Aggregation mechanisms for crowd predictions

When the information of many individuals is pooled, the resulting aggregate often is a good predictor of unknown quantities or facts. This aggregate predictor frequently outperforms the forecasts of experts or even the best individual forecast included in the aggregation process ("wisdom of crowds"). However, an appropriate aggregation mechanism is considered crucial to reaping the benefits of a "wise crowd". Of the many possible ways to aggregate individual forecasts, we compare (uncensored and censored) arithmetic and geometric mean and median, continuous double auction market prices and sealed bid-offer call market prices in a controlled experiment. We use an asymmetric information structure, where participants know different sub-sets of the total information needed to exactly calculate the asset value to be estimated. We find that prices from continuous double auction markets clearly outperform all alternative approaches for aggregating dispersed information and that information lets only the best-informed participants generate excess returns.

The effectiveness of imperfect weighting in advice taking

We investigate decision-making in the Judge-Advisor-System where one person, the “judge”, wants to estimate the number of a certain entity and is given advice by another person. The question is how to combine the judge’s initial estimate and that of the advisor in order to get the optimal expected outcome. A previous approach compared two frequently applied strategies, taking the average or choosing the better estimate. In most situations, averaging produced the better estimates. However, this approach neglected a third strategy that judges frequently use, namely a weighted mean of the judges’ initial estimate and the advice. We compare the performance of averaging and choosing to weighting in a theoretical analysis. If the judge can, without error, detect ability differences between judge and advisor, a straight-forward calculation shows that weighting outperforms both of these strategies. More interestingly, after introducing errors in the perception of the ability differences, we show that such imperfect weighting may or may not be the optimal strategy. The relative performance of imperfect weighting compared to averaging or choosing depends on the size of the actual ability differences as well as the magnitude of the error. However, for a sizeable range of ability differences and errors, weighting is preferable to averaging and more so to choosing. Our analysis expands previous research by showing that weighting, even when imperfect, is an appropriate advice taking strategy and under which circumstances judges benefit most from applying it.

Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography

BACKGROUND

Methods for the calculation and application of quantitative electromyographic (EMG) statistics for the characterization of EMG data detected from forearm muscles of individuals with and without pain associated with repetitive strain injury are presented.

METHODS

A classification procedure using a multi-stage application of Bayesian inference is presented that characterizes a set of motor unit potentials acquired using needle electromyography. The utility of this technique in characterizing EMG data obtained from both normal individuals and those presenting with symptoms of "non-specific arm pain" is explored and validated. The efficacy of the Bayesian technique is compared with simple voting methods.

RESULTS

The aggregate Bayesian classifier presented is found to perform with accuracy equivalent to that of majority voting on the test data, with an overall accuracy greater than 0.85. Theoretical foundations of the technique are discussed, and are related to the observations found.

CONCLUSIONS

Aggregation of motor unit potential conditional probability distributions estimated using quantitative electromyographic analysis, may be successfully used to perform electrodiagnostic characterization of "non-specific arm pain." It is expected that these techniques will also be able to be applied to other types of electrodiagnostic data.