Navigating Uncertainty in Local Election Forecasts: A Scenario Modelling Approach
In the realm of election forecasting, uncertainty often overshadows the immediate shock of results. This case study explores scenario modelling for English local elections, emphasizing calibrated uncertainty, historical error, and why some models prove most valuable when they deliberately avoid making a single forecast. Below, we answer key questions about this nuanced approach.
1. What is scenario modelling in the context of English local elections?
Scenario modelling is a technique that generates multiple plausible future outcomes based on varying assumptions, rather than a single prediction. For English local elections, it involves adjusting key variables—such as voter turnout, swing patterns, or demographic shifts—to create a range of possible seat distributions. Unlike traditional forecasting, which aims to pinpoint one result, scenario modelling embraces uncertainty by presenting a spectrum of outcomes. Each scenario is internally consistent and grounded in historical data, allowing analysts to explore what if situations. This method is especially useful when the electoral landscape is volatile, as it helps stakeholders prepare for different possibilities rather than being caught off guard by a single unexpected result.
2. How does scenario modelling differ from conventional election forecasting?
Conventional election forecasting typically produces a single predicted outcome, such as “Party A wins 45% of seats.” It often relies on a point estimate from a statistical model, with confidence intervals that may be narrow. In contrast, scenario modelling deliberately avoids a single forecast. Instead, it generates a set of distinct, coherent narratives about the future, each with its own probability weight. For example, a model might produce scenarios like “high turnout boosts Labour” or “Liberal Democrats gain in rural seats.” The key difference lies in refusing to forecast a single number; scenario models prioritize illustrating the range of possible realities. This is particularly valuable when uncertainty is large—for instance, during times of political realignment or unusual local issues—because it forces decision-makers to think contingently rather than rely on a potentially false precision.
3. Why is “calibrated uncertainty” important in scenario models?
Calibrated uncertainty means that the model’s range of outcomes accurately reflects the historical error rate of similar forecasts. In practice, if a model says that a party has a 70% chance of winning, historical data should show that such claims are correct 70% of the time. This calibration is critical because it prevents overconfidence or underconfidence. English local elections are especially tricky—they involve thousands of seats with varying local dynamics, making it easy to misestimate uncertainty. A well-calibrated scenario model adjusts its probability distributions based on past performance, ensuring that the scenarios presented are neither too wide (uselessly vague) nor too narrow (misleadingly precise). By matching the model’s confidence to real-world accuracy, calibrate uncertainty gives stakeholders a reliable basis for planning, whether for campaign strategies or resource allocation.
4. How does historical error inform scenario development for local elections?
Historical error—the difference between past model predictions and actual election results—provides a crucial feedback loop. In scenario modelling, analysts examine these errors to identify systematic biases or volatility patterns. For instance, if previous forecasts consistently underestimated the Liberal Democrat vote share in certain regions, scenarios can incorporate stronger performance there. Historical error also helps define the plausible range of outcomes. By studying how much models missed in past English local elections (say, a 5% average error on seat share), modellers can set realistic boundaries for their scenarios. This prevents extremes that have no historical basis. The approach turns past mistakes into a learning tool, making each new set of scenarios more robust. As a result, the scenarios are grounded in empirical evidence rather than pure speculation, increasing their utility for campaigners and analysts alike.
5. Why would a model ever “refuse to forecast,” and how is that useful?
A model “refuses to forecast” when the uncertainty is so large that any single prediction would be misleading. In English local elections, this can happen due to factors like boundary changes, new party coalitions, or unprecedented voter shifts. Instead of offering a false sense of precision, the model presents multiple scenarios with no single “best” guess. This is useful because it forces decision-makers to consider a range of possibilities and prepare contingency plans. For example, a campaign manager might prepare for both a tight race and a landslide win. Refusing to forecast is an honest acknowledgment of epistemic limits—it prevents overreliance on a flawed number and encourages strategic flexibility. In such cases, the model’s value lies in its ability to map the landscape of possibilities, enabling smarter decisions under deep uncertainty.
6. Can you give an example of scenario modelling applied to a recent English local election?
Consider the 2023 English local elections, where the national swing was uncertain due to the cost-of-living crisis. A scenario model might have created three scenarios: (A) strong anti-government protest vote, (B) moderate swing with Labour gains, and (C) surprisingly resilient Conservative support. Each scenario would have specific assumptions about turnout (e.g., low youth turnout in scenario C) and local issues (e.g., potholes in Tory strongholds). Historical error data from 2019 and 2021 elections would calibrate the probability of each scenario. The result was a set of plausible seat maps, not a single number. For a local party planning a campaign, scenario A would suggest focusing resources on defending marginal seats, while scenario B might call for targeting new wards. This concrete application shows how scenario modelling helps tailor strategies to a range of futures instead of betting on one.
7. What are the main benefits of using scenario modelling over traditional polling?
Traditional polling provides a snapshot of voter intentions at a single point in time, often with a margin of error. Scenario modelling offers several advantages: it captures uncertainty more honestly by showing multiple outcomes rather than a single number; it integrates qualitative assumptions (e.g., “what if a scandal erupts?”) that polls can’t; and it forces users to think strategically about different futures. Moreover, scenario models can incorporate historical error to avoid overconfidence. For English local elections, where local dynamics vary hugely, this flexibility is invaluable. While polls are useful for gauging current mood, scenario modelling equips decision-makers with a robust framework for planning under uncertainty. It turns data into narratives that are easier to communicate and act upon, especially when the electoral environment is as unpredictable as it is today.