What does the future look like? This question has fascinated humanity for centuries, but in the field of demography it is particularly challenging. Population development follows slow but powerful forces: births, migration, social change, secularization. Our case study has attempted to transparently model exactly these forces—not to predict the future, but to understand how different trends interact with each other.
The following article documents the complete path from the initial data through the model architecture to the simulation results. It serves as a comprehensible basis for the two accompanying articles, which narratively present the findings from the realistic core layer and the exploratory layer.
Why we conducted this case study
The question of which year a religious group in Germany might overtake another often arises from social curiosity, not from political intent. It is less about predictions and more about understanding how long-term trends—secularization, migration, changing religious affiliation—interact.
Germany is a particularly interesting case: historically shaped by Christianity, secularizing for decades, with moderate to significant international migration. A complex, dynamic space.
Data basis: Where we started
We relied on established sources such as:
- national statistics on religious affiliation
- church membership studies
- established demographic projections
- empirically estimated trends on migration and secularization
Since not all parameters are exactly available, such studies often work with plausible assumption ranges. This is exactly where modeling begins.
Model architecture: Three groups, two layers
In the core model we considered:
- Christians
- Muslims
- Rest group (non-denominational & other religions)
This simplification allows robust, transparent trend modeling.
Layer A – the realistic core model
This model uses tightly calibrated parameters. It is closely aligned with empirically proven trends and avoids extremes.
Layer B – the exploratory model
Here we deliberately expanded the parameter ranges to examine:
- What results do strongly changed assumptions yield?
- Where are the limits of the projection?
- How does the model react to extreme values?
Mathematical framework — without technical overload
The development of a group over time is described by a combination of long-term trend (drift) and annual random fluctuation. This creates flexible, realistic developments without rigid assumptions.
We define the overtaking year as the first year in which:
Share of Muslims > Share of Christians
Within the model, this is a clear mathematical quantity—not a statement about the real future.
Calibration: The comparison with reality
We retrospectively checked how well the model can approximate past developments. Our calibration was based, among other things, on:
- secularization since the 1990s
- expected church membership losses until 2060
- projections of the Muslim population share until 2050
The Monte Carlo simulation: 50,000 possible futures
Each future is a possible path. Thousands of such paths create a probability landscape.
Results:
- Realistic core layer: Overtaking year ~ 2110
- Exploratory model: Overtaking year ~ 2085
These numbers are model results, not forecasts.
The most important thing: Transparency about the limits
- Models react sensitively to long-term assumptions.
- They do not show “the future,” but “what happens if trends continue.”
- The greatest uncertainty factor is not chance, but the assumptions themselves.
Conclusion
This case study shows how different results can be—depending on how narrowly or broadly you set parameters. But it also shows: Modeling helps to understand complex dynamics.
The following two articles deepen the results of the two model layers—once realistic, once exploratory.