  In many social systems, groups of individuals can find remarkably efficient
solutions to complex cognitive problems, sometimes even outperforming a single
expert. The success of the group, however, crucially depends on how the
judgments of the group members are aggregated to produce the collective answer.
A large variety of such aggregation methods have been described in the
literature, such as averaging the independent judgments, relying on the
majority or setting up a group discussion. In the present work, we introduce a
novel approach for aggregating judgments - the transmission chain - which has
not yet been consistently evaluated in the context of collective intelligence.
In a transmission chain, all group members have access to a unique collective
solution and can improve it sequentially. Over repeated improvements, the
collective solution that emerges reflects the judgments of every group members.
We address the question of whether such a transmission chain can foster
collective intelligence for binary-choice problems. In a series of numerical
simulations, we explore the impact of various factors on the performance of the
transmission chain, such as the group size, the model parameters, and the
structure of the population. The performance of this method is compared to
those of the majority rule and the confidence-weighted majority. Finally, we
rely on two existing datasets of individuals performing a series of binary
decisions to evaluate the expected performances of the three methods
empirically. We find that the parameter space where the transmission chain has
the best performance rarely appears in real datasets. We conclude that the
transmission chain is best suited for other types of problems, such as those
that have cumulative properties.
