When deciding for whom to vote, voters should select the candidate they expect to best handle issues, all other things equal. A simple heuristic predicted that the candidate who is rated more favorably on a larger number of issues would win the popular vote. This was correct for nine out of ten...

Purpose: To test whether a structured application of persuasion principles might help improve advertising decisions. Evidence-based principles are currently used to improve decisions in other complex situations, such as those faced in engineering and medicine.Approach: Scores were calculated...

Purpose: To respond to issues posed in the four commentaries on Armstrong, Du, Green and Graefe (this issue) regarding the immediate usefulness of that paper’s test of advertisements’ compliance with persuasion principles, and regarding the need for further research. Approach: Address...

The Golden Rule of Forecasting is a general rule that applies to all forecasting problems. The Rule was developed using logic and was tested against evidence from previously published comparison studies. The evidence suggests that a single violation of the Golden Rule is likely to increase...

This article proposes a unifying theory, or Golden Rule, of forecasting. The Golden Rule of Forecasting is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek out and use all knowledge...

We used 59 biographical variables to create a “bio-index” for forecasting U.S. presidential elections. The bio-index method counts the number of variables for which a candidate rates favourably, and the forecast is that the candidate with the highest score would win the popular vote. The...

We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six U.S. Presidential elections from 1992 through 2012, we then report the reduction in error obtained by averaging forecasts within and...