Navigating High Uncertainty: A Step-by-Step Guide to Scenario Modelling for Local Elections
Introduction
When forecasting local elections in England, you often face a paradox: the uncertainty surrounding your predictions can exceed the impact of any single political shock. Traditional models that aim for a single, precise forecast may prove misleading or even harmful. Instead, a scenario modelling approach—calibrated against historical errors and built to acknowledge what you don't know—offers a robust alternative. This guide will walk you through creating scenario models that are most useful precisely when they refuse to give a simple forecast. By the end, you'll have a repeatable framework for handling high-uncertainty election environments.
What You Need
- Historical election data for English local elections (at least two cycles, preferably five or more). Sources: local authority results, national swing estimates.
- Polling data – national and local polls, with margin of error and sample sizes.
- Demographic and boundary data – ward or council-level population, turnout, and boundary changes.
- Statistical software (e.g., R, Python with pandas/scipy, or a spreadsheet with random number generation).
- Domain knowledge – understanding of local political dynamics (incumbency, key issues, defections).
- Patience and humility – the willingness to accept that uncertainty may dwarf your projections.
Step-by-Step Process
Step 1: Define the Boundaries of Known Unknowns
Begin by mapping the sources of uncertainty. In local elections, these include: polling error (house effects, sample bias), turnout variation, local campaign effects, and last-minute events. For each source, quantify a plausible range based on historical error. For example, national polls for local elections have a typical absolute error of 2–4 percentage points. Local polls may have larger errors. Create a table with three columns: source, historical error range, and probability distribution (normal, uniform, or empirical). This becomes the skeleton of your scenario generation.
Step 2: Calibrate Your Baseline with Historical Error
Take the most recent election results and apply the historical error distributions to produce a baseline forecast. Do not adjust for shocks—the idea is to see what the model would have predicted if only past uncertainties repeated. For each ward or council, run a Monte Carlo simulation (1,000+ iterations) drawing from the error distributions. Record the median forecast and the 90% prediction interval. This step ensures your model respects the fact that uncertainty is often bigger than any single shock.
Step 3: Construct Plausible Shock Scenarios
Now identify potential shocks that could affect the election. Examples: a national party scandal, a local referendum, a change in turnout due to weather, or a boundary revision. For each shock, assign a probability of occurrence (e.g., 5–20%) and a magnitude of impact on vote share (e.g., ±5 points). Keep these estimates grounded in past analogous events. Avoid overfitting to any one scenario.
Step 4: Run Combined Scenario Simulations
Merge the baseline error model with the shock scenarios. For each iteration, decide whether each shock occurs (based on its probability). If it occurs, apply its effect. Then add the baseline error as in Step 2. The outcome is a distribution that reflects both structural uncertainty and discrete shock uncertainty. Summarise the results as a probability of each party winning each ward, rather than a point forecast.
Step 5: Analyze Where the Model Refuses to Forecast
Examine the results for wards where the prediction interval is wider than the gap between the top two parties. These are places where the model essentially says "I don't know." Resist the temptation to collapse that uncertainty into a single prediction. Instead, flag these wards for qualitative analysis—local reporters, candidate quality, and issue salience. The model's honesty is its greatest strength.
Tips for Successful Scenario Modelling
- Always present intervals, not point estimates. A single number hides the uncertainty that is the core of your model.
- Update your historical error data regularly. Each election cycle provides fresh calibration data.
- Work with local experts. They can validate your shock magnitudes and probabilities.
- Communicate the "refusal to forecast" as a feature, not a bug. It indicates respect for genuine unpredictability.
- Visualise your results with fan charts or density plots. Avoid bar charts that imply certainty.
- Document all assumptions. When the election comes, you can learn from where your model was wrong.
- Start simple. A model with just baseline error and two shock scenarios is better than a complex black box.
By following these steps, you build a scenario model that acknowledges when the uncertainty is bigger than any shock. This honest approach is far more valuable than a false precise forecast.