Collective Intelligence: Aggregation of Information from Neighbors in a Guessing Game.
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 |
Related In:
Results -
Collection
License getmorefigures.php?uid=PMC4836688&req=5
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). |
View Article: PubMed Central - PubMed
Affiliation: Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), E07122 Palma de Mallorca, Spain.
No MeSH data available.