Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls

Abstract

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.