Crowds vs swarms, a comparison of intelligence

Rosenberg, Louis; Baltaxe, David; Pescetelli, Niccolò

doi:10.1109/shbi.2016.7780278

Cited by 39 publications

(23 citation statements)

References 7 publications

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“…As social perceptiveness is the greatest known predictor of collective intelligence, this suggests that swarms can perform better on a wide range of tasks and decisions. This interpretation is supported by other successful applications of human swarms to make surprisingly accurate decisions, from predicting sporting event outcomes to forecasting financial markets [28][29][30][31][32]. Future research can examine further the kinds of decisions and tasks that are best suited for human swarming.…”

Section: Discussionmentioning

confidence: 62%

“…This reveals a range of behavioral characteristics within the swarm population and weights their contributions accordingly, from entrenched participants to flexible participants to fickle participants. Already, human swarms using this platform have significantly increased the predictive accuracy of groups across a variety of tasks, from betting on sporting events to forecasting financial markets [28][29][30][31][32]. Successful swarms have included as low as three to over 40 participants.…”

Section: Figure 1 a Human Swarm Choosing Between Options In Real-timementioning

confidence: 99%

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Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Askay

Metcalf

Rosenberg

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Swarm Intelligence is natural phenomenon that enables social animals to make group decisions in real-time systems. This process has been deeply studied in fish schools, bird flocks, and bee swarms, where collective intelligence has been observed to emerge. The present paper describes swarm.ai-a collaborative technology that enables swarms of humans to collectively converge upon a decision as a real-time system. Then we present the results of a study investigating if groups working as "human swarms" can amplify their social perceptiveness, a key predictor of collective intelligence. Results showed that groups reduced their social perceptiveness errors by more than half when operating as a swarm. A statistical analysis revealed with 99.9% confidence that groups working as swarms had significantly higher social perceptiveness than either individuals working alone or through plurality vote.

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Section: Discussionmentioning

confidence: 62%

Section: Figure 1 a Human Swarm Choosing Between Options In Real-timementioning

confidence: 99%

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Askay

Metcalf

Rosenberg

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

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“…This approach could even be extended to compare the performance of mechanisms underlying different forms of group-level intelligence. For example, Rosenberg et al (Rosenberg et al 2016) examined the emergence of group-level intelligence in a sports prediction context. They found that a method of combining individual predictions that simulates processes involved in swarm intelligence performed better than the simple averaging of group member predictions (i.e., comparable to traditional studies of wisdom of the crowd).…”

Section: Principles Of Group-level Intelligencementioning

confidence: 99%

How Approaches to Animal Swarm Intelligence Can Improve the Study of Collective Intelligence in Human Teams

2020

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Researchers of team behavior have long been interested in the essential components of effective teamwork. Much existing research focuses on examining correlations between team member traits, team processes, and team outcomes, such as collective intelligence or team performance. However, these approaches are insufficient for providing insight into the dynamic, causal mechanisms through which the components of teamwork interact with one another and impact the emergence of team outcomes. Advances in the field of animal behavior have enabled a precise understanding of the behavioral mechanisms that enable groups to perform feats that surpass the capabilities of the individuals that comprise them. In this manuscript, we highlight how studies of animal swarm intelligence can inform research on collective intelligence in human teams. By improving the ability to obtain precise, time-varying measurements of team behaviors and outcomes and building upon approaches used in studies of swarm intelligence to analyze and model individual and group-level behaviors, researchers can gain insight into the mechanisms underlying the emergence of collective intelligence. Such understanding could inspire targeted interventions to improve team effectiveness and support the development of a comparative framework of group-level intelligence in animal and human groups.

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“…Recently, it has been proposed that the use of structures similar to natural swarms can correct some of these problems [39]. Indeed, by allowing users to participate in decision making processes in real time with a feedback about what the rest is doing, in some sort of human swarm, it is possible to explore more efficiently the decision space and reach more accurate predictions than with simple majority voting [40]. Admittedly, it has recently been suggested that online crowds might be better described as swarms as something in-between crowds and networks [41].…”

Section: Description Of the Eventmentioning

confidence: 99%

The dynamics of collective social behavior in a crowd controlled game

Aleta

Moreno

2019

EPJ Data Sci.

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Despite many efforts, the behavior of a crowd is not fully understood. The advent of modern communication media has made it an even more challenging problem, as crowd dynamics could be driven by both human-to-human and human-technology interactions. Here, we study the dynamics of a crowd controlled game (Twitch Plays Pokémon), in which nearly a million players participated during more than two weeks. We dissect the temporal evolution of the system dynamics along the two distinct phases that characterized the game. We find that players who do not follow the crowd average behavior are key to succeed in the game. The latter finding can be well explained by an n-th order Markov model that reproduces the observed behavior. Secondly, we analyze a phase of the game in which players were able to decide between two different modes of playing, mimicking a voting system. Our results suggest that under some conditions, the collective dynamics can be better regarded as a swarm-like behavior instead of a crowd. Finally, we discuss our findings in the light of the social identity theory, which appears to describe well the observed dynamics.

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Crowds vs swarms, a comparison of intelligence

Cited by 39 publications

References 7 publications

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

How Approaches to Animal Swarm Intelligence Can Improve the Study of Collective Intelligence in Human Teams

The dynamics of collective social behavior in a crowd controlled game

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