Beyond Point Forecasts: Scenario Modelling for English Local Elections

Introduction

Election forecasting often grabs headlines with a single predicted percentage or seat count. But for local elections in England, the reality is far messier. Small sample sizes, shifting voter allegiances, and local idiosyncrasies mean that a single point forecast can be misleading. Instead, many analysts turn to scenario modelling—a technique that explicitly embraces uncertainty. This article explores why scenario modelling can be more useful than traditional forecasting, especially when the uncertainty surrounding an election is larger than any potential shock.

What Is Scenario Modelling?

Scenario modelling does not produce one number. It generates a range of possible outcomes based on different assumptions about turnout, party performance, and local factors. Unlike a deterministic forecast, which says “Party A will win 45% of the vote,” a scenario model might present several paths: one where turnout is high, another where a local scandal depresses support, and a third where demographics shift. The output is a set of plausible futures, not a single prediction.

Calibrated Uncertainty

A key concept in scenario modelling is calibrated uncertainty. This means the model’s range of outcomes is statistically validated against historical data. For example, if the model says there is a 90% chance that the Labour vote share falls between 30% and 40%, then in 9 out of 10 past similar elections, the actual result should have indeed fallen in that interval. Calibrated uncertainty ensures the model does not over- or under-estimate risk. In local elections, where polling data is sparse, this calibration is crucial to avoid false precision.

Historical Error Patterns

Another component is historical error—the systematic biases that have affected previous models. Perhaps polls consistently overestimated Conservative support in rural wards, or phone surveys missed younger voters in urban centres. By incorporating these error patterns into the scenario model, analysts can adjust their ranges accordingly. For example, if past errors show a +2 percentage point bias for the Liberal Democrats in South West England, the model can correct for it. This transforms a simple forecast into a learning system that becomes more accurate over time.

When Models Refuse to Forecast

Perhaps the most counterintuitive benefit of scenario modelling is that it can deliberately refuse to forecast. When uncertainty is so large that even the broadest range includes nearly all possible outcomes, the model effectively says: “I cannot give you a useful prediction.” This honesty is valuable. It prevents decision-makers from relying on a false sense of certainty. For instance, in a ward where no reliable local polling exists and national trends are a poor proxy, a good scenario model will produce an extremely wide band—perhaps 20 to 60 percent for a given party. Instead of forcing a numeric guess, the analyst can then explain the sources of uncertainty and suggest what additional data would help narrow it down.

The Shock that Didn’t Happen

The original article’s title hints at a paradox: “When the Uncertainty Is Bigger Than the Shock.” Imagine a hypothetical local election where a national scandal erupts just days before the vote. In a traditional forecast, that shock might be treated as a major factor, requiring a new model run. But if the scenario model already accounts for extreme swings (due to calibrated uncertainty and historical error), the shock may fall within the existing range. In that case, the model’s output does not change dramatically; the scenario analysis already included a possibility that looks like the shock. This stability can be reassuring: it shows that the model’s range was not artificially narrow.

Practical Application for English Local Elections

Local elections in England involve thousands of individual contests, many with very small electorates. Historical error patterns vary by region, ward type, and even time of year. A scenario model for these elections typically begins with national vote intention polls, then applies regional adjustments and local demographic factors. Multiple scenarios are run: one with uniform swing, another with differential turnout by age group, and a third with a “local incumbency bonus.” The result is a set of plausible seat ranges for each party, along with a clear statement of which factors drive the widest variation.

Example: Birmingham Local Elections

To illustrate, consider a recent Birmingham ward election. National polls showed Labour leading by 10 points, but local factors included a popular independent councillor and a new housing development that might shift voter registration. A scenario model might produce three outcomes:

• Baseline scenario: Labour wins with 38% of the vote, based on national swing.
• High-turnout scenario: Older voters dominate, boosting Conservative share to 34% and Labour to 36%.
• Independent surge scenario: The independent candidate gains 20%, dropping both major parties below 30%.

Note that none of these point forecasts claim certainty. The model’s value lies in showing that the Conservative vote could range from 25% to 38%, and the independent could reach 20%—information that allows campaigners to allocate resources wisely.

Conclusion

Scenario modelling is not a way to avoid making hard choices; it is a way to make better ones by facing uncertainty head on. By calibrating uncertainty, incorporating historical errors, and sometimes choosing not to forecast at all, these models provide a richer picture of what might happen in English local elections. For anyone interested in election analysis, understanding when and how to use scenario modelling is a powerful tool.

This article builds on concepts from the original When the Uncertainty Is Bigger Than the Shock: Scenario Modelling for English Local Elections.