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Generalizing Contextual Analysis.

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  • 1Department of Philosophy, Unit for the History and Philosophy of Science, Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia. p.bourrat@gmail.com.

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Summary
This summary is machine-generated.

This study challenges Okasha's view on evolutionary multilevel selection. It argues that the hierarchical Price approach and Okasha's contextual/neighbour approaches are inadequate for soft selection, proposing hierarchical linear models as a superior causal decomposition method.

Keywords:
MLS1MLS2Multilevel analysisPrice equationSoft selection

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Area of Science:

  • Evolutionary biology
  • Causal inference
  • Multilevel selection theory

Background:

  • Okasha proposed contextual analysis and neighbour approaches as superior to the Price approach for multilevel selection.
  • Okasha argued the Price approach is better for soft selection, a claim this paper disputes.

Purpose of the Study:

  • To demonstrate flaws in Okasha's arguments regarding soft selection and the Price equation.
  • To propose and defend hierarchical linear models as a more adequate causal decomposition for multilevel selection, especially in soft selection scenarios.

Main Methods:

  • Critically analyze Okasha's arguments and the limitations of the Price equation, contextual analysis, and neighbour approaches.
  • Introduce and defend hierarchical linear models as a superior causal decomposition framework.
  • Distinguish between multilevel selection 1 and multilevel selection 2 to resolve apparent contradictions.

Main Results:

  • The Price equation, contextual analysis, and neighbour partitionings are inadequate causal decompositions for soft selection.
  • Hierarchical linear models overcome the limitations of other approaches, including determinism violations and failure to detect cross-level by-products.
  • Segregation distortion cases, when properly analyzed within multilevel selection 2, do not invalidate the hierarchical linear model.

Conclusions:

  • Hierarchical linear models offer a more robust causal decomposition for multilevel selection than previously proposed methods.
  • The Price equation and Okasha's preferred methods fail to adequately capture causal dynamics in soft selection.
  • A refined understanding of multilevel selection, distinguishing between MLS1 and MLS2, is crucial for accurate causal analysis.