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 individuals. This principle has been successfully applied to improve, for example, economic forecasts, medical judgements and meteorological predictions.
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 person.
The study finds that the effectiveness of within-person aggregation is considerably lower than 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. The efficacy difference is a consequence of the existence of individual-level systematic errors (idiosyncratic bias). The effect of these errors can be eliminated by combining estimates from multiple people, not by combining multiple estimates from a single person.
In the context of a guessing competitions, all individuals were exposed to the same (visual) information about the container and the objects in it, and the sources of variation in idiosyncratic bias were limited to differences in individuals’ comprehension of the task, visual perception, and geometric skills.
In many other real world contexts, additional sources of idiosyncratic bias exist, which can be expected to lower the comparative benefit of within-person aggregation even more.
Within-person aggregation is potentially useful in situations where only one individual can make sufficiently informed estimates.
This may be the case, for example, in strictly personal matters and under extreme degrees of specialization. Because of the relatively limited accuracy gains from within-person aggregation, between person aggregation should be preferred whenever practicable.
An individual can produce the wisdom of the crowds, called ‘the wisdom of the inner crowd’. There is room for improvements in terms of efficacy and convenience. A more efficient method with low cognitive cost, based on findings from cognitive and social psychology might help.
The procedure is to ask participants to give two answers to the same question:
– first, their own estimate and,
– second, their estimate of public opinion.
Experiments using this method showed that the averages of the two estimates were more accurate than the participants’ first estimates. That is, the wisdom of the inner crowd emerged.
In addition, the method could be superior to other methods in terms of efficacy and convenience. Moreover, the conditions where our method worked better were identified: the analysis showed that our method worked better when participants had high confidence in their own estimates. The accuracy of the second estimate was high when the confidence in first estimate was high.
A limitations of using the wisdom of the inner crowd is people’s tendency to fall into overconfidence. Participants tended to fall into overconfidence: specifically, they became more confident about the third or final estimate compared to the first estimate. However, accuracy did not increase as a whole. Assumed is that this is an inherent defect of methods that elicit the wisdom of the inner crowd.
In our daily lives, we must often predict the level of others’ satisfaction with something they have not experienced thus far.
How can such a prediction be accurate?
Existing studies indicate that, by referring to the extent to which people themselves have enjoyed something, they are able to predict others’ future satisfaction, to some extent.
In this study, we propose a method that can further improve such predictions.
This method is expected to allow individuals to exploit the ‘wisdom of the crowd’ within a person, in terms of taste.
Specifically, for a single target, participants in our study group produced two opinions from different perspectives:
– the degree to which they preferred something, and
– they estimated ‘public opinion’.
Utilising two behavioural studies and computer simulations, we confirmed the effectiveness of our method; specifically, blending the two opinions could enhance an individual’s prediction ability.
Subsequently, we mathematically analysed how effective our method is and identified several factors that influenced its efficiency.