What question did this study set out to answer?

The aim is to address cognitive biases affecting data-driven asset management and statistical interpretations.

April 29, 2026Open Access

Micro-Macro Modeling of Inherent Cognitive Biases in 5-Point Likert Scales: Uncovering the Non-Linearity of Critical Sample Sizes for Capturing Identical Statistical Populations

Key Points

The aim is to address cognitive biases affecting data-driven asset management and statistical interpretations.
Develops a micro-macro link model using the Analytic Hierarchy Process and Softmax function.
Conducts large-scale multi-agent simulations with N=107 to validate model convergence.
Analyzes the impact of peer pressure on statistical distance metrics and cognitive conflicts.
Demonstrates that Kullback-Leibler divergence may overlook critical deterioration signals due to cognitive biases.
Finds that subjective evaluations can structurally marginalize accurate statistical insights.
Advocates for a transition to quantitative methods to ensure objective analysis of statistical populations.

Abstract

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations (N=107) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size.

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