论文标题
选举问责制和通过个性化信息汇总的选择
Electoral Accountability and Selection with Personalized Information Aggregation
论文作者
论文摘要
我们研究了选举责任和选择的模型，从而通过有限的关注来将现任政治家的绩效数据汇总到个性化信号中。极端选民的信号表现出自己的党派偏见，这阻碍了他们辨别现任人的好表现和坏事的能力。尽管仅这种影响就会破坏选举的责任制和选择，但党派分歧产生了反击效应，这使中间派选民更有可能是关键的。如果后者的无偏信号对现任人的绩效非常有用，那么对选举问责制和选择的综合影响实际上可能是积极的。因此，在每种政治话语中都具有负面含义的因素（例如增加质量两极分化和关注范围缩小）具有模棱两可的问责制和选择效应。如果做得适当地将选民的信号相关联，可以明确地改善选举责任和选择，从而改善选民福利。
We study a model of electoral accountability and selection whereby heterogeneous voters aggregate incumbent politician's performance data into personalized signals through paying limited attention. Extreme voters' signals exhibit an own-party bias, which hampers their ability to discern the good and bad performances of the incumbent. While this effect alone would undermine electoral accountability and selection, there is a countervailing effect stemming from partisan disagreement, which makes the centrist voter more likely to be pivotal. In case the latter's unbiased signal is very informative about the incumbent's performance, the combined effect on electoral accountability and selection can actually be a positive one. For this reason, factors that carry a negative connotation in every political discourse -- such as increasing mass polarization and shrinking attention span -- have ambiguous accountability and selection effects in general. Correlating voters' signals, if done appropriately, unambiguously improves electoral accountability and selection and, hence, voter welfare.
