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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Comparing data assimilation and likelihood-based inference on latent state estimation in agent-based models.

Blas Kolic1, Corrado Monti2, Gianmarco De Francisci Morales3

  • 1Institute of Big Data (IBiDat), Universidad Carlos III de Madrid, Ronda de Toledo, 1, Madrid 28005, Spain.

PNAS Nexus
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

Data assimilation (DA) and likelihood-based inference (LBI) were compared for agent-based models (ABMs). LBI offers superior agent-level opinion recovery and forecasts, while DA excels at aggregate predictions.

Keywords:
ABMdata assimilationinferencelikelihood-based inferenceprobabilistic modeling

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Area of Science:

  • Computational Social Science
  • Complex Systems Modeling
  • Statistical Inference

Background:

  • Agent-based models (ABMs) simulate complex systems using individual agents with evolving states.
  • Estimating latent states in ABMs is crucial for aligning simulations with real-world data.
  • Traditional data assimilation (DA) methods face challenges with ABM characteristics.

Purpose of the Study:

  • To systematically compare data assimilation (DA) and likelihood-based inference (LBI) for agent-based models (ABMs).
  • To evaluate the performance of DA and LBI in recovering latent states and improving forecasts.
  • To determine the suitability of each method for agent-level versus aggregate predictions.

Main Methods:

  • Comparison of DA and LBI on the bounded-confidence model, an opinion dynamics ABM.
  • Assessment of state estimation accuracy and forecast performance at individual and aggregate levels.
  • Investigation of method performance under model misspecification.

Main Results:

  • Likelihood-based inference (LBI) demonstrated superior recovery of latent agent opinions, even with model misspecification.
  • LBI led to improved individual-level forecasts compared to data assimilation (DA).
  • DA and LBI performed comparably at the aggregate level, with DA showing competitiveness under specific parameters.

Conclusions:

  • LBI is preferable for agent-level inference and individual-level forecasting in ABMs.
  • DA is well-suited for aggregate predictions and remains competitive across aggregation levels.
  • The choice between DA and LBI depends on the specific inference goals and model characteristics.