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Updated: Aug 5, 2026
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Published on: November 2, 2012
Knowing What We Don't Know: Model-Based Uncertainty Decomposition for Categorical Sequences
Marc A Scott1, Fulvia Pennoni2, Ignacio Bórquez3
1Department of Applied Statistics, Social Science, and Humanities, New York University, New York, NY 10003, USA.
Entropy (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces novel uncertainty measures for analyzing life course sequences using Markov chain models. These methods help understand and predict individual pathways by quantifying uncertainty in dynamic processes.
Area of Science:
- Social Sciences
- Statistics
- Longitudinal Data Analysis
Background:
- State sequence analysis examines life course pathways using longitudinal categorical data.
- Characterizing dynamic features of sequences is crucial for descriptive, associative, and predictive purposes.
- Entropy, an information-theoretic measure, quantifies uncertainty in time-series data.
Purpose of the Study:
- To customize entropy measures for assessing uncertainty in dynamic processes, specifically 'what happens next', conditional on past states.
- To develop complementary measures evaluating model predictive power within the context of uncertainty.
- To explore how stratification by demographics and clustering enhances the narrative of social processes.
Main Methods:
- Utilized Markov chain models for nominal state sequences.
- Developed multiple uncertainty measures based on customized entropy.
- Applied complementary measures to assess predictive power and model uncertainty.
- Considered data stratification through demographics and clustering.
Main Results:
- Established customized entropy measures to quantify uncertainty in state sequences.
- Developed methods to adjust expectations for individual-specific differences and historical information.
- Demonstrated how to summarize and contrast changes in uncertainty across different models.
- Showcased how stratification by demographics and clustering enriches the analysis of social processes.
Conclusions:
- The developed uncertainty measures provide a nuanced understanding of dynamic processes in life course data.
- These methods allow for a more accurate assessment of predictive power, accounting for inherent uncertainty.
- Stratification techniques offer a more complete narrative of complex social dynamics.
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