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Updated: Jan 10, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Endogenous conflict and the limits of predictive optimization.

Thomas Chadefaux1, Thomas Schincariol1

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Summary

Simple autoregressive models, predicting future political violence from past events, outperform complex machine learning approaches. This suggests internal feedback, not structural conditions, drives conflict dynamics, advocating for simpler forecasting strategies.

Keywords:
AutoregressionConflict forecastingPolitical violencePredictive modelingTemporal dependence

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Area of Science:

  • Political Science
  • Computational Social Science
  • Conflict Studies

Background:

  • Forecasting political violence often uses complex machine learning with high-dimensional data.
  • However, simpler autoregressive models, using only past outcomes, frequently yield more reliable conflict predictions.
  • This highlights a discrepancy between common practice and effective forecasting in political violence research.

Purpose of the Study:

  • To argue that autoregressive models are theoretically appropriate for forecasting in sparse, dynamic environments like armed conflict.
  • To demonstrate the empirical performance of autoregressive models against more complex alternatives.
  • To provide a theoretical explanation for why simpler models excel in predicting political violence.

Main Methods:

  • Comparative analysis of forecasting model performance across multiple countries and specifications.
  • Empirical testing of autoregressive models against complex machine learning and structural covariate models.
  • Theoretical modeling to explain the dynamics of political violence and its predictability.

Main Results:

  • Autoregressive models consistently matched or outperformed more complex forecasting models.
  • Inclusion of structural covariates often added little predictive value or degraded model performance.
  • Empirical evidence suggests conflict dynamics are driven by internal feedback, burstiness, and short-term adaptation.

Conclusions:

  • Autoregression is a theoretically sound and empirically validated strategy for forecasting political violence in high-entropy systems.
  • Complex models may be less effective due to the inherent nature of conflict dynamics, which are not solely driven by slow-changing structural conditions.
  • The study advocates for epistemic modesty in prediction, favoring principled, simpler strategies like autoregression in unpredictable environments.