For the last decade I’ve been following the fascinating work of Gerd Gigerenzer and colleagues (especially Dan Goldstein) – as briefly as I can state it, he has identified a number of very simple heuristics which outperform far more complex models for decision-making processes or making predictions about certain kinds of data (this stuff has partly inspired my Feweristics project). The most accessible explanation of all this is in his book Gut Feelings, where he explains things such as the recognition heuristic, and how it can be used to predict the winner of Wimbledon, or build a stock market portfolio that outperforms many experts, and so on.

Now two researchers, inspired by Goldstein and Gigerenzer’s ‘take-the-best heuristic’ have applied the less-information-beats-more methodology to the US elections since 1972. You can read their paper, Predicting elections from the most important issue facing the country (PDF – I found it via Decision Science News, the work of GG’s collaborator Dan Goldstein), though the bare bones as follows.

In the abstract, authors Andreas Graefe and J Scott Armstrong say that their simple model, called PollyMIP, “correctly predicted the winner of the  popular vote in 97% of all forecasts. For the last six elections, it yielded a higher number of correct  predictions of the election winner than the Iowa Electronic Markets”. Basically, they used a database of pre-election polls to identify what voters thought was the single most important issue each time (this varied over time before the election, in some cases more than others), then used the same database to pull out poll results for which of the two candidates (ie Democrat or Republican) they believed would deal with that issue best (they looked at all polls up to 100 days before the election). In passing, they corroborated other research that the incumbent party always starts with an advantage. (The authors note in their paper: “In the real world, people usually have to make decisions under the constraints of limited information and time, which is why models of rational choice often fail in explaining behaviour.”)

In full, their PollyMIP heuristic works thus (taken verbatim from their appendix):

Step 1 (identifying the most important problem)
Search rule: Look up last available poll on the most important problem facing the country; sort problems in the order of importance.
Stopping rule: Stop search if there is a single most important problem. If two or more problems are of similar importance, average their importance with the results from the most recent previously published poll until a problem is identified as the single most important.

Step 2 (obtaining voter support for candidates on most important problem)
Search rule: Look up polls that obtained voter support on the problem identified in step 1.
Stopping rule: Stop search if there are one or more polls available. Average voter support for each candidate and calculate the two-­party shares of the incumbent. Move to step 3.
If no polls are available and the most important problem (as identified in step 1) is different from the previous day, move to step 2.A. Otherwise move to step 2.B.

2.A (most important problem different to the day before)
Stopping rule: Take the incumbent’s two party share of voter support from the last available poll on the most important problem. Move to step 3.

2.B (most important problem similar to the day before)
Stopping rule: Take the PollyMIP score (see step 3) from the previous day. Move to step 3.

Step 3 (determining election winner)
Decision rule: Average the incumbent’s two-­‐party share of voter support for the last three days, which is referred to as the PollyMIP score. If the PollyMIP score is above 50%, predict the incumbent to win. If it is below 50%, predict the challenger to win. Otherwise, predict a tie.

Or, more briefly: “(1) Identify the  problem seen as most important by voters, (2) calculate the two-­party shares of voter support for the  candidates on this problem and average them for the last three days, and (3) predict the candidate with the higher voter support to win the popular vote.”

Not bad for predicting election results 97% of the time. I’d love to see whether this would work for Britain’s elections, too. (They used the iPOLL databank – anyone know if there’s an equivalent for the UK?)