Navigating Electoral Uncertainty: A Q&A on Scenario Modelling for Local Elections

When forecasting local elections in England, traditional models often fail because the uncertainty surrounding key variables can dwarf the actual impact of unexpected events. Scenario modelling offers a powerful alternative: instead of pretending to predict the future, it maps out plausible outcomes under different conditions. This Q&A explores how calibrated uncertainty, historical error analysis, and models that refuse to produce a single forecast can provide more useful insights for analysts and decision-makers.

What is scenario modelling and how does it differ from traditional forecasting?

Scenario modelling is a technique that explores multiple possible futures rather than trying to pinpoint a single most likely outcome. Traditional forecasting relies on historical trends and assumes stable relationships between variables. In contrast, scenario modelling explicitly acknowledges that the future is uncertain and that small changes in underlying assumptions can lead to dramatically different results. For English local elections, this means considering various turnout levels, shifts in voter preferences, or even the sudden emergence of a new political party. By constructing a set of internally consistent narratives, analysts can examine how different forces might play out. The key difference is humility: a forecast claims to know what will happen, while a scenario model says, 'Here are some things that could happen, given these conditions.' This approach is especially valuable when the uncertainty in the system is larger than any single shock, such as a scandal or policy change.

Why is the uncertainty often bigger than the shock itself in local elections?

In local elections, many variables interact in complex ways. The 'shock' might be a major national event, a local scandal, or a sudden change in policy. However, the underlying uncertainty about voter engagement, demographic shifts, and local issues can be far larger. For example, a scandal might shift a few percentage points, but the uncertainty about baseline turnout could be ±10% or more. This means that even a dramatic shock may be lost within the noise of normal variability. Moreover, local elections are often influenced by factors that are hard to measure accurately, such as the effectiveness of ground campaigns or the mood on the doorstep. Scenario modelling helps by forcing analysts to consider a range of plausible values for these uncertain inputs. Rather than being surprised by an outcome that seems improbable under a single forecast, the model prepares stakeholders for a spectrum of possibilities. This makes the uncertainty explicit and manageable.

How does calibrated uncertainty improve scenario models?

Calibrated uncertainty means that the ranges used in scenario models are not just arbitrary bands but are based on historical error analysis. For English local elections, this involves studying past forecasts versus actual results to understand the typical magnitude of errors. For instance, if polls have historically missed by an average of 3% for a certain type of seat, the scenario model will incorporate a ±3% range around key variables. This calibration ensures that the scenarios are realistic and not overly optimistic or pessimistic. It also allows analysts to quantify the confidence they have in each scenario. By anchoring uncertainty in real-world data, the model becomes more trustworthy. Decision-makers can then see which outcomes are most consistent with past performance and where the risks are highest. In short, calibrated uncertainty replaces guesswork with evidence-based ranges, making scenario modelling a rigorous tool even when the future is highly uncertain.

What role does historical error play in building scenarios?

Historical error is the foundation for setting realistic boundaries in scenario modelling. Analysts examine past elections to identify how often and by how much various predictions were wrong. This includes errors in polling, turnout models, and demographic assumptions. For example, if a model predicted a 60% turnout but the actual was 55%, the error is 5 percentage points. By collecting many such errors, analysts can create a distribution that shows the typical range of inaccuracy. This distribution then feeds into the scenario model by defining the plausible upper and lower bounds for each uncertain variable. Without historical error, scenario limits might be set too narrowly, missing extreme but possible outcomes, or too broadly, making the model useless. By grounding scenarios in actual past performance, historical error ensures that the model reflects true uncertainty rather than analyst bias. It also helps in stress-testing: asking what would happen if errors were at the extremes of historical experience.

Why would a model refuse to forecast, and how is that useful?

Sometimes, a model is most useful when it explicitly states that it cannot provide a single forecast. This happens when the uncertainty is so large that any specific prediction would be misleading. For example, if historical error ranges are larger than the expected margin of victory in many seats, producing a point forecast would imply a false sense of precision. In such cases, the model 'refuses to forecast' by only offering scenarios. This forces stakeholders to confront the true level of uncertainty rather than making decisions based on a single number that is likely wrong. It also avoids the common pitfall of overconfidence, where a forecast is mistaken for a guarantee. By refusing to offer a point estimate, the model fosters a more nuanced discussion about risks and contingencies. Decision-makers can then ask, 'Under what conditions would we win or lose?' rather than 'Will we win?' This shift in thinking is especially valuable for campaign strategy, resource allocation, and risk management in elections.

How can analysts apply scenario modelling in practice for local elections?

To apply scenario modelling, analysts first gather historical data on past elections, polls, and demographic trends. Then they calibrate uncertainty ranges using historical error distributions. Next, they identify key drivers of local election outcomes, such as national swing, candidate popularity, and local issues. For each driver, they define a set of plausible values (low, medium, high) based on calibrated uncertainty. The model then generates a matrix of scenarios by combining these values, often using a Monte Carlo simulation. Each scenario yields a hypothetical result for each seat. Analysts can then present the most likely scenarios, the best-case and worst-case, and the probability of different outcomes. For example, they might say, 'In 70% of scenarios, the Labour Party wins at least 45% of council seats in this county.' This approach provides a richer picture than a single point forecast. Finally, the model can be updated as new data arrives, making it dynamic and responsive—a key advantage in fast-moving campaign environments.