Political Normality, Hidden Degradation, and the Drivers of Change
Igor Trubin | October 2026
Introduction: Is Normality a Political Concept?
Recently, while discussing my ongoing book project on the Area of Normal Functioning (ANF) with my friend Eric, an unexpected question emerged: Can the concept of normality also be applied to politics?
After years of studying normal functioning in industrial robotics, computer performance, and more recently human and organizational systems, I had not seriously considered political science as another possible application of ANF.
Eric introduced me to the Overton Window, a concept developed by Joseph P. Overton in the 1990s. It describes the range of political ideas and policies that are considered acceptable or politically feasible at a particular time.
The boundaries of this window are not fixed. They may shift, expand, or contract as public attitudes and social expectations evolve.
That immediately reminded me of ANF.
Our subsequent discussions raised two additional questions:
What happens when the boundaries of normality change in a way that conceals a gradual deterioration in system performance or behavior?
Can we identify the independent factors—or drivers—associated with such changes and use them to forecast future shifts?
These questions connect political normality with my earlier work on statistical exception detection, trend analysis, and business-driver modeling.
This article explores those connections as possible directions for further research.
1. The Overton Window: Political Normality Is Dynamic
The Overton Window describes the range of policy ideas that political decision-makers can support without unacceptable political risk.
An idea considered politically impossible today may eventually become mainstream. Conversely, a previously acceptable policy may move outside the range of politically feasible options.
The window may move in different directions, and its width may change.
This resembles the basic idea behind the Area of Normal Functioning.
In ANF, normality is not necessarily a fixed condition. It is a region whose boundaries may evolve as system behavior, workload, environment, or other operating conditions change.
The important question is therefore not only what is normal today, but how the boundaries of normality change over time.
However, an important distinction must be recognized.
The Overton Window concerns political acceptability and feasibility, while ANF was originally developed to describe the operating conditions under which a technical system functions normally.
These ideas are related but not identical. Their connection requires careful investigation rather than a simple transfer of terminology.
2. Similar Concepts in Other Disciplines
Eric also shared three interesting analogies from ecology, psychology, and safety engineering.
Shifting Baseline Syndrome — Ecology
In 1995, Daniel Pauly described how successive generations of fisheries researchers could accept increasingly depleted fish populations as their normal reference conditions.
Each generation begins with a different baseline, potentially losing sight of earlier environmental conditions.
As a result, long-term degradation may become less visible.
Adaptation-Level Theory — Psychology
Harry Helson's Adaptation-Level Theory describes how human judgments depend on reference levels shaped by previous experiences.
Something initially perceived as unusual may eventually become ordinary as people adapt.
The reference point used to define normality changes.
Normalization of Deviance — Safety Engineering
Diane Vaughan's research on the 1986 Space Shuttle Challenger disaster examined how repeated departures from established standards could gradually become accepted within an organization.
When deviations repeatedly occur without immediate failure, they may begin to appear safe or routine.
Eventually, exceptional behavior can become standard practice even when the underlying risk remains.
These examples illustrate different mechanisms through which normality changes:
In ecology, the historical baseline changes.
In psychology, the perceptual reference level changes.
In organizations, the boundaries of tolerated behavior change.
In politics, the boundaries of acceptable policies and practices change.
The common feature is a moving reference point.
Could these processes be studied through a common analytical framework?
3. From the Overton Window to Political ANF
In my earlier research, the Area of Normal Functioning was represented as a region bounded by lower and upper limits.
For a measurable variable X:
ANF_X(t) = [L_X(t), U_X(t)]
Here, L and U represent the lower and upper boundaries of normal functioning at time t.
Rather than being fixed thresholds, these boundaries can be estimated from observations and updated as the system evolves.
In subsequent work on the Statistical Exception and Trend Detection System (SETDS), related methods were used to detect exceptions, identify trends and change points, and distinguish isolated anomalies from persistent changes in system behavior.
Could a similar approach help study political normality?
Consider a specific political issue whose policy alternatives can reasonably be represented along a numerical scale.
Repeated surveys might allow researchers to estimate the range of positions considered acceptable at different times.
A preliminary representation could be:
ANF_P(t) = [L_P(t), U_P(t)]
where the boundaries describe an estimated range of political acceptability for issue P.
This is a proposed adaptation of ANF, not an established political-science measurement method.
Its practical usefulness depends on the data, the definition of acceptability, and whether an interval is an adequate representation.
Importantly, political scientists have already developed quantitative approaches to policy acceptability.
For example, Matthew Jenkins and Daniel Gomez, in Unveiling the Multidimensional Nature of Policy Acceptability: A Cross-National Approach (first published in 2025), distinguish political, affective, and deliberative dimensions of policy acceptability.
Such work could provide a valuable measurement foundation for investigating how acceptance changes over time.
ANF's potential contribution would not be to invent a measurement of political acceptability, but to investigate its changing boundaries, exceptions, structural transitions, and possible future trajectories.
4. Detecting Changes in Political Normality
In computer performance monitoring, a system may remain within a relatively stable operating region for an extended period.
Then something changes.
A new workload, software release, hardware modification, or environmental condition may produce a gradual trend or a sudden shift.
SETDS and IT-Control Charts were developed to identify statistical exceptions and changing behavior in such data.
Political processes may exhibit analogous statistical patterns.
Imagine repeated surveys concerning the public acceptability of AI-assisted government services.
Initially, acceptance is limited. Over several years, it may increase gradually or change abruptly following technological or institutional developments.
The data could reveal several distinct phenomena:
Stable periods: Public attitudes fluctuate within a relatively consistent range.
Exceptions: Individual observations differ significantly from the established statistical pattern.
Trends: Acceptance increases or decreases persistently.
Change points: The statistical properties of the time series change.
Boundary migration: The estimated region of acceptable positions moves.
Boundary expansion or contraction: The range of politically acceptable alternatives becomes wider or narrower.
These processes resemble those investigated in technical performance analysis.
There is also a connection to Punctuated Equilibrium Theory, associated with Frank Baumgartner and Bryan Jones, which examines periods of relative policy stability interrupted by substantial change.
ANF might complement such research through explicit estimation and monitoring of dynamic boundaries.
5. When the New Normal Hides Degradation
This connection leads to a particularly important question, extending far beyond politics:
What happens when a system adapts to deteriorating conditions and begins treating them as normal?
Consider a computer application whose response time gradually increases.
An adaptive statistical monitoring system may detect the trend and eventually establish a new baseline.
The application could then appear statistically stable again.
However, its response time may now exceed the service level required by the business.
In other words, the system has established a new statistical normal without necessarily restoring acceptable performance.
The same problem can arise elsewhere.
In ecology, the baseline used to evaluate environmental conditions may gradually shift toward a degraded state.
In organizational management, recurring procedural violations may become tolerated because they have not yet produced visible consequences.
In politics, repeated behavior may become increasingly familiar or accepted even when its relationship to established institutional standards has not changed.
This suggests the importance of distinguishing two kinds of boundaries.
Observed ANF
The region estimated from the system's actual measured behavior.
It describes what is statistically typical under observed conditions.
Reference operating limits
The region defined by independent requirements, safety criteria, experimental results, service-level objectives, or other externally justified standards.
These limits describe what the system is expected or required to achieve.
Unlike the observed ANF, reference limits should not automatically move merely because the observed behavior changes.
They may themselves require revision, but such revision should be based on independent justification.
The difference between these regions can provide useful evidence of possible degradation.
It is important to emphasize that a moving ANF does not by itself prove deterioration. A shift may reflect adaptation, improvement, environmental change, or a new operating mode.
Degradation requires an independent definition of what constitutes an undesirable outcome.
This leads to a potentially general principle:
Statistical stability is not the same as healthy functioning.
I believe this distinction may be relevant to many dynamic systems within the broader scope of ANF.
6. Why Normality Changes: From SETDS to Model Factory
So far, we have considered how to identify changes in normal behavior and how to distinguish adaptation from possible degradation.
But another question remains:
Why do the boundaries of normality move?
This question brings us back to an important component of my earlier SETDS research.
In a 2018 blog post on anomaly and novelty detection, I described three related levels of SETDS.
The first detects statistical anomalies and outliers.
The second uses trend and change-point analysis to identify persistent changes that may indicate a new operating regime.
The third relates the observed performance data to external demand indicators, or business drivers, to develop statistically supported forecasting models.
I implemented this modeling direction in an approach called Model Factory.
Related methods are also described in my work on computer resource modeling, including US Patent 10,437,697, Systems and Methods for Modeling Computer Resource Metrics, on which I am a co-inventor.
That patent describes automated construction and evaluation of regression models connecting resource utilization with business transaction volumes, including statistical diagnostics, model selection, and forecasting.
The key methodological principle is that the behavior of a system should be analyzed in relation to measurable external demand rather than exclusively as an isolated time series.
Could this idea be extended beyond computer systems?
Political normality and independent drivers
Suppose we observe a persistent change in public acceptance of a particular policy.
Instead of merely noting that acceptance changed around a certain historical event, we could examine whether the change is statistically associated with independently measured factors, such as:
economic conditions;
adoption of relevant technologies;
levels of institutional trust;
changes in demographic composition;
exposure to information or public discussion.
The purpose would be to identify measurable relationships and determine whether they improve explanation and forecasting.
For example, the adoption of AI-assisted government services might be examined alongside measurements of technological familiarity, direct experience with such services, and institutional trust.
The selected drivers would need to be defined and measured independently of the outcome under investigation.
Statistical validation would be essential.
It would include examining model fit, stability, time lags, competing explanations, and forecasting performance using observations not employed in model construction.
And an important limitation remains:
Statistical correlation does not automatically establish causation.
Political processes are complex, and relationships may be affected by hidden variables, feedback effects, and changing measurement conditions.
Nevertheless, driver-based modeling could provide a more systematic approach than simply associating observed changes with historical events.
7. A Driver-Conditioned ANF
The preceding discussion suggests a further possibility.
In the simplest representation, ANF depends on time:
ANF_X(t) = [L_X(t), U_X(t)]
But some boundaries may also depend on external operating conditions.
We could therefore explore a conditional representation:
ANF_X(t | B(t))
where B(t) is a vector of measured external drivers.
This notation means that the estimated normal region for X depends on the values of relevant drivers at time t.
For example, normal computer CPU utilization may depend on transaction demand.
Normal physiological measurements may depend on activity level, age, and environmental conditions.
Normal organizational workload may depend on staffing, incoming demand, and operational complexity.
Likewise, observed political attitudes might depend on economic conditions, technology adoption, and other measurable factors.
A useful model could distinguish between changes explained by measured drivers and changes that remain unexpected even after accounting for those drivers.
However, two different modeling tasks must be kept separate:
Explaining or forecasting the observed value of a metric using independent drivers.
Estimating whether the boundaries of its normal operating region also change with those drivers.
The first is closely related to established Model Factory methods.
The second is a proposed ANF extension requiring additional mathematical formulation and experimental verification.
Together, they offer a possible path from exception detection to more explanatory and predictive modeling of dynamic normality.
8. What If There Is No Single Political Normal?
Political normality introduces another challenge: different groups may have different definitions of what is acceptable.
A single average can conceal substantial differences among populations.
Consequently, a political ANF may need to be multidimensional or represented by several distinct regions.
Relevant dimensions might include:
political issue;
population group;
geographic region;
type of acceptability;
time and external conditions.
For strongly polarized or multimodal distributions, one interval with lower and upper boundaries may be misleading.
In my earlier work on exception-based modeling, the normal functioning region could be represented in a multidimensional space, with different projections used for analysis.
That perspective may be useful here as well.
The challenge is not necessarily to find one universal political norm, but to characterize the structure and evolution of multiple observed normal regions.
9. Testing Political ANF with Real Data
Fortunately, political science provides historical datasets that could support exploratory experiments.
One possibility is Bright Line Watch, which publishes surveys of experts and the public concerning democratic principles and institutional performance.
A research experiment could be organized into five stages.
Stage 1 — Measurement
Choose a clearly defined indicator measured consistently across time. Determine whether it represents acceptability, preference, institutional performance, or another construct.
Stage 2 — Dynamic ANF estimation
Construct the time series, estimate the normal operating region, and detect exceptions, trends, and change points.
Stage 3 — Degradation assessment
Compare the observed ANF with independently defined criteria or preserved historical reference conditions where justified.
This step is essential when the research question concerns possible deterioration rather than merely statistical change.
Stage 4 — Driver analysis
Identify independently measured candidate drivers and test their statistical relationships with the observed metric or ANF boundaries.
Model Factory principles could guide automated model selection, validation, and forecasting.
Stage 5 — Evaluation
Compare the results against conventional statistical methods and existing political-science interpretations.
Account for uncertainty, survey sampling, question wording, time-series dependence, and possible feedback between variables.
An important practical limitation is that some political surveys are conducted only periodically. Their sampling frequency may be insufficient to identify short-lived exceptions or accurately estimate certain change points.
Such an experiment would not establish a universal theory of political normality.
It could, however, help determine whether ANF and SETDS offer a useful complementary perspective.
10. What This Means for the ANF Book
My ongoing book project explores extending the Area of Normal Functioning from its origins in industrial robotics to technical, AI, biological, human, organizational, and other dynamic systems.
Political normality was not part of my original plan.
Yet the discussion with Eric has suggested several broader research directions.
The first is the relationship between adaptation and degradation.
A dynamic system may adjust to changing conditions and establish a new stable operating region. But adaptation alone does not guarantee that the new state is desirable or safe.
The second is the relationship between normality and its drivers.
Understanding how a system changes is only part of the problem. Identifying the independent factors associated with its behavior may help explain those changes and forecast future developments.
These topics are sufficiently general to deserve separate treatment in the book, extending beyond politics.
For now, political ANF remains an exploratory concept. Its scientific usefulness must be established through careful definitions, appropriate measurements, and empirical comparison with existing methods.
But the discussion raises three questions that seem increasingly central to the broader ANF project:
How do we detect when normality changes?
How do we recognize when a new normal conceals degradation?
And can we identify the drivers that explain and help predict those changes?
These questions connect industrial robotics, computing, organizational management, human behavior, and now political science.
They also bring me back to a possible title for the book:
Everything Is Normal… Until It Isn't.
Perhaps the most intriguing possibility is that the system may continue to appear normal—even after it has begun moving toward failure.
References and Further Reading
Mackinac Center for Public Policy. The Overton Window of Political Possibility.
Pauly, D. (1995). Anecdotes and the Shifting Baseline Syndrome of Fisheries. Trends in Ecology & Evolution, 10(10), 430.
Helson, H. (1948). Adaptation-Level as a Basis for a Quantitative Theory of Frames of Reference. Psychological Review, 55(6), 297–313.
Vaughan, D. (1996). The Challenger Launch Decision: Risky Technology, Culture, and Deviance at NASA. University of Chicago Press.
Baumgartner, F. R., & Jones, B. D. (1993). Agendas and Instability in American Politics. University of Chicago Press.
Jenkins, M., & Gomez, D. (2025, online; 2026 journal issue). Unveiling the Multidimensional Nature of Policy Acceptability: A Cross-National Approach. Policy Studies Journal, 54(1).
Helmke, G., & Rath, J. (2025). Defining and Measuring Democratic Norms. Annual Review of Political Science, 28, 233–251.
Trubin, I. (2018). Anomaly Detection vs. Novelty Detection: SETDS Method Detects and Separates Both. System Management by Exception.
Trubin, I., Schutt, M., & Robinson, J. (2019). Systems and Methods for Modeling Computer Resource Metrics. US Patent 10,437,697.
Trubin, I. (2026). Starting a Book Project: The Area of Normal Functioning.
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