This paper proposes a correction to the Flis-Słomczyński-Stolicki (2019, 2020) formula for countries with large variation among their districts in terms of political divisions. The Flis-Słomczyński-Stolicki formula (FSS formula) estimates seat allocations under the Jefferson-D’Hondt method by using national vote shares, as well as other parameters that are often readily available. However, the FSS formula does not yield precise estimates in those countries where there are independent candidates, special rights assigned to minority parties, signifi cant variation in district sizes, or an unequal distribution of votes due to ethnic or other regional divisions. Hence, I propose dividing the national distribution of votes into regions that satisfy the assumptions of the FSS formula within their district borders. By applying the FSS formula to regions consisting of historically and politically homogenous districts, I demonstrate that the formula’s estimates become signifi cantly more precise. For instance, by applying the regional correction to the 2018 Turkish Parliamentary elections, as well as other Turkish elections between 2007 and 2015, I show that the formula with the correction in three separate regions improves the Loosemore-Hanby goodness of fi t estimates from 2.1 to 3.41 percentage points (95% CI). Thus, the correction might signifi cantly improve the estimates of the FSS formula in various countries, including Spain, Peru, and Belgium.

Before the parliamentary elections in 2018, the ruling party in Turkey, AKP, introduced a new apparentement provision in the electoral law, which allowed parties to make electoral alliances in order to meet the electoral threshold. We claim that this was an ex post mistake. AKP’s electoral engineering was motivated by their fear that its coalition partner, MHP, would not exceed the 10% threshold. While MHP actually met the threshold in the election, the opposition party, İP, failed to do so. Thanks to the new law, the votes for İP were not wasted, as would have happened under the old law. AKP less than 50% of seats and it consequently lost the parliamentary majority. Under the old electoral law, AKP would have won the majority. We use four alternative scenarios in order to estimate the seats and evaluate the political consequences of the unsuccessful electoral manipulation

The main goal of this research was to investigate whether people exhibit algorithm aversion—a tendency to avoid using an imperfect algorithm even if it outperforms human judgments—in the case of estimating students’ percentile scores on a standardized math test. We also explored the relationships between numeracy and algorithm aversion and tested two interventions aimed at reducing algorithm aversion. In two studies, we asked participants to estimate the percentiles of 46 real 15-year-old Polish students on a standardized math test. Participants were offered the opportunity to compare their estimates with the forecasts of an algorithm—a statistical model that predicted real percentile scores based on fi ve explanatory variables (i.e., gender, repeating a class, the number of pages read before the exam, the frequency of playing online games, socioeconomic status). Across two studies, we demonstrated that even though the predictions of the statistical model were closer to students’ percentile scores,
participants were less likely to rely on the statistical model predictions in making forecasts. We also found that higher statistical numeracy was related to a higher reluctance to use the algorithm. In Study 2, we introduced two interventions to reduce algorithm aversion. Depending on the experimental condition, participants either received feedback on statistical model predictions or were provided with a detailed description of the statistical model. We found that people, especially those with higher statistical numeracy, avoided using the imperfect algorithm even though it outperformed human judgments.
Interestingly, a simple intervention that explained how the statistical model
works led to better performance in an estimation task.