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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

Group performance: temporal evolution of the accuracy.(A) Average number of correct positions for aggregated data of the games with the same interaction topology (solid thick lines, red random networks; blue: regular lattice). The number of correct positions of the individual players is also shown (dimmed symbols). For visualization purposes a slight vertical displacement has been applied. (B) Ratio between the probabilities of correct positions at distances d1 = 1 and d2 = 2 from the closest source. Inset: Temporal evolution of the fraction of correct positions for distances d1 and d2 to the closest source. In both panels, results are aggregated over all the sessions of the experiments.
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pone.0153586.g004: Group performance: temporal evolution of the accuracy.(A) Average number of correct positions for aggregated data of the games with the same interaction topology (solid thick lines, red random networks; blue: regular lattice). The number of correct positions of the individual players is also shown (dimmed symbols). For visualization purposes a slight vertical displacement has been applied. (B) Ratio between the probabilities of correct positions at distances d1 = 1 and d2 = 2 from the closest source. Inset: Temporal evolution of the fraction of correct positions for distances d1 and d2 to the closest source. In both panels, results are aggregated over all the sessions of the experiments.

Mentions: In all the games, as the time increases, the participants tend toward the proposed color code. The hamming distance, defined as the number of differing positions between two color codes, averaged over all pairs of players at the end of each game lies in the range (2.1, 3.3). The performance of the collective is measured by the temporal evolution of the accuracy averaged over the total number of players, that is, , where δa, b is the Kronecker’s delta. Fig 4A shows p(t) as well as the individual distributions for the two networks considered in the experiment. The average performance of the group at the end of the games with the same interaction network is p = 8.3. The unpaired Mann-Whitney U test and the paired Wilcoxon signed rank test, computed for the distributions of p(t) values at each time step of 1 s indicate the absence of any statistical difference between the distributions of p(t) in the two networks (average p-values > 0.4, see S1 Fig).


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

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

Group performance: temporal evolution of the accuracy.(A) Average number of correct positions for aggregated data of the games with the same interaction topology (solid thick lines, red random networks; blue: regular lattice). The number of correct positions of the individual players is also shown (dimmed symbols). For visualization purposes a slight vertical displacement has been applied. (B) Ratio between the probabilities of correct positions at distances d1 = 1 and d2 = 2 from the closest source. Inset: Temporal evolution of the fraction of correct positions for distances d1 and d2 to the closest source. In both panels, results are aggregated over all the sessions of the experiments.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153586.g004: Group performance: temporal evolution of the accuracy.(A) Average number of correct positions for aggregated data of the games with the same interaction topology (solid thick lines, red random networks; blue: regular lattice). The number of correct positions of the individual players is also shown (dimmed symbols). For visualization purposes a slight vertical displacement has been applied. (B) Ratio between the probabilities of correct positions at distances d1 = 1 and d2 = 2 from the closest source. Inset: Temporal evolution of the fraction of correct positions for distances d1 and d2 to the closest source. In both panels, results are aggregated over all the sessions of the experiments.
Mentions: In all the games, as the time increases, the participants tend toward the proposed color code. The hamming distance, defined as the number of differing positions between two color codes, averaged over all pairs of players at the end of each game lies in the range (2.1, 3.3). The performance of the collective is measured by the temporal evolution of the accuracy averaged over the total number of players, that is, , where δa, b is the Kronecker’s delta. Fig 4A shows p(t) as well as the individual distributions for the two networks considered in the experiment. The average performance of the group at the end of the games with the same interaction network is p = 8.3. The unpaired Mann-Whitney U test and the paired Wilcoxon signed rank test, computed for the distributions of p(t) values at each time step of 1 s indicate the absence of any statistical difference between the distributions of p(t) in the two networks (average p-values > 0.4, see S1 Fig).

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