Combining forecasts predicts a Democratic win in this year’s election

With the 2016 presidential primary now well underway, many pundits and commentators are beginning to cast their minds towards the fall general election. But what is the best way to predict the outcome? Andreas Graefe argues that given the plethora of models and forecasting methods which are available, the best option is to combine all the approaches into one forecast. Using this combined approach – which has been accurate over the last three presidential elections – the PollyVote.com currently predicts that a Democrat would win nearly 53 percent of the two-party vote in November, while a Republican would gain just over 47 percent

Asking voters who they think will win is one of the mostaccurate methods for forecasting elections available

For decades political pollsters have relied on questions about people’s voting intention in order to predict who will win an election. But what about asking voters about their expectation of which party will win? In new research Andreas Graefe analyzes the accuracy of expectation-based forecasts in presidential elections from 1988 to 2012. He finds that such forecasts are, on average, more accurate at forecasting the election’s outcome than four more established methods

Looking at how candidates handle issues and their leadership capability can be just as effective at predicting presidential races as the strength of the economy

With every election come new and different models of forecasting the results. With presidential elections, most of these models tend to rely heavily on the state of the economy for their predictions. While these models are relatively successful, they do nothing to aid decision-making of parties, candidates, and voters. Andreas Graefe explains the Issues and Leaders model, which bases its predictions on how candidates are expected to handle issues and their perceived strength as leaders. He argues that not only is the model effective, but it provides valuable feedback for campaigns and voters

Computational Campaign Coverage

Algorithms for automatically generating stories from machine-readable data have been shaking up the news industry, not least since the Associated Press started to automate the production and publication of quarterly earnings reports in 2014. Once developed, such algorithms can create an unlimited number of news stories for a routine and repetitive topic—faster, cheaper, and with fewer errors than any human journalist ever could. Within the “Computational Campaign Coverage” research project, researchers teamed up with the German-based software company AX Semantics to develop automated news based on forecasting data for the 2016 U.S. presidential elections. Over the course of the project, nearly 22,000 automated news articles were published in English and German

Issue-handling beats leadership: Issues and Leaders model predicts Clinton will defeat Trump

The Issues and Leaders model predicts the national popular two-party vote in US presidential elections from people’s perceptions of the candidates’ issue-handling competence and leadership qualities. In previous elections from 1972 to 2012, the model’s Election Eve forecasts missed the actual vote shares by, on average, little more than one percentage point and thus reduced the error of the Gallup pre-election poll by 30%. This research note presents the model’s forecast prior to the 2016 election, when most polls show that voters view Republican candidate Donald Trump as the stronger leader but prefer the Democrat’s nominee Hillary Clinton when it comes to dealing with the issues. A month prior to Election Day, the model predicts that Clinton will win by four points, gaining 52.0% of the two-party vote

Guide to Automated Journalism

In recent years, the use of algorithms to automatically generate news from structured data has shaken up the journalism industry—most especially since the Associated Press, one of the world’s largest and most well-established news organizations, has started to automate the production of its quarterly corporate earnings reports. Once developed, not only can algorithms create thousands of news stories for a particular topic, they also do it more quickly, cheaply, and potentially with fewer errors than any human journalist. Unsurprisingly, then, this development has fueled journalists’ fears that automated content production will eventually eliminate newsroom jobs, while at the same time scholars and practitioners see the technology’s potential to improve news quality. This guide summarizes recent research on the topic and thereby provides an overview of the current state of automated journalism, discusses key questions and potential implications of its adoption, and suggests avenues for future research. Some of the key points can be summarized as follows

Accuracy gains of adding vote expectation surveys to a combined forecast of US presidential election outcomes

In averaging forecasts within and across four-component methods (i.e. polls, prediction markets, expert judgment and quantitative models), the combined PollyVote provided highly accurate predictions for the US presidential elections from 1992 to 2012. This research note shows that the PollyVote would have also outperformed vote expectation surveys, which prior research identified as the most accurate individual forecasting method during that time period. Adding vote expectations to the PollyVote would have further increased the accuracy of the combined forecast. Across the last 90 days prior to the six elections, a five-component PollyVote (i.e. including vote expectations) would have yielded a mean absolute error of 1.08 percentage points, which is 7% lower than the corresponding error of the original four-component PollyVote. This study thus provides empirical evidence in support of two major findings from forecasting research. First, combining forecasts provides highly accurate predictions, which are difficult to beat for even the most accurate individual forecasting method available. Second, the accuracy of a combined forecast can be improved by adding component forecasts that rely on different data and different methods than the forecasts already included in the combination

Automated Journalism: A Meta-Analysis of Readers’ Perceptions of Human-Written in Comparison to Automated News

This meta-analysis summarizes evidence on how readers perceive the credibility, quality, and readability of automated news in comparison to human-written news. Overall, the results, which are based on experimental and descriptive evidence from 12 studies with a total of 4,473 participants, showed no difference in readers’ perceptions of credibility, a small advantage for human-written news in terms of quality, and a huge advantage for human-written news with respect to readability. Experimental comparisons further suggest that participants provided higher ratings for credibility, quality, and readability simply when they were told that they were reading a human-written article. These findings may lead news organizations to refrain from disclosing that a story was automatically generated, and thus underscore ethical challenges that arise from automated journalism

Conditions Under Which Index Models Are Useful

This paper summarizes the key conditions under which the index method is valuable for forecasting and describes the procedures one should use when developing index models. The paper also addresses the specific concern of selecting inferior candidates when using the bio-index as a nomination helper. Political decision-makers should not use the bioindex as a stand-alone method but should combine forecasts from a variety of different methods that draw upon different information