Abstract
Recent studies in the evolution of cooperation have shifted focus from altruistic to mutualistic cooperation. This change in focus is purported to reveal new explanations for the evolution of prosocial behavior. We argue that the common classification scheme for social behavior used to distinguish between altruistic and mutualistic cooperation is flawed because it fails to take into account dynamically relevant game-theoretic features. This leads some arguments about the evolution of cooperation to conflate dynamical scenarios that differ regarding the basic conditions on the emergence and maintenance of cooperation. We use the tools of evolutionary game theory to increase the resolution of the classification scheme and analyze what evolutionary inferences classifying social behavior can license.
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Notes
Hamilton (1964a, 15) presents the 2 × 2 scheme in terms of inclusive fitness gains and losses; these are now referred to as benefits and costs. In his classification scheme, behaviors where individuals (actors) lose and neighbors (recipients) gain are altrustic. Conversely, behaviors where actors gain and recipients lose are selfish. Interestingly, the other two categories are labeled “selected” (mutual benefit, both gain) and “counter-selected” (spite, both lose); subsequent work revealed a flaw in these labels (Hamilton 1970).
That said, it turns out that even in the so-called easy cases for cooperation certain difficulties can arise in the form of spite Forber and Smead (2014).
Mutually beneficial social behavior is often referred to as “mutualism” or as “by-product mutualism” in the literature. West et al. (2007) argue that the term “mutual benefit” is better than “mutualism,” given the common use of the latter term in ecology to describe cross-specific symbioses. Since we do not consider symbioses here, we will use the terms interchangeably, though we will follow this recommendation as best we can.
In fact, Tomasello et al. (2012, 674) specifically endorse this game-theoretic characterization of the shift, though they do not pursue their arguments with formal evolutionary game theory.
Perhaps more problematic, there are scenarios when classification fails completely. If one’s opponent plays a strategy corresponding to the mixed Nash equilibrium (for the above game the mixed strategy exhibits X and Y with equal probability) then there are no expected costs or benefits associated with X and Y for the actor.
Thanks to Marty Barrett for making this point clear to us. The details underlying the change in classification are as follows. The payoff comparison for actor’s behavior X (when recipient does Y) in the modified game is 0 versus 2 for the actor and 1 versus 2 for her recipient. Opting for X over Y inflicts costs on both and so looks spiteful. The payoff comparison for actor’s behavior Y (when recipient does Y) changes in a similar way (2 versus 0 for the actor and 2 versus 1 for her recipient). Opting for Y over X benefits both and so looks mutualistic. In asymmetric games different shifts in payoffs are possible, illustrating further flexibility in classifying behaviors.
This way of describing the problem of classification has not, to our knowledge, been made explicit in the literature, though there are some approaches that do focus on the interactions rather than individual strategies. For example, Bomze (1983) provides a “complete classification of the two-dimensional phase flows” for the replicator dynamics where different cases are individuated based differences in the corresponding payoff matrix. The payoff matrices provide the necessary information to classify types of interactions.
Hauert et al. (2006) show how this sort of framework can be extended to N-players and can incorporate synergy and discounting amongst the contributions of multiple players. Bomze (1983) investigates a complex dynamic classification for 3 × 3 symmetric game. For our purposes, the 2-player 2 × 2 framework found in Weibull (1995) suffices to reveal the limitations in the standard scheme, and so we pursue our argument without these additional formal details.
Fixing the interaction context could mean examining a particular interaction with an opponent that is behaving in a particular way. It could be specified in other ways as well. For instance, one might take the distribution of behaviors in a given population and imagining an interaction with an idealized “average” interaction partner.
Of course, better prosocial strategies can invade. And, if certain factors so conspire, such as non-random assortment, population structure, or the like, antisocial or selfish behavior can invade as well.
Cooperation is stable in some settings and unstable in others. In settings where cooperation is stable, it may or may not evolve reliably. Errors in action, information transfer, and nuanced conditional behavior can all result in different evolutionary scenarios, some more favorable for cooperation than others. See Nowak and Sigmund (2005) for a survey of these results.
This line of reasoning can generalize to combinations of alleles in diploid population genetic models and therefore identifies a limitation on optimality analyses of the evolution of recombination that assume additivity across alleles. See Feldman et al. (1997) for discussion.
It is important to note that this contrast between interaction type and interaction structure may collapse at higher levels of abstraction. For example, a repeated Prisoner’s Dilemma with a restricted strategy space has the same strategic dynamics as a one-shot Stag Hunt (Skyrms 2004). This shows that there are cases where a given interaction type and interaction structure can be abstractly represented with another interaction type with a different interaction structure. Thus, the distinction between interaction type and interaction structure should be treated as a methodological tool rather than a hard distinction.
References
Alexander R (1987) The biology of moral systems. Aldine Transaction, Chicago
Allen B, Nowak MA, Wilson EO (2013) Limitations of inclusive fitness. Proc Natl Acad Sci 110:20135–20139
Ariew A, Lewontin RC (2004) The confusions of fitness. Br J Philos Sci 55:347–363
Baumard N, André JB, Sperber D (2013) A mutualistic approach to morality: the evolution of fairness by partner choice. Behav Brain Sci 36:59–122
Beatty J (1992) Fitness: theoretical contexts. In: Keller EF, Lloyd EA (eds) Keywords in evolutionary biology. Harvard University Press, Cambridge, pp 115–119
Beatty J, Finsen S (1989) Rethinking the propensity interpretation: a peek inside pandora’s box. In: Ruse M (ed) What philosophy of biology is. Kluwer Academic Publishers, Dordrecht, pp 17–30
Bednar J, Page S (2007) Can game(s) theory explain culture? The emergence of cultural behavior within multiple games. Ration Soc 19:65–97
Bomze IM (1983) Lotka–Volterra equation and replicator dynamics: a two-dimensional classification. Biol Cybern 48:201–211
Cavalli-Sforza LL, Feldman MW (1978) Darwinian selection and “altruism”. Theor Popul Biol 14:268–280
Eshel I, Cavalli-Sforza LL (1982) Assortment of encounters and the evolution of cooperativeness. Proc Natl Acad Sci 79:1331–1335
Feldman MW, Otto SP, Christiansen FB (1997) Population genetic perspectives on the evolution of recombination. Annu Rev Genet 30:261–295
Forber P, Smead R (2014) An evolutionary paradox for prosocial behavior. J Philos 111:151–166
Frank SA (1998) Foundations of social evolution. Princeton University Press, Princeton
Hamilton WD (1964a) The genetical evolution of social behaviour, I. J Theor Biol 7:1–16
Hamilton WD (1964b) The genetical evolution of social behaviour, II. J Theor Biol 7:17–52
Hamilton WD (1970) Selfish and spiteful behavior in an evolutionary model. Nature 228:1218–1220
Harsanyi J (1967) Games with incomplete information played by “Bayesian” players. Manag Sci 14(3):159–182
Hashimoto T, Kumagi Y (2003) Meta-evolutionary game dynamics for mathematical modeling of rules dynamics. In: Banzhaf B, Christaller T, Ziegler J (eds) Advances in artificial life. Springer, Berlin, pp 107–117
Hauert C, Michor F, Nowak MA, Doebeli M (2006) Synergy and discounting of cooperation in social dilemmas. J Theor Biol 239(2):195–202
Lehmann L, Bargum K, Reuter M (2006) An evolutionary analysis of the relationship between spite and altruism. J Evol Biol 19:1507–1516
Lehmann L, Feldman MW, Rousset F (2009) On the evolution of harming and recognition in finite panmictic and infinite structured populations. Evolution 63:2896–2913
Maynard Smith J, Price GR (1973) The logic of animal conflict. Nature 246:15–18
Nowak MA, Sigmund K (2005) Evolution of indirect reciprocity. Nature 437:1291–1298
Okasha S (2007) Evolution and the levels of selection. Oxford University Press, Oxford
Price GR (1970) Selection and covariance. Nature 277:520–521
Queller D (1985) Kinship, reciprocity and synergism in the evolution of social behavior. Nature 318:366–367
Skyrms B (1996) Evolution of the social contract. Cambridge University Press, Cambridge
Skyrms B (2004) The stag hunt and the evolution of social structure. Cambridge University Press, Cambridge
Smead R (2014) Evolving games and the social contract. In: Youngman PA, Hadzikadic M (eds) Complexity and the human experience. Pan Stanford, Singapore, pp 61–80
Smead R, Forber P (2013) The evolutionary dynamics of spite in finite populations. Evolution 67(3):698–707
Sober E (2001) The two faces of fitness. In: Singh RS, Krimbas CB, Paul DP, Beatty J (eds) Thinking about evolution. Cambridge University Press, Cambridge
Sober E, Wilson DS (1998) Unto others. Harvard University Press, Cambridge
Sterelny K (2012) The evolved apprentice: how evolution made humans unique. MIT Press, Cambridge
Sterelny K, Joyce R, Calcott B, Fraser B (2013) Cooperation and its evolution. MIT Press, Cambridge
Taylor HM, Gourley RS, Lawrence CE, Kaplan RS (1974) Natural selection of life history attributes: an analytical approach. Theor Popul Biol 5:104–122
Tomasello M, Vaish A (2013) Origins of human cooperation and morality. Annu Rev Psychol 64:231–255
Tomasello M, Melis AP, Tennie C, Wyman E, Herrmann E (2012) Two key steps in the evolution of human cooperation: the interdependence hypothesis. Curr Anthropol 53:673–692
van Veelen M (2009) Group selection, kin selection, altruism and cooperation: when inclusive fitness is right and when it can be wrong. J Theor Biol 259:589–600
Weibull JW (1995) Evolutionary game theory. MIT Press, Cambridge
West SA, Gardner A (2010) Altruism, spite, and greenbeards. Science 327:1341–1344
West SA, Griffin AS, Gardner A (2007) Social semantics: altruism, cooperation, mutualism, strong reciprocity and group selection. J Evol Biol 20:415–432
Worden L, Levin SA (2007) Evolutionary escape from the prisoner’s dilemma. J Theor Biol 245:411–411
Zollman KJS (2008) Explaining fairness in complex environments. Polit Philos Econ 7(1):81–98
Acknowledgments
Thanks to Elliott Sober, Marty Barrett, Malcolm Forster, two anonymous referees, and the audience at POBAM 2014 for valuable feedback and discussion.
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Patrick Forber and Rory Smead have contributed equally to this work.
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Forber, P., Smead, R. Evolution and the classification of social behavior. Biol Philos 30, 405–421 (2015). https://doi.org/10.1007/s10539-015-9486-y
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DOI: https://doi.org/10.1007/s10539-015-9486-y