  How do we design and deploy crowdsourced prediction platforms for real-world
applications where risk is an important dimension of prediction performance? To
answer this question, we conducted a large online Wisdom of the Crowd study
where participants predicted the prices of real financial assets (e.g. S&P
500). We observe a Pareto frontier between accuracy of prediction and risk, and
find that this trade-off is mediated by social learning i.e. as social learning
is increasingly leveraged, it leads to lower accuracy but also lower risk. We
also observe that social learning leads to superior accuracy during one of our
rounds that occurred during the high market uncertainty of the Brexit vote. Our
results have implications for the design of crowdsourced prediction platforms:
for example, they suggest that the performance of the crowd should be more
comprehensively characterized by using both accuracy and risk (as is standard
in financial and statistical forecasting), in contrast to prior work where risk
of prediction has been overlooked.
