Context-sensitive heuristics in statistical reasoning

Previous work has shown that people use anchor-and-adjust heuristics to forecast future data points from previous ones in the same series. We report three experiments that show that they use different versions of this heuristic for different types of series. To forecast an untrended series, our subjects always took a weighted average of the long-term mean of the series and the last data point. In contrast, the way that they forecast a trended series depended on the serial dependences in it. When these were low, people forecast by adding a proportion of the last difference in the series to the last data point. When stronger serial dependences made this difference less similar to the next one, they used a version of the averaging heuristic that they employed for untrended series. This could take serial dependences into account and included a separate component for trend. These results suggest that people use a form of the heuristic that is well adapted to the nature of the series that they are forecasting. However, we also found that the size of their adjustments tended to be suboptimal. They overestimated the degree of serial dependence in the data but underestimated trends. This biased their forecasts.     

Method of Suicide in the Mentally Ill: A National Clinical Survey

Comparisons of psychiatric patients who die by suicide using different methods are scarce. We aimed to establish the methods of suicide used by those who are currently or have recently been in contact with mental health services in England and Wales (N = 6,203), and describe the social and clinical characteristics of suicides by different methods. We found that hanging, self‐poisoning, and jumping (from a height or in front of a moving vehicle) were the most common methods of suicide, accounting for 79% of all deaths. The implications of these and other findings are discussed.     

Election Outcome and Tax Compliance: The Role of Political Party Affiliation,Affect Balance,and Trust in Government

The present study examines partisan reactions to presidential election outcomes. Our model investigates the interactive role of political party affiliation on the relationship between identification with the winning party and affect balance. We subsequently examine how tax compliance intentions are influenced by this moderation relationship through affect balance and trust in government. We conducted a quasi-experiment one week prior to the first mass 2016 presidential primary, where 12 of the 50 US states voted to decide which candidates would represent the Republican and Democratic parties in the 2016 US presidential election. Our sample consisted of 205 Republicans and Democrats. We manipulated press releases showing various presidential candidates winning the presidency to examine how matches / mismatches between partisans’ political party affiliation and the party winning the election influence citizens’ overall feelings, beliefs, and intentions. We find election outcomes generate significant overall positive or negative feelings (i.e., affect balance) among partisans, which influences beliefs about trust in government, and subsequently their tax compliance intentions. Political party moderates the relationship between election outcomes and affect balance in such a way that Democrats experience greater overall positive affect balance when their party wins the election compared to Republicans.