Decision-making quality in German cities and municipalities: Why municipal investment decisions are structurally suboptimal from a mathematical perspective
Cities have data at their disposal.
They work with budgets, profitability analyses, follow-up cost calculations and subsidy quotas.
They use specialist procedures, controlling instruments, medium-term financial planning and budget processes.
And yet, systematically suboptimal investment decisions are made.
The cause is rarely a lack of figures.
It lies in the structure of decision-making.
The misunderstanding: more data does not automatically mean better decisions
.
Municipal budgets are data-based. Investments are calculated, prioritised and politically legitimised. Nevertheless, one key question usually remains unanswered. :
Is the chosen combination of projects really the best possible one, given all the restrictions?
In political discourse, decision quality is often evaluated normatively – but in a mathematical sense, it means something else. :
- Maximum impact per euro.
- Minimum opportunity costs.
- Compliance with all constraints.
- Transparent and reproducible prioritisation.
This is not an ideological question.
It is an optimisation problem.
Why people do not make mathematically optimal decisions, even in a municipal context
Cities rarely fail due to a lack of competence, experience or commitment.
They fail because of decisions made in highly complex decision-making environments.
For over two decades, modern behavioural economics has shown that people do not make completely rational decisions in the mathematical sense. When faced with complexity, time pressure and uncertainty, they resort to heuristics – cognitive shortcuts that systematically lead to distortions.
These findings are empirically based and have received numerous awards:
- Daniel Kahneman (Nobel Prize 2002) – Evidence of systematic decision-making errors
- Robert J. Shiller (Nobel Prize 2013) – Analysis of irrational market and valuation dynamics
- Richard Thaler (Nobel Prize 2017) – Founding of behavioural economics
The key insight is : Bad decisions are not the result of individual failure on the part of mayors, treasurers or city councillors.
They are a structural feature of human thinking in complex situations. (*see source reference)
The structural core of the problem: the exponential decision space
Let us assume that a city has :
- 50 projects ready for investment
- €81 million available budget
- €220 million total investment requirement
Then there are not 50 options to choose from.
There are 2⁵⁰ possible project combinations – over 1,125 quadrillion variants.
No budget committee.
No closed-door meeting.
No Excel model.
can fully evaluate this decision-making space.
In practice, projects are discussed individually. Departments prioritise in isolation. Funding logic influences the order. Political majorities structure compromises.
The result is often a locally plausible optimum – but
ly, it is highly unlikely to be the best combination overall.
Six typical distortion mechanisms in the municipal decision-making process
Our structural analysis reveals recurring patterns that influence municipal investment decisions :
1. Funding bias
Projects are prioritised because they are eligible for funding – not because they have the greatest impact in the overall portfolio. The subsidy rate is optimised, not the portfolio effect.
2. Political escalation
A project that has been started is continued even though the framework conditions have changed or alternatives would be more attractive. Abandoning the project is considered a political risk.
3. Election cycle logic
Measures with short-term visibility are given priority. Long-term structural projects (digitalisation, energy integration, transport logic, resilience) come under pressure.
4. Isolated project evaluation
Projects are evaluated individually – not as an interdependent portfolio. Opportunity costs remain invisible.
5. Departmental thinking
Each specialist department optimises its own area. The overall impact of the city as a system is rarely modelled simultaneously.
6. Compromise overlap
Political agreements replace mathematical optimisation. Decisions are consensual – but not necessarily maximise impact.
These mechanisms are not individual mistakes.
They arise from organisational structures, incentive systems and limited information processing.
Constraints increase complexity exponentially
Municipal investments are simultaneously subject to :
- budget caps
- credit limits
- CO₂ budgets
- funding deadlines and earmarking
- construction and personnel capacities
- legal obligations
- strategic urban development goals
Each additional restriction expands the scope of decision-making.
With each additional project option, the number of possible combinations grows exponentially.
From the local to the global optimum
The crucial question is not :
Which project makes sense?
But rather :
Which combination of all projects generates the maximum possible overall effect for the city under all restrictions?
Improving the quality of municipal decision-making requires :
- formal modelling of all projects as a portfolio
- clearly defined target values (impact, sustainability, economic efficiency)
- simultaneous consideration of all constraints
- systematic evaluation of possible combinations
- transparent derivation of the optimal starting position
Political decision-making authority remains unaffected.
But it is based on a calculated decision space – not on implicit assumptions.
Transparency instead of implicit opportunity costs
A mathematically sound portfolio analysis enables :
- Disclosure of opportunity costs
- Visualisation of hidden synergies
- Objective prioritisation under restrictions
- Comprehensible decision-making bases
- Greater legitimacy vis-à-vis citizens
Decisions are not replaced by technocracy.
They are structurally refined.
Conclusion
Municipal investment decisions are not irrational.
But they take place in an exponentially growing decision-making space.
As long as projects are prioritised in isolation, there remains a high probability that :
- budget impact will be suboptimally distributed
- combination advantages will remain undiscovered
- opportunity costs will remain invisible
The quality of decision-making in the municipal sphere is therefore less a question of political competence –
than a question of structured mastery of complex decision-making spaces.
Do cities inevitably make suboptimal decisions? The mathematical explanation in the videos :
Order
:
1. Intro video – Understanding the problem and the decision space
2. Deep dive video – Modelling, constraints and optimisation logic
Video 1 :
Video 2: From overview to mathematical depth :
Before we delve into the technical structure, it is crucial to fully understand the fundamental problem: Why do local optima arise structurally in municipal budgets – even with careful political work?
The intro video provides a concise explanation of the exponential decision space, the combinatorial logic behind 2N project combinations, and the systemic limitations of traditional prioritisation methods. It lays the conceptual foundation for everything that follows.
Only then do we recommend watching the technical deep dive video. It shows in detail how projects are formally modelled, constraints are mathematically integrated, and optimal combinations are algorithmically calculated. The deep dive builds on the content of the intro.
Sources: From structural analysis to practical urban or municipal portfolio application
The distortion mechanisms described are not theoretical constructs.
They operate in real investment and infrastructure portfolios – in energy projects, research programmes, municipal infrastructure measures, asset management, IT security initiatives or investment decisions.
Regardless of industry or public sector, a recurring pattern emerges :
- Qualitative and quantitative factors are evaluated
- Individual projects are ranked in isolation
- Real constraints are only considered downstream
- The optimal combination under restrictions remains uncalculated
This is precisely where the structural bottleneck arises
:
Not in a lack of data – but in the lack of decision-making architecture.
Scientific basis of decision-making architecture
Behavioural economics and decision-making research
Daniel Kahneman (2002 Nobel Prize in Economic Sciences)
Integration of psychological findings into economics and evidence of systematic decision-making biases.
Nobel Prize – Daniel Kahneman
Richard H. Thaler (2017 Nobel Prize in Economic Sciences)
Foundations of behavioural economics and analysis of reproducible decision-making errors.
Nobel Prize – Richard Thaler
Robert J. Shiller (2013 Nobel Prize in Economic Sciences)
Analysis of irrational market decisions and structural misvaluations.
Nobel Prize – Robert J. Shiller
Tversky & Kahneman (1974)
Judgment under Uncertainty: Heuristics and Biases – Fundamental work on the systematic distortion of human decisions.
Science Journal – Heuristics and Biases
Capital allocation, corporate finance and escalation mechanisms
Barberis & Thaler (2003)
A Survey of Behavioural Finance – Overview of behavioural economic effects in financial markets.
NBER Working Paper
Harvard Business Review – Escalation of Commitment
Analysis of organisational escalation mechanisms in investment decisions.
Harvard Business Review
Algorithm aversion and AI acceptance in decision-making processes
Dr Bob Hutchins
7 Reasons People Resist AI—And How We Overcome Them
LinkedIn
CFO Dive – AI and Decision Making in Finance
Analysis of the challenges and acceptance of AI in financial decisions.
Top 5 AI adoption challenges facing CFOs in 2026
Contextual classification
The sources listed here form the scientific basis for the analysis of decision quality, cognitive biases and structural mechanisms in investment and portfolio decisions.
The decision-making architecture presented here builds on these established research findings and translates them into a formal, combinatorial model of complex capital allocation processes under real-world constraints.
We calculate the city budget ex ante – before any decisions are made
.
Municipal decisions should not only be evaluated after the fact. The optimal starting point before the political decision is made is crucial. By simultaneously taking into account budget constraints, CO₂ targets, capacities, funding logic and strategic objectives, the entire decision-making space is systematically analysed.
The result is a transparent, reproducible and mathematically sound prioritisation of all investment options – as a reliable basis for decision-making for the administration, treasurer and city council.