The quality of decisions depends on the accuracy of estimates of relevant quantities. According to the wisdom of crowds principle, accurate estimates can be obtained by combining the judgements of different individuals1,2. This principle has been successfully applied to improve, for example, economic forecasts3,4,5, medical judgements6,7,8,9 and meteorological predictions10,11,12,13. Unfortunately, there are many situations in which it is infeasible to collect judgements of others. Recent research proposes that a similar principle applies to repeated judgements from the same person14. This paper tests this promising approach on a large scale in a real-world context. Using proprietary data comprising 1.2 million observations from three incentivized guessing competitions, we find that within-person aggregation indeed improves accuracy and that the method works better when there is a time delay between subsequent judgements. However, the benefit pales against that of between-person aggregation: the average of a large number of judgements from the same person is barely better than the average of two judgements from different people.
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We thank Holland Casino for providing the data, and A. Baillon, S. Herzog, A. Lucas, L. Molleman, A. Opschoor, R. Potter van Loon, V. Spinu, and L. Wolk for their constructive and valuable comments. The paper has benefited from discussions with seminar participants at the Max Planck Institute for Human Development, Carnegie Mellon University and the University of Nottingham, and with participants of the 2015 NIBS workshop, SPUDM 2015 Budapest, WESSI 2016 Abu Dhabi, IMEBESS 2016 Rome, TIBER 2016 Tilburg and BFWG 2017 London. We gratefully acknowledge support from the Netherlands Organisation for Scientific Research (NWO) and from the Economic and Social Research Council via the Network for Integrated Behavioural Sciences (ES/K002201/1). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Nature Human Behaviour (2018)