Is the true ‘wisdom of the crowd’ to copy successful individuals?

King, Andrew J.; Cheng, L.; Starke, Sandra D.; Myatt, Julia P.

doi:10.1098/rsbl.2011.0795

Cited by 57 publications

(75 citation statements)

References 11 publications

(14 reference statements)

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“…While there is growing evidence that the wisdom of crowds leads to substantially improved decision accuracy [16][17][18][19][20]62], it is still not well understood how it operates in complex, real-world conditions. For example, how social information is shared between group members can affect the resulting decision accuracy [40] and can often undermine the wisdom of crowds by improving confidence in the collective decision without improving its accuracy [63]. Here, we have shown that the external environment from which group members draw information can also substantially affect the wisdom of crowds by introducing correlations in group members' opinions.…”

Section: Resultsmentioning

confidence: 82%

Decision accuracy in complex environments is often maximized by small group sizes

2014

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Individuals in groups, whether composed of humans or other animal species, often make important decisions collectively, including avoiding predators, selecting a direction in which to migrate and electing political leaders. Theoretical and empirical work suggests that collective decisions can be more accurate than individual decisions, a phenomenon known as the 'wisdom of crowds'. In these previous studies, it has been assumed that individuals make independent estimates based on a single environmental cue. In the real world, however, most cues exhibit some spatial and temporal correlation, and consequently, the sensory information that near neighbours detect will also be, to some degree, correlated. Furthermore, it may be rare for an environment to contain only a single informative cue, with multiple cues being the norm. We demonstrate, using two simple models, that taking this natural complexity into account considerably alters the relationship between group size and decision-making accuracy. In only a minority of environments do we observe the typical wisdom of crowds phenomenon (whereby collective accuracy increases monotonically with group size). When the wisdom of crowds is not observed, we find that a finite, and often small, group size maximizes decision accuracy. We reveal that, counterintuitively, it is the noise inherent in these small groups that enhances their accuracy, allowing individuals in such groups to avoid the detrimental effects of correlated information while exploiting the benefits of collective decision-making. Our results demonstrate that the conventional view of the wisdom of crowds may not be informative in complex and realistic environments, and that being in small groups can maximize decision accuracy across many contexts.

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Section: Resultsmentioning

confidence: 82%

Decision accuracy in complex environments is often maximized by small group sizes

2014

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“…The conclusion of his study was that simple averaging of individual estimates is, as an empirical matter, a more useful way to estimate quantities than relying on faulty individual opinions. In addition to Galton's work, another classic study of crowd intelligence involved subjects estimating the number of jelly beans or marbles contained in a jar (Treynor, 1987;Krause et al, 2011;King et al, 2012). The true number of beans is typically between 500 and 1,000, so exact counting is not feasible for the subjects.…”

Section: Measures Of Intelligencementioning

confidence: 99%

Rescuing Collective Wisdom when the Average Group Opinion Is Wrong

2017

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The total knowledge contained within a collective supersedes the knowledge of even its most intelligent member. Yet the collective knowledge will remain inaccessible to us unless we are able to find efficient knowledge aggregation methods that produce reliable decisions based on the behavior or opinions of the collective's members. It is often stated that simple averaging of a pool of opinions is a good and in many cases the optimal way to extract knowledge from a crowd. The method of averaging has been applied to analysis of decision-making in very different fields, such as forecasting, collective animal behavior, individual psychology, and machine learning. Two mathematical theorems, Condorcet's theorem and Jensen's inequality, provide a general theoretical justification for the averaging procedure. Yet the necessary conditions which guarantee the applicability of these theorems are often not met in practice. Under such circumstances, averaging can lead to suboptimal and sometimes very poor performance. Practitioners in many different fields have independently developed procedures to counteract the failures of averaging. We review such knowledge aggregation procedures and interpret the methods in the light of a statistical decision theory framework to explain when their application is justified. Our analysis indicates that in the ideal case, there should be a matching between the aggregation procedure and the nature of the knowledge distribution, correlations, and associated error costs. This leads us to explore how machine learning techniques can be used to extract near-optimal decision rules in a data-driven manner. We end with a discussion of open frontiers in the domain of knowledge aggregation and collective intelligence in general.

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“…Indeed, it has long been recognized that 'elites' or 'key individuals' may contribute disproportionately to collective processes [28, [40][41][42]. On the other hand, for effective exploitation of the 'wisdom of the crowds' effect in decision-making, individual contributions should be independent, and increasing the diversity of individual contributors can be more beneficial than adding expertise [43,44]. It thus remains unclear to what degree decision-making in social insects relies on specialists.…”

Section: Introductionmentioning

confidence: 99%

Ants work harder during consensus decision-making in small groups

Cronin

Stumpe²

2014

J. R. Soc. Interface.

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Individuals derive many benefits from being social, one of which is improved accuracy of decision-making, the so-called 'wisdom of the crowds' effect. This advantage arises because larger groups can pool information from more individuals. At present, limited empirical data indicate that larger groups outperform smaller ones during consensus decision-making in human and non-human animals. Inaccurate decisions can lead to significant costs, and we might therefore expect individuals in small groups to employ mechanisms to compensate for the lack of numbers. Small groups may be able to maintain decision accuracy if individuals are better informed than those in larger groups and/or by increasing the proportion of the group involved in collective decision-making relative to larger groups. In this study, we use interactive computer vision software to investigate individual contributions to consensus decision-making during house-hunting in different sized groups of the ant Myrmecina nipponica. We show that individuals in small colonies invest greater effort in the consensus decision process than those in large colonies and should be better informed as a result. This may act to ameliorate the limitations of group size, but could leave smaller groups more susceptible to additional stresses.

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Is the true ‘wisdom of the crowd’ to copy successful individuals?

Cited by 57 publications

References 11 publications

Decision accuracy in complex environments is often maximized by small group sizes

Decision accuracy in complex environments is often maximized by small group sizes

Rescuing Collective Wisdom when the Average Group Opinion Is Wrong

Ants work harder during consensus decision-making in small groups

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