation intermediaries, few studies on such mediation exist. Using an embedded case study, this article examines the role of an innovation intermediary that facilitates online innovation contests for client firms and identifies an apparent membership paradox evolving around three key tensions of power, competence, and identity. The article reveals that innovation intermediaries shape new understandings of power, competence, and identity that shift focus from resolving tensions to managing paradoxes. The membership paradox (re)appears at both the project level (between control and openness) and at the individual level, between professionalism and personality for employees and between imitation and authenticity for community participants. This article contributes to the user innovation literature by demonstrating how opposing firm and community interests are mediated through managing new forms of membership uncertainty. Moreover, the lens of paradox management offers a novel dimension to explaining why tensions that arise between firms and innovation communities are difficult to resolve, and also how the ensuing gaps in mutual understanding might be tackled. Theoretical and managerial implications of these findings for user innovation researchers and practitioners are discussed.

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Section: Limitations and Future Researchmentioning

confidence: 99%

The Role of Innovation Intermediaries in Firm‐Innovation Community Collaboration: Navigating the Membership Paradox

Lauritzen¹

2017

J of Product Innov Manag

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“…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)(7)(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).…”

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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.

326

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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“…Our results help generalize collective intelligence research to political domains. collective intelligence | polarization | networks | the wisdom of crowds | deliberative democracy A major concern for democratic theorists is that citizens are simply too ignorant of basic political facts to benefit from deliberation (1), yet research on the "wisdom of crowds" (2)(3)(4) has found the aggregated beliefs of large groups can be "wise"i.e., factually accurate-even when group members are individually inaccurate. While these statistical theories offer optimistic support for democratic principles (5,6), normative theories of deliberative democracy remain challenged by the argument that social influence processes-in contrast with the aggregation of independent survey responses-amplify group biases (7)(8)(9).…”

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confidence: 99%

“…One argument against deliberative democracy derives from a common premise in the wisdom of crowds theory, which states that for groups to produce accurate beliefs, individuals within those groups must be statistically independent, such that their errors are uncorrelated and cancel out in aggregate (3,10,11). When individuals can influence each other, the dynamics of herding and groupthink are expected to undermine belief accuracy (10,11), an argument that has raised concerns about the value of deliberative democracy (12).…”

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confidence: 99%

The wisdom of partisan crowds

Becker

Porter

Centola

2019

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

130

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Theories in favor of deliberative democracy are based on the premise that social information processing can improve group beliefs. While research on the "wisdom of crowds" has found that information exchange can increase belief accuracy on noncontroversial factual matters, theories of political polarization imply that groups will become more extreme-and less accurate-when beliefs are motivated by partisan political bias. A primary concern is that partisan biases are associated not only with more extreme beliefs, but also with a diminished response to social information. While bipartisan networks containing both Democrats and Republicans are expected to promote accurate belief formation, politically homogeneous networks are expected to amplify partisan bias and reduce belief accuracy. To test whether the wisdom of crowds is robust to partisan bias, we conducted two web-based experiments in which individuals answered factual questions known to elicit partisan bias before and after observing the estimates of peers in a politically homogeneous social network. In contrast to polarization theories, we found that social information exchange in homogeneous networks not only increased accuracy but also reduced polarization. Our results help generalize collective intelligence research to political domains. collective intelligence | polarization | networks | the wisdom of crowds | deliberative democracy

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Are crowds on the internet wiser than experts? The case of a stock prediction community

Cited by 50 publications

References 89 publications

The Role of Innovation Intermediaries in Firm‐Innovation Community Collaboration: Navigating the Membership Paradox

The Role of Innovation Intermediaries in Firm‐Innovation Community Collaboration: Navigating the Membership Paradox

Network dynamics of social influence in the wisdom of crowds

The wisdom of partisan crowds

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