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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

Interactive interface of the game.(A) Example of the partial target code shown initially to the player displaying positions 3, 5 and 10 with the correct color, while the other positions are empty. Green boxes with question marks are the remaining positions of the code to be guessed by the player (green color is not used in the color code sequence). (B) Interface for the first guess. When the mouse cursor is placed over a question mark green box, a list of available colors present in the code is shown. (C) Player interface during the game. At the top of the screen the proposals from neighboring players (Team) are shown. When a member of the Team updates her code, a flashing ‘new code’ bubble is shown on the icon of the updater.
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pone.0153586.g002: Interactive interface of the game.(A) Example of the partial target code shown initially to the player displaying positions 3, 5 and 10 with the correct color, while the other positions are empty. Green boxes with question marks are the remaining positions of the code to be guessed by the player (green color is not used in the color code sequence). (B) Interface for the first guess. When the mouse cursor is placed over a question mark green box, a list of available colors present in the code is shown. (C) Player interface during the game. At the top of the screen the proposals from neighboring players (Team) are shown. When a member of the Team updates her code, a flashing ‘new code’ bubble is shown on the icon of the updater.

Mentions: In each game, players had to guess a color code of ten positions. Each position could be colored with one of the following colors: red, blue, or yellow. Participants had 225 seconds to guess the code after initial access to a partial sequence of the code. An example as well as the proposed codes of the neighboring players is shown in Fig 2A.


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

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

Interactive interface of the game.(A) Example of the partial target code shown initially to the player displaying positions 3, 5 and 10 with the correct color, while the other positions are empty. Green boxes with question marks are the remaining positions of the code to be guessed by the player (green color is not used in the color code sequence). (B) Interface for the first guess. When the mouse cursor is placed over a question mark green box, a list of available colors present in the code is shown. (C) Player interface during the game. At the top of the screen the proposals from neighboring players (Team) are shown. When a member of the Team updates her code, a flashing ‘new code’ bubble is shown on the icon of the updater.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153586.g002: Interactive interface of the game.(A) Example of the partial target code shown initially to the player displaying positions 3, 5 and 10 with the correct color, while the other positions are empty. Green boxes with question marks are the remaining positions of the code to be guessed by the player (green color is not used in the color code sequence). (B) Interface for the first guess. When the mouse cursor is placed over a question mark green box, a list of available colors present in the code is shown. (C) Player interface during the game. At the top of the screen the proposals from neighboring players (Team) are shown. When a member of the Team updates her code, a flashing ‘new code’ bubble is shown on the icon of the updater.
Mentions: In each game, players had to guess a color code of ten positions. Each position could be colored with one of the following colors: red, blue, or yellow. Participants had 225 seconds to guess the code after initial access to a partial sequence of the code. An example as well as the proposed codes of the neighboring players is shown in Fig 2A.

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