Munk School of Global Affairs and Public Policy, University of Toronto, Canada.
Department of Psychology, Boston College, United States.
Cognition. 2019 May;186:95-107. doi: 10.1016/j.cognition.2019.02.002. Epub 2019 Feb 12.
In this research, we investigated voters' mathematical processing of election-related information before and after the 2012 and 2016 U.S. Presidential Elections. We presented voters with mental math problems based on fictional polling results, and asked participants who they intended to vote for and who they expected to win. We found that committed voters (in both 2012 and 2016) demonstrated wishful thinking, with inflated expectations that their preferred candidate would win. When performing mathematical operations on polling information, voters in 2012 and 2016 deflated support for the opponent. Underestimation of the opponent was found to be absent among the participants who did not expect their preferred candidate to win. Identical experiments conducted after the elections revealed that partisan mathematical biases largely disappeared in favor of estimates in alignment with reality. Results indicate that mathematical processing of political polling data is biased by people's voting intentions and wishful thinking, and, crucially, by their expectations about the likely or actual state of the world.
在这项研究中，我们调查了选民在 2012 年和 2016 年美国总统选举前后对与选举相关信息的数学处理。我们向选民展示了基于虚构民意调查结果的心理数学问题，并询问参与者他们打算投票给谁以及他们预计谁会获胜。我们发现，坚定的选民（无论是在 2012 年还是 2016 年）都表现出一厢情愿的想法，对他们喜欢的候选人获胜的期望过高。当对民意调查信息进行数学运算时，2012 年和 2016 年的选民对对手的支持度降低。我们发现，在不期望自己喜欢的候选人获胜的参与者中，没有低估对手的情况。在选举后进行的相同实验表明，党派数学偏见在很大程度上消失了，转而支持与现实相符的估计。研究结果表明，对政治民意调查数据的数学处理受到人们投票意图和一厢情愿的影响，而且，选民对世界可能或实际状况的预期至关重要。
