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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

Data fitting.(A) Root-mean-square error between the probabilities computed from experimental data (aggregated over all the experiments) and Eq (4). Results for each color are represented by the corresponding colored lines. (B) Color-coded root-mean-square error between the probabilities computed from experimental data (aggregated over all the colors across experiments) and Eq (5).
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pone.0153586.g006: Data fitting.(A) Root-mean-square error between the probabilities computed from experimental data (aggregated over all the experiments) and Eq (4). Results for each color are represented by the corresponding colored lines. (B) Color-coded root-mean-square error between the probabilities computed from experimental data (aggregated over all the colors across experiments) and Eq (5).

Mentions: We can estimate the value of the parameter s from the experimental data by computing the root-mean-square error (RMSE) of the difference between the probabilities extracted from the experimental data and the probabilities computed using Eq (4). The RMSE, shown in Fig 6A, exhibits a minimum for indicating that the probability that the signal is of the same value than the current state is twice the probability that the signal has a different value. This result is in agreement with the experimental findings shown in Fig 5B.


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

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

Data fitting.(A) Root-mean-square error between the probabilities computed from experimental data (aggregated over all the experiments) and Eq (4). Results for each color are represented by the corresponding colored lines. (B) Color-coded root-mean-square error between the probabilities computed from experimental data (aggregated over all the colors across experiments) and Eq (5).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153586.g006: Data fitting.(A) Root-mean-square error between the probabilities computed from experimental data (aggregated over all the experiments) and Eq (4). Results for each color are represented by the corresponding colored lines. (B) Color-coded root-mean-square error between the probabilities computed from experimental data (aggregated over all the colors across experiments) and Eq (5).
Mentions: We can estimate the value of the parameter s from the experimental data by computing the root-mean-square error (RMSE) of the difference between the probabilities extracted from the experimental data and the probabilities computed using Eq (4). The RMSE, shown in Fig 6A, exhibits a minimum for indicating that the probability that the signal is of the same value than the current state is twice the probability that the signal has a different value. This result is in agreement with the experimental findings shown in Fig 5B.

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