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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Rational disagreements in phylogenetics.

Fabrizzio Guerrero Mc Manus1

  • 1Graduate Program in Philosophy of Science, Instituto de Investigaciones Filosóficas, UNAM, Mexico City, Mexico. FabrizzioMc@yahoo.com

Acta Biotheoretica
|February 21, 2009
PubMed
Summary
This summary is machine-generated.

Choosing between maximum likelihood (ML) and maximum parsimony (MP) algorithms for phylogenetic inference involves contextual values. A Kuhnian approach suggests these methods aid rational tree selection but don't resolve the core controversy.

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Area of Science:

  • Evolutionary Biology
  • Philosophy of Science
  • Computational Biology

Background:

  • Phylogenetic inference relies on algorithms like maximum likelihood (ML) and maximum parsimony (MP).
  • A long-standing debate exists regarding the rational choice between ML and MP methods.
  • Understanding algorithm choice is crucial for advancing evolutionary studies.

Purpose of the Study:

  • To reframe the ML vs. MP controversy within the philosophical context of theory choice.
  • To propose a Kuhnian framework using 'Styles of Modeling' to analyze algorithm selection.
  • To explore the sociological and epistemological dimensions of choosing phylogenetic algorithms.

Main Methods:

  • Application of a Kuhnian philosophy of science framework.
  • Introduction of the 'Style of Modeling' concept with four distinct stages.
  • Analysis of the values and normative force associated with ML and MP algorithms.

Main Results:

  • The choice between ML and MP is context-dependent and value-laden.
  • A 'Style of Modeling' framework provides a structured approach to algorithm selection.
  • ML and MP algorithms offer contextual justification and therapeutic value in tree selection.

Conclusions:

  • The ML and MP debate cannot be definitively resolved by the algorithms themselves.
  • Rational algorithm choice in phylogenetics is bounded and influenced by specific scientific values.
  • The 'Style of Modeling' approach offers insights into scientific communities and epistemological practices.