A Synthesis of Game Theory and Quantitative Genetic Models of Social Evolution

Abstract

Two popular approaches for modeling social evolution, evolutionary game theory and quantitative genetics, ask complementary questions but are rarely integrated. Game theory focuses on evolutionary outcomes, with models solving for evolutionarily stable equilibria, whereas quantitative genetics provides insight into evolutionary processes, with models predicting short-term responses to selection. Here we draw parallels between evolutionary game theory and interacting phenotypes theory, which is a quantitative genetic framework for understanding social evolution. First, we show how any evolutionary game may be translated into two quantitative genetic selection gradients, nonsocial and social selection, which may be used to predict evolutionary change from a single round of the game. We show that synergistic fitness effects may alter predicted selection gradients, causing changes in magnitude and sign as the population mean evolves. Second, we show how evolutionary games involving plastic behavioral responses to partners can be modeled using indirect genetic effects, which describe how trait expression changes in response to genes in the social environment. We demonstrate that repeated social interactions in models of reciprocity generate indirect effects and conversely, that estimates of parameters from indirect genetic effect models may be used to predict the evolution of reciprocity. We argue that a pluralistic view incorporating both theoretical approaches will benefit empiricists and theorists studying social evolution. We advocate the measurement of social selection and indirect genetic effects in natural populations to test the predictions from game theory, and in turn, the use of game theory models to aid in the interpretation of quantitative genetic estimates.

Runaway evolution from male-male competition

“…It is Because selection gradients are equivalent to partial regression slopes, we can use partial derivatives of Eq. with respect to focal and opponent traits (evaluated at the population mean) to calculate nonsocial and social selection gradients (44). Taking these derivatives, we find nonsocial selection gradients for each trait to be…”

“…Evolutionary changes in the social environment can lead to concerted evolution because the social environment can be a source of selection on the traits that themselves compose the social environment (12,32,37,44,45). Such "social selection" (12-14, 35, 37, 41) is expected to arise whenever traits act as both the agents and the targets of selection and is potentially a key to understanding how male-male competition drives the evolution of elaborate male traits.…”

“…Because selection gradients are equivalent to partial regression slopes, we can use partial derivatives of Eq. 4 with respect to focal and opponent traits (evaluated at the population mean) to calculate nonsocial and social selection gradients (McGlothlin et al 2021). Taking these derivatives, we find nonsocial selection gradients for each trait to be and social selection gradients to be In other words, males with large signals are selected against via nonsocial selection but interacting with such males does not directly impose social selection.…”

“…In Eq. 4, the benefit term and the first cost term derive from the hawk-dove model of evolutionary game theory (Appendix; Maynard Smith & Price 1973; Maynard Smith 1982; McGlothlin et al . 2021).…”

“…Taking partial derivatives of with respect to focal and opponent traits (evaluated at the population mean) allows us to translate the fitness model in Eq. 4 into nonsocial and social selection gradients (McGlothlin et al . 2021).…”