Collective intelligence: aggregation of information from neighbors in a guessing game

TL;DR

This study investigates how individuals in a networked environment collectively solve a guessing problem, revealing that their decision-making follows Bayesian patterns and that network structure has limited impact on overall accuracy.

Contribution

It introduces an online experiment analyzing collective problem-solving, showing Bayesian decision strategies and linking individual behavior to collective outcomes.

Findings

No significant difference in performance between lattice and random networks.

Individuals follow Bayesian decision strategies in aggregating information.

Collective performance is suboptimal compared to ideal decision models.

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.