Optimizing Municipal Budgets with AI - Optimizing Municipal Budgets - Strategic Budget Planning for Cities Using AI

Municipal Budget Optimization with AI: Budget Simulation and Mathematical Optimization Presented Live at the City Council Meeting

From a static budget plan to interactive budget simulation in the finance committee

What if a city council could not only vote on a proposed budget draft, but also simulate different budget scenarios directly during the meeting, mathematically optimize them, and compare them with one another?

This is exactly where StratePlan comes in.

Municipalities face an increasingly difficult challenge: rising social spending, infrastructure in need of renovation, investments in schools, digitalization, climate protection, and public facilities are all competing for limited financial resources.

The challenge is not merely to reduce spending or prioritize individual projects based on their urgency.

The crucial question is:

Which combination of municipal projects achieves the greatest possible overall social, economic, and strategic benefit within the available budget?

With StratePlan, this question can be systematically examined through mathematical portfolio optimization.

In this process, the administration’s technical assessments, the financial framework, and political objectives are integrated into a unified decision-making model.

The result is a new form of municipal budget management: transparent, traceable, dynamic, and directly applicable in political decision-making bodies.

1. The Fundamental Problem: One Budget, Many Projects, Limited Funds

Let’s consider an example German city:

• Available investment budget: 81 million euros
• Planned investment projects: 50
• Total investment needs: 220 million euros

Consequently, the city cannot finance all of the planned measures at the same time.

Decisions must be made.

Which schools will be renovated? Which streets will be repaved? Which administrative buildings will be modernized? Which climate protection measures will be implemented? Which social services will receive additional funding?

In traditional budget planning, these questions are often answered through technical consultations, political negotiations, and the gradual adjustment of individual budget items.

The problem: Every decision affects the remaining financial leeway for other projects.

With 50 independently selectable projects, this results in:

²⁵⁰ = 1,125,899,906,842,624 possible project combinations.

That corresponds to approximately 1.126 quadrillion combinations.

Not every combination is financially or organizationally feasible. Nevertheless, this number illustrates why a purely manual assessment of individual projects cannot provide a systematic analysis of the entire decision space.

The challenge lies not in evaluating a single project, but in selecting the best possible combination of all projects within the existing budget constraints.

This is precisely a classic problem in combinatorial optimization from operations research.

2. A Simple Starting Point: Project ID, Expenditures, and Subject-Matter Weighting

A municipal budget simulation does not have to start with a complex IT project.

For a simple optimization model, three pieces of information per project are sufficient to start with:

1. Project ID: Unique identifier for the initiative
2. Expenses: Planned financial requirements of the project
3. Weight: The relative importance or urgency as determined by the relevant department

The weight is assigned on a scale from 0.01 to 0.99.

A higher value indicates a higher level of importance as assessed by subject matter experts within the agreed-upon evaluation model.

Example of a municipal project list

Illustrative example values; not actual municipal project data.

The data can be prepared, for example, in a simple Excel spreadsheet.

The key feature: The weighting comes from the relevant departments

StratePlan is not intended to independently decide whether a school is more important than a road or whether a fire station is more urgent than an administrative building.

This technical expertise lies with the responsible departments and specialized units.

The Department of Education knows the structural condition of the schools and the actual need for renovation. The Department of Public Works knows the infrastructure, the condition of the roads, and the technical risks. The fire department knows its operational requirements. The Department of Social Services can assess the social significance of social institutions.

For this reason, the weightings are determined in advance of the budget simulation by the respective department specialists.

The evaluation may be based, among other things, on urgency, strategic importance, societal impact, technical risks, and time constraints.

To ensure that the weightings are comparable across departments, all departments should use a common evaluation grid.

The Treasury Department coordinates this process and consolidates the data.

The subject matter expertise remains within the administration. StratePlan handles the mathematical analysis of the possible project combinations.

3. From Technical Weighting to the Mathematical Decision Space

A binary decision variable is defined for each project:

_xi ∈ {0,1}

• 1: The project is included in the calculated portfolio.
• 0: The project is not included in the calculated portfolio.

With 50 projects, this results in 50 binary decision variables.

StratePlan examines possible combinations of these variables, taking into account the available budget and other defined constraints.

A simplified mathematical model is as follows:

Maximize the weighted project value:

Max Σ (_wi×xi)

Subject to the budget constraint:

Σ (_{ci × xi}) ≤ B

Where:

• _wi: Weight determined by subject matter experts
• _ci: Planned project expenditures
• _xi: Binary selection decision
• B: Available budget

This simplified model illustrates how technical evaluations and financial constraints can be mathematically linked.

It maximizes the sum of the weights of selected projects. This is not automatically equivalent to maximizing the monetary ROI or conducting a comprehensive assessment of societal impact.

Depending on the specific optimization model, additional criteria such as project dependencies, resource constraints, legal obligations, or multi-year budget limits may be taken into account.

The goal is not to fund as many projects as possible, but to identify the best possible combination of projects according to the defined criteria within the available funds.

4. Live Budget Simulation in the Finance Committee or City Council

The real added value arises when mathematical optimization is directly integrated into the political decision-making process.

Let’s imagine a meeting of the Finance Committee.

The Finance Department presents a draft budget with 50 projects and an available investment budget of 81 million euros.

The relevant departments have previously evaluated their projects and assigned corresponding weights.

StratePlan uses this information to calculate an optimized project portfolio.

The screen displays the selected projects, the budget funds required for them, the remaining budget flexibility, and the overall rating achieved in the model.

Now the political discussion begins.

Scenario A: The Initial Calculation

The finance committee first reviews the project combination based on the originally defined weightings.

Which projects should be implemented with a budget of 81 million euros if the technical evaluations remain unchanged?

StratePlan provides a calculated project combination as a basis for decision-making.

Scenario B: Education is given higher priority

A city council member asks:

“What happens if we give greater weight to the renovation of our schools?”

For example, the weighting of selected school projects is increased from 0.75 to 0.95.

The entire portfolio is then recalculated.

The committee can immediately examine:

• Which school projects are now selected?
• Which other projects might be displaced?
• How does the overall evaluation change?
• Will the budget be maintained?
• What conflicting policy objectives are arising?

Instead of discussing individual budget items exclusively, the committee can understand the impact of a changed priority on the entire project portfolio.

Scenario C: Greater Emphasis on Infrastructure

Another member proposes giving higher priority to roads, bridges, and municipal buildings.

The corresponding weighting parameters are adjusted.

StratePlan calculates a new project combination.

The results can then be compared directly with the original scenario and the education scenario.

This reveals the effects of different policy priorities within the same budget.

Scenario D: The Budget Changes

The available budget can also be included in the simulation.

What happens if only 75 million euros are available instead of 81 million euros?

Or if additional funding increases the budget to 90 million euros?

StratePlan can re-optimize the project mix under the changed budget constraints.

This allows for a discussion not only of the budget amount but also of the specific implications for the projects that can be implemented.

5. Compare Scenarios Instead of Negotiating Individual Items in Isolation

The key advantage lies in the systematic comparison of different budget options.

The actual results are derived from the calculation using the complete project data.

This allows the members of the finance committee to see which projects remain in all scenarios and which projects depend on changes in weightings.

It also allows for an analysis of which projects are competing with one another for the same budget funds.

Any change to a parameter can trigger a new mathematical calculation of the entire project portfolio.

It is crucial that the weightings, budget assumptions, and calculation results used are documented.

6. The New Collaboration Between Line Departments, the Finance Office, and Policy Makers

The introduction of a mathematical budget simulation does not alter democratic responsibilities.

It changes the information base upon which decisions are made.

The line departments evaluate

The relevant departments provide the project information, planned expenditures, and technical weightings.

They are familiar with the technical, social, and organizational requirements of their initiatives and are best positioned to assess their respective levels of urgency.

The Finance Department coordinates and models

The Finance Department compiles the project information, reviews the available budget funds, and ensures compliance with the relevant financial and legal frameworks.

It prepares the budget simulation and ensures that the weightings are based on a transparent, cross-departmental, and comparable foundation.

The Finance Committee simulates and discusses

The prepared scenarios are presented to the Finance Committee.

Policy priorities can be modeled as changes to weightings or budget parameters and then recalculated.

This reveals the consequences of certain priorities for the entire investment portfolio.

The City Council decides

Ultimate political responsibility remains with the democratically elected decision-makers.

StratePlan replaces neither the technical assessment by the administration nor the political decision.

The technology calculates the consequences of defined assumptions. Policymakers decide which goals and priorities should take precedence.

7. Mandatory responsibilities, project dependencies, and legal requirements

Not every municipal measure can be freely determined.

Mandatory statutory duties, contractual obligations, or measures that have already been bindingly adopted must be taken into account accordingly during modeling.

For a simple budget simulation, fixed expenditures can initially be deducted from the available budget.

Only the remaining available budget is then optimized for the selection of optional projects.

Example

• Total budget: 81 million €
• Measures already committed: €21 million
• Remaining optimization budget: €60 million

The mandatory measures are documented separately and do not need to be treated again as freely selectable projects.

Project dependencies are also relevant.

For example, if an energy-efficient building renovation is necessarily linked to a roof renovation, these measures must not be evaluated independently of one another.

In a simplified model, such measures can be combined into a single project.

In advanced optimization models, such dependencies can also be explicitly modeled as constraints.

This ensures that the calculation remains aligned with the actual organizational and legal framework.

8. Why Weighting Is the Key to Policy Simulation

Weights ranging from 0.01 to 0.99 bridge the gap between expert judgment and mathematical calculation.

It allows different priorities to be modeled as parameters.

An important distinction must be made here:

A weighting is not an objective truth, but rather a documented assessment within a defined model.

A school project with a weighting of 0.95 is not automatically more socially valuable than an infrastructure project with a weighting of 0.80.

The values express the relative priority according to the agreed-upon evaluation criteria.

That is why preparation by department specialists is crucial.

They can justify their assessments based on transparent criteria. The finance committee can then examine how changing political priorities affect the overall result.

Sensitivity Analysis Using Different Weightings

Repeated calculations can answer important questions:

• How stable is the original project selection?
• Which projects remain selected even when weightings are significantly altered?
• For which projects do even small changes in parameters lead to a different decision?
• Which investment measures compete particularly strongly with one another?
• Which policy priorities have the greatest impact on the budget structure?

This information can significantly improve the quality of policy advice.

9. From One-Time Budget Planning to Continuous Management

A municipal budget simulation does not have to be limited to the annual budget preparation process.

Significant conditions can change over the course of a fiscal year.

Construction costs rise. Funding is approved or withdrawn. Projects are delayed. New legal requirements arise. Additional investments become necessary.

With an updated dataset, the effects of such changes can be recalculated.

Multi-year investment planning can also be analyzed using a correspondingly expanded model.

To do this, factors such as annual budget limits, project durations, time dependencies, and resource availability are taken into account.

This allows the finance department to transition from a predominantly static budget analysis to dynamic decision support.

The central question is then no longer simply:

“Which projects are included in the budget?”

But rather:

“Given the current financial, technical, and political conditions, which combination of projects is the best?”

10. Transparency and Democratic Accountability

Mathematical optimization must not be confused with an automated political decision.

An optimization model can only take into account those objectives, weightings, and constraints that have been defined in advance.

That is why the transparency of the process is crucial.

For each simulation, the project information, weightings, budget limits, and other assumptions used should be documented in a way that is easy to understand.

Likewise, the calculated project combinations and changes compared to earlier scenarios should be clearly visible.

This enables a fact-based discussion of the actual impacts of different decisions.

StratePlan serves as a mathematical tool for decision support—not as a substitute for democratic decision-making.

11. How a municipality can get started with StratePlan

An initial pilot can be conducted with a limited number of municipal investment projects.

The finance department defines the available budget and compiles the planned measures.

The respective departments provide the expenditures and their technically justified weightings.

The data is then transferred to a standardized project list.

Even with just the three basic parameters— project ID, expenditures, and weighting —a simplified municipal portfolio optimization model can be prepared.

After the initial calculation, alternative weightings and budget limits can be tested.

The results can then be presented in a workshop with the Finance Department, the Finance Committee, or the City Council.

This demonstrates the benefits of mathematical optimization using concrete municipal decision-making questions, without initially requiring a complete overhaul of the existing budget software.

12. Conclusion: The city council of the future does not merely discuss budget figures—it simulates decisions

Municipal budget planning is one of the most challenging tasks of public administration.

Limited funds must be allocated to numerous projects with varying technical, social, and political objectives.

Traditional methods reach their limits, particularly when the interactions between many projects and budget constraints need to be analyzed.

StratePlan offers a complementary approach.

The departments assess the urgency of their projects.

The finance department defines the financial framework and consolidates the data.

Mathematical optimization calculates suitable project combinations.

Policy-making bodies can immediately compare different scenarios.

Change the weighting. Recalculate the portfolio. Visualize the impacts. Compare scenarios. Make more informed decisions.

This transforms a static budget proposal into a dynamically analyzable decision-making space.

Mathematics does not determine a city’s future.

But it can reveal the consequences of different decisions within a limited budget.

How much more impact is there in your municipal budget?

See for yourself how municipal investment projects can be mathematically modeled, optimized, and compared across different budget scenarios with just a few input data points.

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