Skip to main content
SpringerLink
Log in

By genes alone: a model selectionist argument for genetical explanations of cooperation in non-human organisms

  • Published:
Biology & Philosophy Aims and scope Submit manuscript

Abstract

I distinguish two versions of kin selection theory—a purely genetic version (GKST) and a version that also appeals to cultural (i.e. non-genetically-derived) forms of cooperation (WKST)—and present an argument in favor of using the former when it comes to accounting for the evolution of cooperation in non-human organisms. Specifically, I first show that both GKST and WKST are equally mathematically coherent—they can both be derived from the Price equation—but not necessarily equally empirically plausible, as they are based on different assumptions about the inheritance system underlying the cooperative phenotype. Given this, I then, second, present a model selection theoretic argument in favor of GKST over WKST. This argument is based on the fact that, in non-human cases, the former theory is likely to be as empirically successful as WKST, while containing fewer degrees of freedom. I end by defending both the intrinsic importance of this argument and its relevance to the discussion surrounding the “gene’s eye view of evolution.”

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. For more details about KST and multi-level selection theory (MSLT), see e.g. Gardner et al. (2011), Grafen (1985), Hamilton (1963) (for KST), and Damuth and Heisler (1988), Okasha (2006), Sober and Wilson (1998) (for MLST).

  2. Of course, it is possible that they did not evolve by natural selection. However, given the fact that these traits are quite common (West et al. 2011), and that there are—perhaps despite initial appearances to the contrary—selective explanations available for them, I will not consider this further here. This is not to say that many such traits might not have evolved in ways that do not strongly depend on natural selection; it just means that this is not what is at stake here.

  3. As noted by Frank (1998), there are many possible interpretations of z. The ones in text are just chosen for expositional clarity.

  4. Alternatively, one might conceive of KST as a family of models (Weisberg 2013; Morrison 2015). However, since settling this issue is not central for present purposes, I will not consider it further here.

  5. Some authors (see e.g. Queller 1985, 1992) have argued that, in phenotypic forms of Hamilton’s rule, it is likely that there are “synergistic effects” that need to be taken into account: in particular, the cooperative phenotype may yield a benefit to other cooperators independently of the assortative effect measured by the correlation of genotypes gi and gi’ (e.g. helping a helper may yield benefits that helping a non-helper does not). There are different ways of including this into Hamilton’s rule; for example, Queller (1985) shows that, in some cases, these synergistic effects can be captured by adding an extra term to (1) like this: Δp > 0 if and only if −c + br + dS > 0, where S is a variable that captures the size of the synergistic (non-additive) effects of helping helpers. However, it is controversial to what extent this is unique to the phenotypic case (Queller 1985, 1992, 2011; Birch and Marshall 2014; Marshall 2011; Sober 2000). For this reason, I will not consider synergistic effects further here—noting just that, if synergistic effects do systematically affect WKST more than GKST, this would only favor my argument, as it adds degrees of freedom to the former. See also below.

  6. Of course, it is also possible to define other restricted versions of WKST: for example, one could consider a form of KST that is restricted to cultural inheritance (i.e. qi) only. However, the interest here is in GKST, as this is the theory that makes for an interesting empirical contrast to WKST. See also below.

  7. Note that WKST is a not a purely additive expansion of GKST. However, all that is relevant here is that WKST has more degrees of freedom than GKST; the nature and exact quantity of this increase are not so important—see also note 16.

  8. This marks an important contrast to other restricted forms of WKST: e.g. ones restricted to cultural factors only. The latter would not be empirically equivalent to WKST in non-human cases of the evolution of cooperation.

  9. The one widely studied case of non-human reciprocal altruism—concerning food sharing in vampire bats—is no exception to this either (Carter and Wilkinson 2013; see also Hammerstein 2003), as this does not actually concern genuine evolutionary altruism, and is thus to be explained in a non-cooperative framework (Ramsey and Brandon 2011).

  10. It is also worth noting that cases of mutualism do not fall into the class of cases that cannot be handled well by genetic forms of GKST, as these do not obviously concern the evolution of altruistic cooperation (Wyatt et al. 2013; but see also Frank 1994). Indeed, they may be better handled as cases where a population of organisms adapts to an environment partially constituted by other types of organisms (see also Gardner and West 2010; Godfrey-Smith 2009).

  11. For a treatment of appeals to simplicity in science and philosophy more generally, see Sober (2015).

  12. Some only do so implicitly, though, by being asymptotically equivalent to AIC (Stone 1977; Hitchcock and Sober 2004).

  13. In this way, I also avoid Sober’s (2002) charge that some model selectionist frameworks—like likelihoodism—lack an established epistemic foundation: this may be so, but all that I am claiming here is that, to the extent that likelihoodism is accepted, we have a reason to favor GKST over WKST. Given the wide acceptance of likelihoodism, this is still a sufficiently strong conclusion. At any rate, as will be made clearer below, the argument of this paper does not depend on the acceptance of likelihoodism.

  14. So, formally, one could define y1 = 1 if and only if (–c + b rg) > 0, y1 = 0 otherwise (for GKST), and y2 = 1 if and only if –c + b [a rg + (1 − a) (rq + rgq + rqg)] > − d; y2 = 0 otherwise (for WKST). Then one could generate a (large) data set containing all investigations of the evolution of cooperation, noting for each one the values (or at least estimates) of c, b, rg, rq, rgq, rqg, and d, and adding a variable z coded as 1 if cooperation did evolve in the case in question (or did so easily and quickly), and 0 if not. On this basis, one could then compare how well y1 and y2 match z. A more sophisticated version of such an approach would have yi be graded, to model differences in the ease with which cooperation can evolve.

  15. Note also that, given the randomness inherent in evolutionary processes, there is no doubt that the comparison between GKST and WKST can be seen as a statistical inference problem to begin with. Furthermore, there is also little reason to think that the statistical properties of these evolutionary processes change from application to application (see also Forster and Sober 1994).

  16. Another concern one might have with placing the GKST/WKST comparison in a model selection framework is that these two theories contain no adjustable parameters, in the sense that is that there is no flexibility in how rg, rq, rgq, rqg, etc. are to be related to each other. However, this point does not raise major problems for the present argument either. On the one hand, likelihood ratio tests—for one—apply whether or not the two theories have adjustable parameters in this sense (Burnham and Anderson 2002; Abraham and Ledolter 2006). On the other hand, one can work around this issue by introducing adjustable parameters into the two theories and then applying AIC or BIC (for example). So, one could just compare (GKST*) Δg > 0 if and only if s1 + (−c + b rg) > 0 and (WKST*) Δp > 0 if and only if s1 + [–c + b rg + s2 [b [(a − 1) rg + (1 − a)(s3 rq + s4 rgq + s5 rqg)]]] > − s6d, where s1 is a parameter capturing an overarching measurement error, and s2, s3, s4, s5, and s6 are parameters capturing the weights that should be given to Var(qi), rq, rgq rqg, and E(wiΔqi) respectively. (One could further make s2 to s6 dummy parameters by restricting them to taking on the value of either 0 or 1). It is then possible to let the data determine the best values of s1 to s6. The introduction of these parameters is furthermore made reasonable by the fact that, in actual applications of the two theories, we may need to replace population statistics (like variances and covariances) with their estimates (sample variances or covariances). If this is done, GKST* is nested in WKST*—it has s2 = s3 = s4 = s5 = s6 = 0—and their comparison is a standard model selection problem (Burnham and Anderson 2002).

  17. This is sometimes expressed with the notion of “inclusive fitness”; however, for present purposes, the details of this are not so important. For more on this, see e.g. Maynard Smith (1976), Taylor and Frank (1996), West et al. (2007, 2008), Frank (1998) and Hamilton (1964).

  18. In fact, one could see the comparison between GKST and WKST as related to the dispute as to whether KST, in general, or MLST, in general, should be seen as the key framework with which to approach the evolution of cooperation (for more on this dispute, see e.g. West et al. 2007, 2008; Wilson 2008; Sober and Wilson 1998; Okasha and Martens 2016; Birch and Okasha 2014; Okasha 2015). This is due to the fact that MLST is often stated as emphasizing precisely the importance of non-genetic interactions in the explanation of the evolution of cooperation. So, for example Sober (2000, pp. 110–111), in laying out an MLST-based perspective towards the evolution of cooperation states: “the key to the evolution of altruism is population structure.” In turn, this might lead one to think that, in spirit, MLST is very close to WKST (see e.g. Lehmann et al. 2007). However, laying out and justifying this argument calls for another paper.

References

  • Abraham B, Ledolter J (2006) Introduction to regression modeling, 1st edn. Cengage, Independence

    Google Scholar 

  • Birch J, Marshall JA (2014) Queller’s separation condition explained and defended. Am Nat 184(4):531–540

    Article  Google Scholar 

  • Birch J, Okasha S (2014) Kin selection and its critics. BioScience, Oxford

    Google Scholar 

  • Boyd R, Richerson P (1985) Culture and the evolutionary process. University of Chicago Press, Chicago

    Google Scholar 

  • Boyd R, Richerson P (2005) The origin and evolution of cultures. Oxford University Press, Oxford

    Google Scholar 

  • Bretthorst GL (1996) An introduction to model selection using probability theory as logic. In: Heidbreder G (ed) Maximum entropy and bayesian methods, vol 62. Springer, Amsterdam, pp 1–42

    Google Scholar 

  • Brown SP, West SA, Diggle SP, Griffin AS (2009) Social evolution in micro-organisms and a Trojan horse approach to medical intervention strategies. Philos Trans R Soc Lond B: Biol Sci 364(1533):3157–3168

    Article  Google Scholar 

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New York

    Google Scholar 

  • Carter G, Wilkinson G (2013) Food sharing in vampire bats: reciprocal help predicts donations more than relatedness or harassment. Proc R Soc B 280:20122573

    Article  Google Scholar 

  • Creanza N, Fogarty L, Feldman MW (2012) Models of cultural niche construction with selection and assortive matings. PLoS ONE 7(8):e42744

    Article  Google Scholar 

  • Damuth J, Heisler IL (1988) Alternative formulations of multi-level selection. Biol Philos 3:407–430

    Article  Google Scholar 

  • Dawkins R (1989) The selfish gene, 2nd edn. Oxford University Press, Oxford

    Google Scholar 

  • El Mouden C, Andre JB, Morin O, Nettle D (2014) Cultural transmission and the evolution of human behaviour: a general approach based on the Price equation. J Evol Biol 27(2):231–241

    Article  Google Scholar 

  • Forster M, Sober E (1994) How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions. Br J Philos Sci 45(1):1–35

    Article  Google Scholar 

  • Forster M, Sober E (2011) AIC scores as evidence—a Bayesian interpretation. In: Forster M, Bandyopadhyay PS (eds) The philosophy of statistics. Kluwer, Dordrecht, pp 535–549

    Chapter  Google Scholar 

  • Frank SA (1994) Genetics of mutualism: the evolution of altruism between species. J Theor Biol 170(4):393–400

    Article  Google Scholar 

  • Frank SA (1998) Foundations of social evolution. Princeton University Press, Princeton

    Google Scholar 

  • Frank SA (2012) Natural selection. IV. The Price equation. J Evol Biol 25:1002–1019

    Article  Google Scholar 

  • Gardner A, West SA (2010) Greenbeards. Evolution 64(1):25–38

    Article  Google Scholar 

  • Gardner A, West SA, Wild G (2011) The genetical theory of kin selection. J Evol Biol 24(5):1020–1043

    Article  Google Scholar 

  • Godfrey-Smith P (2009) Darwinian populations and natural selection. Oxford University Press, Oxford

    Book  Google Scholar 

  • Goodman SN, Royall R (1988) Evidence and scientific research. Am J Public Health 78(12):1568–1574

    Article  Google Scholar 

  • Grafen A (1985) A geometric view of relatedness. Oxf Surv Evolut Biol 2:28–90

    Google Scholar 

  • Griffin AS, West SA (2002) Kin selection: fact and fiction. Trends Ecol Evol 17:15–21

    Article  Google Scholar 

  • Hamilton W (1963) The evolution of altruistic behaviour. Am Nat 97:354–356

    Article  Google Scholar 

  • Hamilton W (1964) The genetical theory of social behavior. J Theor Biol 7:1–52

    Article  Google Scholar 

  • Hammerstein P (2003) Why is reciprocity so rare in social animals? a protestant appeal. In: Hammerstein P (ed) Genetic and cultural evolution of cooperation. MIT Press, Cambridge, pp 83–94

    Google Scholar 

  • Henrich J (2015) The secret of our success: how culture is driving human evolution, domesticating our species, and making us smarter. Princeton University Press, Princeton

    Google Scholar 

  • Henrich J, McElreath R (2011) The evolution of cultural evolution. Evol Anthropol 12:123–135

    Article  Google Scholar 

  • Heyes CM, Frith C (2014) The cultural evolution of mind reading. Science 344:1243091

    Article  Google Scholar 

  • Heyes C, Galef BG (eds) (1996) Social learning in animals: the roots of culture. Academic, San Diego

    Google Scholar 

  • Hitchcock C, Sober E (2004) Prediction versus accommodation and the risk of overfitting. Br J Philos Sci 55(1):1–34

    Article  Google Scholar 

  • Hoogland JL (1983) Nepotism and alarm calling in the black-tailed prairie dog (Cynomys ludovicianus). Anim Behav 31(2):472–479

    Article  Google Scholar 

  • Laland KN, Janik VM (2006) The animal cultures debate. Trends Ecol Evol 21:542–547

    Article  Google Scholar 

  • Lehmann L, Keller L, West SA, Roze D (2007) Group selection and kin selection: two concepts but one process. Proc Natl Acad Sci USA 104:6736–6739

    Article  Google Scholar 

  • Lewens T (2015) Cultural evolution: conceptual challenges. Oxford University Press, Oxford

    Book  Google Scholar 

  • Lewontin R (1970) The units of selection. Ann Rev Ecol Systemat 1:1–18

    Article  Google Scholar 

  • Luque VJ (2017) One equation to rule them all: a philosophical analysis of the Price equation. Biol Philos 32:97–125

    Article  Google Scholar 

  • Marshall JAR (2011) Group selection and kin selection: formally equivalent approaches. Trends Ecol Evol 26(7):325–332

    Article  Google Scholar 

  • Maynard Smith J (1976) Group selection. Q Rev Biol 51:277–283

    Article  Google Scholar 

  • Morin O (2016) How traditions live and die. Oxford University Press, Oxford

    Google Scholar 

  • Morrison M (2015) Reconstructing reality: models, mathematics and simulations. Oxford University Press, Oxford

    Book  Google Scholar 

  • Okasha S (2006) Evolution and the levels of selection. Oxford University Press, Oxford

    Book  Google Scholar 

  • Okasha S (2015) The relation between kin and multi-level selection: an approach using causal graphs. Br J Philos Sci 67:435–470

    Article  Google Scholar 

  • Okasha S, Martens J (2016) The causal meaning of Hamilton’s rule. R Soc Open Sci 3:160037

    Article  Google Scholar 

  • Orzack SH, Sober E (1994) Optimality models and the test of adaptationism. Am Nat 143(3):361–380

    Article  Google Scholar 

  • Oyama S (2000) Evolution’s eye. Duke University Press, Durgham

    Book  Google Scholar 

  • Price GR (1970) Selection and covariance. Nature 227:520–521

    Article  Google Scholar 

  • Price GR (1972) Extension of covariance selection mathematics. Ann Hum Genet 35:485–490

    Article  Google Scholar 

  • Queller DC (1985) Kinship, reciprocity and synergism in the evolution of social behavior. Nature 318(28):366–367

    Article  Google Scholar 

  • Queller DC (1992) Quantitative genetics, inclusive fitness and group selection. Am Nat 139:540–558

    Article  Google Scholar 

  • Queller DC (2004) Kinship is relative. Nature 430:975–976

    Article  Google Scholar 

  • Queller DC (2011) Expanded social fitness and Hamilton’s rule for kin, kith, and kind. Proc Natl Acad Sci 108:10792–10799

    Article  Google Scholar 

  • Ramsey G, Brandon R (2011) Why reciprocal altruism is not a kind of group selection. Biol Philos 26:385–400

    Article  Google Scholar 

  • Reader SM, Biro D (2010) Experimental identification of social learning in wild animals. Learn Behav 38(3):265–283

    Article  Google Scholar 

  • Rice SH (2004) Evolutionary theory: mathematical and conceptual foundations. Sinauer Associates, Sunderland

    Google Scholar 

  • Richerson P, Boyd R (2005) Not by genes alone. University of Chicago Press, Chicago

    Google Scholar 

  • Rochefort-Maranda G (2016) Simplicity and model selection. Eur J Philos Sci 6(2):261–279

    Article  Google Scholar 

  • Rosenberg A (2012) Philosophy of social science, 4th edn. Westview Press, Boulder

    Google Scholar 

  • Royall R (1997) Statistical evidence—a likelihood paradigm. Chapman and Hall, Boca Raton

    Google Scholar 

  • Rubin H (2015) Genetic models in evolutionary game theory: the evolution of altruism. Erkenntnis 80(6):1175–1189

    Article  Google Scholar 

  • Sarkar S (2005) Molecular models of life. MIT Press, Cambridge

    Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–465

    Article  Google Scholar 

  • Skyrms B (2010) Signals: evolution, learning, and information. Oxford University Press, Oxford

    Book  Google Scholar 

  • Sober E (1988) Reconstructing the past: parsimony, evolution, and inference. MIT Press, Cambridge

    Google Scholar 

  • Sober E (1990) The poverty of pluralism: a reply to Sterelny and Kitcher. J Philos 87(3):151–158

    Article  Google Scholar 

  • Sober E (1992) Models of cultural evolution. In: Griffiths P (ed) Trees of life, vol 11. Springer, Amsterdam, pp 17–39

    Chapter  Google Scholar 

  • Sober E (2000) Philosophy of biology, 2nd edn. Westview Press, Boulder

    Google Scholar 

  • Sober E (2002) Bayesianism—its scope and limits. In: Swinburne R (ed) Bayes’s theorem. Oxford University Press, Oxford, pp 21–38

    Google Scholar 

  • Sober E (2008) Evidence and evolution. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Sober E (2015) Ockham’s razors: a user’s manual. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Sober E, Wilson DS (1998) Unto others: the evolution and psychology of unselfish behavior. Harvard University Press, Cambridge

    Google Scholar 

  • Sterelny K (2003) Thought in a hostile world: the evolution of human cognition. Wiley-Blackwell, Oxford

    Google Scholar 

  • Sterelny K (2012) The evolved apprentice: how evolution made humans unique. MIT Press, Cambridge

    Book  Google Scholar 

  • Sterelny K, Griffiths P (1999) Sex and death. University of Chicago Press, Chicago

    Google Scholar 

  • Sterelny K, Kitcher P (1988) The return of the gene. J Philos 85(7):339–361

    Article  Google Scholar 

  • Stone M (1974) Cross-validictory choice and assessment of statistical predictions (with discussion). J R Stat Soc B 36:111–147

    Google Scholar 

  • Stone M (1977) An asumptotic equivalence of choice of model by cross-validation and Akaike’s criterion. J R Stat Soc B 39:44–47

    Google Scholar 

  • Strassmann JE, Gilbert OM, Queller DC (2011) Kin discrimination and cooperation in microbes. Annu Rev Microbiol 65:349–367

    Article  Google Scholar 

  • Taylor PD, Frank SA (1996) How to make a kin selection model. J Theor Biol 180:27–37

    Article  Google Scholar 

  • Weisberg M (2013) Simulation and similarity: using models to understand the world. Oxford University Press, Oxford

    Book  Google Scholar 

  • West SA, Griffin AS, Gardner A (2007) Social semantics: altruism, cooperation, mutualism, strong reciprocity and group selection. J Evol Biol 20(2):415–432

    Article  Google Scholar 

  • West SA, Griffin AS, Gardner A (2008) Social semantics: how useful has group selection been? J Evol Biol 21(1):374–385

    Article  Google Scholar 

  • West SA, El Mouden C, Gardner A (2011) Sixteen common misconceptions about the evolution of cooperation in humans. Evol Human Behav 32(4):231–262

    Article  Google Scholar 

  • Wilson DS (2008) Social semantics: toward a genuine pluralism in the study of social behaviour. J Evol Biol 21(1):368–373

    Article  Google Scholar 

  • Wilson DS, Dugatkin L (1992) Altruism: contemporary debates. In: Keller EF, Lloyd EA (eds) Keywords in evolutionary biology. Harvard University Press, Cambridge, pp 29–33

    Google Scholar 

  • Wyatt GA, West SA, Gardner A (2013) Can natural selection favour altruism between species? J Evol Biol 26(9):1854–1865

    Article  Google Scholar 

  • Zucchini W (2000) An introduction to model selection. J Math Psychol 44:41–61

    Article  Google Scholar 

Download references

Acknowledgements

I would like to thank Elliott Sober, Joel Velasco, Angela Potochnik, Daniel Reuman, the referees for this journal, and audiences at the University of Kansas, the University of Missouri-Columbia, and the University of Wisconsin-Madison, for helpful comments on previous drafts of the paper.

Author information

Authors and Affiliations

  1. Department of Philosophy, University of Kansas, 3101 Wescoe Hall, Lawrence, KS, 66045, USA

    Armin W. Schulz

Authors
  1. Armin W. Schulz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Armin W. Schulz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schulz, A.W. By genes alone: a model selectionist argument for genetical explanations of cooperation in non-human organisms. Biol Philos 32, 951–967 (2017). https://doi.org/10.1007/s10539-017-9584-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10539-017-9584-0

Keywords

Search

Navigation