Advertisement

Biology & Philosophy

, 34:48 | Cite as

Graphical causal models of social adaptation and Hamilton’s rule

  • Wes AndersonEmail author
Article
  • 122 Downloads

Abstract

Part of Allen et al.’s criticism of Hamilton’s rule makes sense only if we are interested in social adaptation rather than merely social selection. Under the assumption that we are interested in casually modeling social adaptation, I illustrate how graphical causal models of social adaptation can be useful for predicting evolution by adaptation. I then argue for two consequences of this approach given some of the recent philosophical literature. I argue Birch’s claim that the proper way to understand Hamilton’s rule is as providing an organizational framework for causal models is incorrect. I provide an account of a causally adequate decomposition of evolutionary change due to social adaptation and show that my account is superior to Okasha’s.

Keywords

Graphical causal models Hamilton’s rule Path-specific effects Intervention calculi Causally adequate decompositions Social adaptation 

Notes

Acknowledgements

I express my gratitude to Jonathan Birch, Valerie Racine, and an anonymous referee. The paper significantly improved due to their comments on previous drafts.

References

  1. Allen B, Nowak M, Wilson E (2013) Limitations of inclusive fitness. PNAS 110:20,135–20,139CrossRefGoogle Scholar
  2. Birch J (2017) The philosophy of social evolution. Oxford University Press, OxfordCrossRefGoogle Scholar
  3. Crespi B, Bookstein F (1989) A path-analytic model for the measurement of selection on morphology. Evolution 43:18–28CrossRefGoogle Scholar
  4. Hamilton W (1964) The genetic evolution of social behavior. J Theor Biol 7:1–52CrossRefGoogle Scholar
  5. Malinsky D, Shpitser I, Richardson T (2019) A potential outcomes calculus for identifying conditional path-specific effects. In: Proceedings of the 22nd international conference on artificial intelligence and statistics (AISTATS)Google Scholar
  6. Okasha S (2006) Evolution and the levels of selection. Oxford University Press, OxfordCrossRefGoogle Scholar
  7. Okasha S (2015) The relation between kin and multilevel selection: an approach using causal graphs. Br J Philos Sci 67:1–36Google Scholar
  8. Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press, CambridgeGoogle Scholar
  9. Pearl J (2001) Direct and indirect effects. In: Breese J, Koller D (eds) Proceedings of the 17th conference on uncertainty in artificial intelligence (UAI-01). Morgan Kaufmann, Burlington, pp 411–420Google Scholar
  10. Price G (1970) Selection and covariance. Nature 227:520–521CrossRefGoogle Scholar
  11. Robins J, Greenland S (1992) Identifiability and exchangeability for direct and indirect effects. Epidemiology 3:143–155CrossRefGoogle Scholar
  12. Robins J, Richardson T (2010) Alternative graphical causal models and the identification of direct effects. In: Shrout P (ed) Causality and psychopathology. Oxford University Press, Oxford, pp 103–158Google Scholar
  13. Sober E (1984) The nature of selection. University of Chicago Press, ChicagoGoogle Scholar
  14. Sober E, Wilson D (1998) Unto others: the evolution and psychology of unselfish behavior. Harvard University Press, CambridgeGoogle Scholar
  15. Wolf J, Brodie E III, Cheverud J, Moore A, Wade M (1998) Evolutionary consequences of indirect genetic effects. Trends Ecol Evol 13:64–69CrossRefGoogle Scholar
  16. Wright S (1934) The method of path coefficients. Ann Math Stat 5:161–215CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Western New England UniversitySpringfieldUSA

Personalised recommendations