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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

Confronting different models via simulations.Temporal evolution of p(t) for different simulation models in two types of interaction networks. Probabilities correspond to simulations using the experimentally determined probabilities. Experimental data is represented by the black line and it is aggregated over games with the same interaction topology. Simulations are averaged over 50 independent realizations.
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pone.0153586.g007: Confronting different models via simulations.Temporal evolution of p(t) for different simulation models in two types of interaction networks. Probabilities correspond to simulations using the experimentally determined probabilities. Experimental data is represented by the black line and it is aggregated over games with the same interaction topology. Simulations are averaged over 50 independent realizations.

Mentions: We have performed simulations confronting various models with the experimental data, where agents change their state according to the experimentally determined transition probabilities. We have also considered other models with different updating rules. In the Majority rule, agents pick the color of the majority of their neighborhood and ties are broken by random elections among the tied options. In the Voter model, agents pick a color at random from the ones present in the neighborhood. In the Random model however, agents pick a color at random not necessarily present in the neighborhood. The simulations mimic the experimental conditions of the games by using the same number of agents, the same initial conditions, and the same temporal sequence of updates. Fig 7 shows the time evolution of the performance p(t) for the different models and the experimental data, in both cases aggregated over games with the same interaction topology. In both networks, Majority rule outperformed all other strategies, including the one used by humans. In the random network, Voter performs at the end of the games as well as human strategies. In the regular network, Voter performs slightly worse than Majority, and slightly better than human strategies. For both networks, choosing a color at random independently of the neighbors’ proposals is the worst strategy. Simulations also corroborate that, for the human strategy (Probabilities), there are no differences in the performance of the different networks considered.


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

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

Confronting different models via simulations.Temporal evolution of p(t) for different simulation models in two types of interaction networks. Probabilities correspond to simulations using the experimentally determined probabilities. Experimental data is represented by the black line and it is aggregated over games with the same interaction topology. Simulations are averaged over 50 independent realizations.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0153586.g007: Confronting different models via simulations.Temporal evolution of p(t) for different simulation models in two types of interaction networks. Probabilities correspond to simulations using the experimentally determined probabilities. Experimental data is represented by the black line and it is aggregated over games with the same interaction topology. Simulations are averaged over 50 independent realizations.
Mentions: We have performed simulations confronting various models with the experimental data, where agents change their state according to the experimentally determined transition probabilities. We have also considered other models with different updating rules. In the Majority rule, agents pick the color of the majority of their neighborhood and ties are broken by random elections among the tied options. In the Voter model, agents pick a color at random from the ones present in the neighborhood. In the Random model however, agents pick a color at random not necessarily present in the neighborhood. The simulations mimic the experimental conditions of the games by using the same number of agents, the same initial conditions, and the same temporal sequence of updates. Fig 7 shows the time evolution of the performance p(t) for the different models and the experimental data, in both cases aggregated over games with the same interaction topology. In both networks, Majority rule outperformed all other strategies, including the one used by humans. In the random network, Voter performs at the end of the games as well as human strategies. In the regular network, Voter performs slightly worse than Majority, and slightly better than human strategies. For both networks, choosing a color at random independently of the neighbors’ proposals is the worst strategy. Simulations also corroborate that, for the human strategy (Probabilities), there are no differences in the performance of the different networks considered.

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