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Updated: Aug 30, 2025

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
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How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

Fabrizio Albanito1, David McBey1, Matthew Harrison2

  • 1Institute of Biological and Environmental Sciences, School of Biological Science, University of Aberdeen, 23 Street Machar Drive, Aberdeen AB24 3UU, U.K.

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Summary

Modeler experience and approach significantly impact ensemble study results, introducing uncertainty. Understanding human factors is crucial for reliable model calibration and validation in biogeochemical research.

Keywords:
AgMIPbiogeochemical modelsclimate changegreenhouse gasesmodel calibrationmodel ensemblesmodel intercomparisonmulti-criteria decision-makingsoil carbon

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Area of Science:

  • Environmental modeling
  • Computational science
  • Ecology

Background:

  • Model ensemble studies are vital for understanding complex environmental systems.
  • Uncertainty in these studies often stems from modeler experience and calibration approaches, not just model complexity.
  • Human and social attributes are increasingly recognized as critical factors in modeling outcomes.

Purpose of the Study:

  • To investigate the decision-making rationale of modelers during ensemble calibration using a multi-criteria approach.
  • To identify how modeler experience and access to data influence the calibration of biogeochemical models.
  • To assess the impact of subjective weighting and cognitive biases on model intercomparison studies.

Main Methods:

  • Applied a multi-criteria decision-making method to analyze modeler rationale.
  • Compared 12 process-based biogeochemical models across five successive calibration stages.
  • Analyzed the importance attributed to input variables by modelers based on model type and experience level.

Main Results:

  • Modelers agreed on initial calibration variables but showed inconsistency in judging variable importance across stages.
  • Subjective weighting of calibration data decreased as more variables were provided.
  • Perceived importance of variables like fertilization, irrigation, soil properties, and initial carbon/nitrogen stocks differed by model type.
  • Importance of variables like experimental duration and ecosystem exchange varied significantly with modeler experience.

Conclusions:

  • Gradual data access across calibration stages can negatively influence interpretation consistency and introduce cognitive bias.
  • Human factors, including modeler assumptions and perceptions of parameter importance, are as critical as numerical details for quality model calibration.
  • Overlooking human and social attributes in modeling and model intercomparison studies leads to critical gaps in understanding outcomes.