Social Learning and the Accuracy-Risk Trade-off in the Wisdom of the Crowd

TL;DR

This study investigates how social learning influences the accuracy and risk trade-off in crowdsourced financial predictions, revealing a Pareto frontier and implications for platform design.

Contribution

It demonstrates the existence of a Pareto trade-off between accuracy and risk mediated by social learning in crowdsourced predictions, highlighting the importance of considering both metrics.

Findings

Social learning reduces both prediction accuracy and risk.

A Pareto frontier exists between accuracy and risk in crowdsourced predictions.

Social learning improved accuracy during high market uncertainty, such as Brexit.

Abstract

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.