Gaissmaier,W.
Marewski,J.N.

We investigated the extent to which the human capacity for recognition\nhelps to forecast political elections: We compared naive recognition-based\nelection forecasts computed from convenience samples of citizens'\nrecognition of party names to (i) standard polling forecasts computed\nfrom representative samples of citizens' voting intentions, and to\n(ii) simple--and typically very accurate--wisdom-of-crowds-forecasts\ncomputed from the same convenience samples of citizens' aggregated\nhunches about election results. Results from four major German elections\nshow that mere recognition of party names forecast the parties' electoral\nsuccess fairly well. Recognition-based forecasts were most competitive\nwith the other models when forecasting the smaller parties' success\nand for small sample sizes. However, wisdom-of-crowds-forecasts outperformed\nrecognition-based forecasts in most cases. It seems that wisdom-of-crowds-forecasts\nare able to draw on the benefits of recognition while at the same\ntime avoiding its downsides, such as lack of discrimination among\nvery famous parties or recognition caused by factors unrelated to\nelectoral success. Yet it seems that a simple extension of the recognition-based\nforecasts--asking people what proportion of the population would\nrecognize a party instead of whether they themselves recognize it--is\nalso able to eliminate these downsides. (PsycINFO Database Record\n(c) 2012 APA, all rights reserved)

Gaissmaier, W., & Marewski, J. N. (2011). Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls. Judgment and Decision Making, 6, 73-88. (Full text)

Publication year	2011
Document type:	Article
Publication status:	Published
External URL:	http://journal.sjdm.org/11/10608/jdm10608.pdf View
Categories:	ForecastingMemorySports
Keywords:	forecastingheuristicspolitical electionsrecognitionwisdom of crowds