Many collaborative online projects such as Wikipedia and OpenStreetMap organize collaboration among their contributors sequentially. In sequential collaboration, one contributor creates an entry which is then consecutively encountered by other contributors who decide whether to adjust or maintain the presented entry. For numeric and geographical judgments, sequential collaboration yields improved judgments over the course of a sequential chain and results in accurate final estimates. We hypothesize that these benefits emerge since contributors adjust entries according to their expertise, implying that judgments of experts have a larger impact compared with those of novices. In three preregistered studies, we measured and manipulated expertise to investigate whether expertise leads to higher change probabilities and larger improvements in judgment accuracy. Moreover, we tested whether expertise results in an increase in accuracy over the course of a sequential chain. As expected, experts adjusted entries more frequently, made larger improvements, and contributed more to the final estimates of sequential chains. Overall, our findings suggest that the high accuracy of sequential collaboration is due to an implicit weighting of judgments by expertise.

Environmental uncertainty is at the core of much of human activity, ranging fromdaily decisions by individuals to long-term policy planning by governments. Yet,there is little quantitative evidence on the ability of non-expert individualsor populations to forecast climate-related events. Here we report on data from a90-year old prediction game on a climate related event in Alaska: the Nenana IceClassic (NIC). Participants in this contest guess to the nearest minute when theice covering the Tanana River will break, signaling the start of spring.Previous research indicates a strong correlation between the ice breakup datesand regional weather conditions. We study betting decisions between 1955 and2009. We find the betting distribution closely predicts the outcome of thecontest. We also find a significant correlation between regional temperatures aswell as past ice breakups and betting behavior, suggesting that participantsincorporate both climate and historical information into theirdecision-making.

We investigated the extent to which the human capacity for recognition helps to forecast political elections: We compared naïve recognition-based election forecasts computed from convenience samples of citizens’ recognition of party names to (i) standard polling forecasts computed from representative samples of citizens’ voting intentions, and to (ii) simple—and typically very accurate—wisdom-of-crowds-forecasts computed from the same convenience samples of citizens’ aggregated hunches about election results. Results from four major German elections show that mere recognition of party names forecast the parties’ electoral success fairly well. Recognition-based forecasts were most competitive with the other models when forecasting the smaller parties’ success and for small sample sizes. However, wisdom-of-crowds-forecasts outperformed recognition-based forecasts in most cases. It seems that wisdom-of-crowds-forecasts are able to draw on the benefits of recognition while at the same time avoiding its downsides, such as lack of discrimination among very famous parties or recognition caused by factors unrelated to electoral success. Yet it seems that a simple extension of the recognition-based forecasts—asking people what proportion of the population would recognize a party instead of whether they themselves recognize it—is also able to eliminate these downsides.

We apply the principles of the “Wisdom of Crowds (WoC)” to improve the calibration of interval estimates. Previous research has documented the significant impact of the WoC on the accuracy of point estimates but only a few studies have examined its effectiveness in aggregating interval estimates. We demonstrate that collective probability intervals obtained by several heuristics can reduce the typical overconfidence of the individual estimates. We re-analyzed data from Glaser, Langer and Weber (2013) and from Soll and Klayman (2004) and applied four heuristics Averaging, Median, Enveloping, Probability averaging-suggested by Gaba, Tsetlin and Winkler (2014) and new heuristics, Averaging with trimming and Quartiles. We used the hit rate and the Mean Squared Error (MSE) to evaluate the quality of the methods. All methods reduced miscalibration to some degree, and Quartiles was the most beneficial securing accuracy and informativeness.

The shell of the Manus green tree snail Papustyla pulcherrima is renowned for its beauty and is subject to international protection under CITES, having been harvested intensively in the past. To determine its threat status, and whether further conservation action is justified, an inexpensive Wisdom of Crowds approach was used to estimate the change in relative density of the snail between 1998 and 2013. Local men and women were approached around the main market on Manus Island, Papua New Guinea, and asked to map the relative abundance of the snail on an ordinal scale, based on their personal observations in 2013 and 1998 (a year of cultural significance). The spatial abundance data from 400 surveys were analysed using an information-theoretic approach. A suite of cumulative link models incorporating geographical factors was used to determine the magnitude of the change and to investigate possible biological influences underpinning the reported pattern. High abundance of the snail was associated with intact forested areas, high elevation and low population density. A slow decline was evident, with the median percentage of map cells where the snail was categorized as plentiful decreasing by c. 20% between the 2 years. On this basis a categorization of Near Threatened was advocated for the species. Although it is arguable that Wisdom of Crowds methods cannot be substituted for in situ quantification, the approach appears to have utility as a preliminary assessment for further conservation expenditure, and as a tool for determining threat status.