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This summary is machine-generated.

Robustness analysis of minimal models can confirm hypotheses, but its effectiveness depends on specific contexts. This Bayesian approach clarifies the epistemic value of minimal models in scientific confirmation.

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Agent-based modelsConfirmationMinimal modelsRobustness analysisStylized facts of financeVariety of evidence

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Area of Science:

  • Philosophy of Science
  • Epistemology
  • Scientific Modeling

Background:

  • Minimal models are debated for their epistemic value.
  • Robustness analysis is proposed for hypothesis confirmation but faces resistance.
  • Bayesian frameworks offer a potential rationalization for robustness analysis.

Purpose of the Study:

  • To provide a Bayesian rationalization for robustness analysis in confirming hypotheses.
  • To explore the confirmatory potential of minimal models.
  • To identify conditions under which robustness analysis is detrimental to confirmation.

Main Methods:

  • Bayesian rationalization of robustness analysis.
  • Case study from macroeconomics.
  • Analysis of evidential variety.

Main Results:

  • Robustness analysis over minimal models can indeed confirm hypotheses.
  • The confirmatory value is context-dependent.
  • Specific cases where robustness analysis hinders confirmation were identified and linked to evidential variety.

Conclusions:

  • Robustness analysis, when applied to minimal models, can serve a confirmatory role.
  • The epistemic benefits of robustness analysis are contingent on specific circumstances.
  • Understanding evidential variety is crucial for assessing the impact of robustness analysis.