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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

Transition Probabilities.(A) Probability P(X/nX) to change the unknown positions of the code from any color (R, B or Y) to a given color X (R, B or Y) as a function of nX, this is, the number of nearest neighbors already with color X. Results for each color are represented by the corresponding colored symbols. (B) Probabilities P(Xf/Xi; nXf) for the unknown positions of the code as a function of nXf. Open and solid symbols represent, for a given position, transitions between different colors () and between the same color (XX) respectively. Results for each color are represented by the corresponding colored symbols. Dashed lines represent fitting Eq (5) to the data (see Section Bayesian update for more details).
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pone.0153586.g005: Transition Probabilities.(A) Probability P(X/nX) to change the unknown positions of the code from any color (R, B or Y) to a given color X (R, B or Y) as a function of nX, this is, the number of nearest neighbors already with color X. Results for each color are represented by the corresponding colored symbols. (B) Probabilities P(Xf/Xi; nXf) for the unknown positions of the code as a function of nXf. Open and solid symbols represent, for a given position, transitions between different colors () and between the same color (XX) respectively. Results for each color are represented by the corresponding colored symbols. Dashed lines represent fitting Eq (5) to the data (see Section Bayesian update for more details).

Mentions: Given the sequence of proposals of one player and the color codes of the neighbors, we calculate the transition probabilities between different colors. We consider that each position is independent of each other, thus, we average the transition probabilities over all positions. From all the possible transitions between colors, we compute the conditional probability to change from any color to a given color X, P(X/nX) given nX neighbors in state X. This probability, shown in Fig 5A, indicates that colors are practically equivalent for the players. This probability also points that, for a given position, as the number of neighbors with the same color nX increases, the higher the probability that the player will switch to that color (or remain on it).


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

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

Transition Probabilities.(A) Probability P(X/nX) to change the unknown positions of the code from any color (R, B or Y) to a given color X (R, B or Y) as a function of nX, this is, the number of nearest neighbors already with color X. Results for each color are represented by the corresponding colored symbols. (B) Probabilities P(Xf/Xi; nXf) for the unknown positions of the code as a function of nXf. Open and solid symbols represent, for a given position, transitions between different colors () and between the same color (XX) respectively. Results for each color are represented by the corresponding colored symbols. Dashed lines represent fitting Eq (5) to the data (see Section Bayesian update for more details).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153586.g005: Transition Probabilities.(A) Probability P(X/nX) to change the unknown positions of the code from any color (R, B or Y) to a given color X (R, B or Y) as a function of nX, this is, the number of nearest neighbors already with color X. Results for each color are represented by the corresponding colored symbols. (B) Probabilities P(Xf/Xi; nXf) for the unknown positions of the code as a function of nXf. Open and solid symbols represent, for a given position, transitions between different colors () and between the same color (XX) respectively. Results for each color are represented by the corresponding colored symbols. Dashed lines represent fitting Eq (5) to the data (see Section Bayesian update for more details).
Mentions: Given the sequence of proposals of one player and the color codes of the neighbors, we calculate the transition probabilities between different colors. We consider that each position is independent of each other, thus, we average the transition probabilities over all positions. From all the possible transitions between colors, we compute the conditional probability to change from any color to a given color X, P(X/nX) given nX neighbors in state X. This probability, shown in Fig 5A, indicates that colors are practically equivalent for the players. This probability also points that, for a given position, as the number of neighbors with the same color nX increases, the higher the probability that the player will switch to that color (or remain on it).

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