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Collective Intelligence: Aggregation of Information from Neighbors in a Guessing Game.

Pérez T, Zamora J, Eguíluz VM - PLoS ONE (2016)

Bottom Line: Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited.In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective.Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), E07122 Palma de Mallorca, Spain.

ABSTRACT
Complex systems show the capacity to aggregate information and to display coordinated activity. In the case of social systems the interaction of different individuals leads to the emergence of norms, trends in political positions, opinions, cultural traits, and even scientific progress. Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited. Here, we conducted an online experiment to investigate the performance of a collective when solving a guessing problem in which each actor is endowed with partial information and placed as the nodes of an interaction network. We measure the performance of the collective in terms of the temporal evolution of the accuracy, finding no statistical difference in the performance for two classes of networks, regular lattices and random networks. We also determine that a Bayesian description captures the behavior pattern the individuals follow in aggregating information from neighbors to make decisions. In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective. Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena.

No MeSH data available.


Related in: MedlinePlus

Illustration of the network configurations.Focal player (red node) is connected to k = 4 neighbors (blue nodes) having access to their proposals, and, at the same time, she shares her proposals with them. The remaining nodes and network connections are depicted by the green nodes and the gray links, respectively. Random network is represented on the left and regular network on the right.
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pone.0153586.g001: Illustration of the network configurations.Focal player (red node) is connected to k = 4 neighbors (blue nodes) having access to their proposals, and, at the same time, she shares her proposals with them. The remaining nodes and network connections are depicted by the green nodes and the gray links, respectively. Random network is represented on the left and regular network on the right.

Mentions: We developed a social experiment consisting of an online game in which players have to guess a sequence of colors using information of an incomplete sequence provided initially to them and from the proposals of their neighbors. The experiment was structured in sessions each consisting of a set of N individuals assigned randomly to the nodes of a network as sketched in Fig 1. The target color code was composed of a sequence of li positions (i = 1, …, 10) colored with color c(i) from the available set (red, blue, and yellow). We defined xj(li, t) as the color chosen by player j for the position li at time t.


Collective Intelligence: Aggregation of Information from Neighbors in a Guessing Game.

Pérez T, Zamora J, Eguíluz VM - PLoS ONE (2016)

Illustration of the network configurations.Focal player (red node) is connected to k = 4 neighbors (blue nodes) having access to their proposals, and, at the same time, she shares her proposals with them. The remaining nodes and network connections are depicted by the green nodes and the gray links, respectively. Random network is represented on the left and regular network on the right.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836688&req=5

pone.0153586.g001: Illustration of the network configurations.Focal player (red node) is connected to k = 4 neighbors (blue nodes) having access to their proposals, and, at the same time, she shares her proposals with them. The remaining nodes and network connections are depicted by the green nodes and the gray links, respectively. Random network is represented on the left and regular network on the right.
Mentions: We developed a social experiment consisting of an online game in which players have to guess a sequence of colors using information of an incomplete sequence provided initially to them and from the proposals of their neighbors. The experiment was structured in sessions each consisting of a set of N individuals assigned randomly to the nodes of a network as sketched in Fig 1. The target color code was composed of a sequence of li positions (i = 1, …, 10) colored with color c(i) from the available set (red, blue, and yellow). We defined xj(li, t) as the color chosen by player j for the position li at time t.

Bottom Line: Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited.In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective.Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), E07122 Palma de Mallorca, Spain.

ABSTRACT
Complex systems show the capacity to aggregate information and to display coordinated activity. In the case of social systems the interaction of different individuals leads to the emergence of norms, trends in political positions, opinions, cultural traits, and even scientific progress. Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited. Here, we conducted an online experiment to investigate the performance of a collective when solving a guessing problem in which each actor is endowed with partial information and placed as the nodes of an interaction network. We measure the performance of the collective in terms of the temporal evolution of the accuracy, finding no statistical difference in the performance for two classes of networks, regular lattices and random networks. We also determine that a Bayesian description captures the behavior pattern the individuals follow in aggregating information from neighbors to make decisions. In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective. Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena.

No MeSH data available.


Related in: MedlinePlus