  This study investigates the practice of experts aggregating forecasts before
informing a decision-maker. The significance of this subject extends to various
contexts where experts inform their assessments to a decision-maker following
discussions with peers. My findings show that, irrespective of the information
structure, aggregation rules introduce no bias to decision-making in expected
terms. Nevertheless, the concern revolves around variance. In situations where
experts are equally precise, and pair-wise correlation of forecasts is the same
across all pairs of experts, the network structure plays a pivotal role in
decision-making variance. For classical structures, I show that star networks
exhibit the highest variance, contrasting with $d$-regular networks that
achieve zero variance, emphasizing their efficiency. Additionally, by employing
the Poisson random graph model under the assumptions of a large network size
and a small connection probability, the results indicate that both the expected
Network Bias and its variance converge to zero as the network size becomes
sufficiently large. These insights enhance the understanding of decision-making
under different information, network structures and aggregation rules. They
enrich the literature on combining forecasts by exploring the effects of prior
network communication on decision-making.
