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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

Activity of the players during the games.Main figure: complementary cumulative distribution function (ccdf) of the inter-proposal times (bin size: 1 s). Dashed line shows an exponential fit (MLE) to the data f(t) = exp(−bt) with b = 0.036 s−1. Inset: Temporal distribution of the number of proposals across the games aggregated in bins of 5 s. Dotted line shows the averaged activity of 10 proposals every 5 seconds.
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pone.0153586.g003: Activity of the players during the games.Main figure: complementary cumulative distribution function (ccdf) of the inter-proposal times (bin size: 1 s). Dashed line shows an exponential fit (MLE) to the data f(t) = exp(−bt) with b = 0.036 s−1. Inset: Temporal distribution of the number of proposals across the games aggregated in bins of 5 s. Dotted line shows the averaged activity of 10 proposals every 5 seconds.

Mentions: The activity during the game is measured by the number of complete color codes submitted by the players. Fig 3 shows how the activity of the players during the games fluctuates around an average activity of 10 proposals every 5 seconds. The complementary cumulative distribution of inter-proposal time, that is, the time between two consecutive proposals for the same individual, reveals an exponential decay of the individuals’ activity with a characteristic time of 28 ± 3 s after Maximum Likelihood Estimation (MLE) [28].


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

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

Activity of the players during the games.Main figure: complementary cumulative distribution function (ccdf) of the inter-proposal times (bin size: 1 s). Dashed line shows an exponential fit (MLE) to the data f(t) = exp(−bt) with b = 0.036 s−1. Inset: Temporal distribution of the number of proposals across the games aggregated in bins of 5 s. Dotted line shows the averaged activity of 10 proposals every 5 seconds.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153586.g003: Activity of the players during the games.Main figure: complementary cumulative distribution function (ccdf) of the inter-proposal times (bin size: 1 s). Dashed line shows an exponential fit (MLE) to the data f(t) = exp(−bt) with b = 0.036 s−1. Inset: Temporal distribution of the number of proposals across the games aggregated in bins of 5 s. Dotted line shows the averaged activity of 10 proposals every 5 seconds.
Mentions: The activity during the game is measured by the number of complete color codes submitted by the players. Fig 3 shows how the activity of the players during the games fluctuates around an average activity of 10 proposals every 5 seconds. The complementary cumulative distribution of inter-proposal time, that is, the time between two consecutive proposals for the same individual, reveals an exponential decay of the individuals’ activity with a characteristic time of 28 ± 3 s after Maximum Likelihood Estimation (MLE) [28].

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