More Like this

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

   https://doi.org/10.1093/jhered/esab064  

   McGlothlin, Joel W; Akçay, Erol; Brodie, Edmund D; Moore, Allen J; Van Cleve, Jeremy (January 2022, Journal of Heredity)

   Hughes, Kimberly (Ed.)

   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. 

   more » « less
2. Cyclic and multilevel causation in evolutionary processes

   https://doi.org/10.1007/s10539-020-09753-3  

   Warrell, Jonathan; Gerstein, Mark (October 2020, Biology & Philosophy)

   null (Ed.)

   Abstract Many models of evolution are implicitly causal processes. Features such as causal feedback between evolutionary variables and evolutionary processes acting at multiple levels, though, mean that conventional causal models miss important phenomena. We develop here a general theoretical framework for analyzing evolutionary processes drawing on recent approaches to causal modeling developed in the machine-learning literature, which have extended Pearls do-calculus to incorporate cyclic causal interactions and multilevel causation. We also develop information-theoretic notions necessary to analyze causal information dynamics in our framework, introducing a causal generalization of the Partial Information Decomposition framework. We show how our causal framework helps to clarify conceptual issues in the contexts of complex trait analysis and cancer genetics, including assigning variation in an observed trait to genetic, epigenetic and environmental sources in the presence of epigenetic and environmental feedback processes, and variation in fitness to mutation processes in cancer using a multilevel causal model respectively, as well as relating causally-induced to observed variation in these variables via information theoretic bounds. In the process, we introduce a general class of multilevel causal evolutionary processes which connect evolutionary processes at multiple levels via coarse-graining relationships. Further, we show how a range of fitness models can be formulated in our framework, as well as a causal analog of Prices equation (generalizing the probabilistic Rice equation), clarifying the relationships between realized/probabilistic fitness and direct/indirect selection. Finally, we consider the potential relevance of our framework to foundational issues in biology and evolution, including supervenience, multilevel selection and individuality. Particularly, we argue that our class of multilevel causal evolutionary processes, in conjunction with a minimum description length principle, provides a conceptual framework in which identification of multiple levels of selection may be reduced to a model selection problem. 

   more » « less
3. Multilevel Network Data Facilitate Statistical Inference for Curved ERGMs with Geometrically Weighted Terms

   https://doi.org/10.1016/j.socnet.2018.11.003  

   Stewart, Jonathan; Schweinberger, Michael; Bojanowski, Michal; Morris, Martina (January 2019, Social networks)

   Multilevel network data provide two important benefits for ERG modeling. First, they facilitate estimation of the decay parameters in geometrically weighted terms for degree and triad distributions. Estimating decay parameters from a single network is challenging, so in practice they are typically fixed rather than estimated. Multilevel network data overcome that challenge by leveraging replication. Second, such data make it possible to assess out-ofsample performance using traditional cross-validation techniques. We demonstrate these benefits by using a multilevel network sample of classroom networks from Poland. We show that estimating the decay parameters improves in-sample performance of the model and that the out-of-sample performance of our best model is strong, suggesting that our findings can be generalized to the population of interest. 

   more » « less
4. Recombination as an enforcement mechanism of prosocial behavior in cooperating bacteria

   https://doi.org/10.1016/j.isci.2023.107344  

   Lee, Isaiah_Paolo A; Eldakar, Omar Tonsi; Gogarten, J Peter; Andam, Cheryl P (August 2023, iScience)

   Prosocial behavior is ubiquitous in nature despite the relative fitness costs carried by cooperative individuals. However, the stability of cooperation in populations is fragile and often maintained through enforcement. We propose that homologous recombination provides such a mechanism in bacteria. Using an agent-based model of recombination in bacteria playing a public goods game, we demonstrate how changes in recombination rates affect the proportion of cooperating cells. In our model, recombination converts cells to a different strategy, either freeloading (cheaters) or cooperation, based on the strategies of neighboring cells and recombination rate. Increasing the recombination rate expands the parameter space in which cooperators outcompete freeloaders. However, increasing the recombination rate alone is neither sufficient nor necessary. Intermediate benefits of cooperation, lower population viscosity, and greater population size can promote the evolution of cooperation from within populations of cheaters. Our findings demonstrate how recombination influences the persistence of cooperative behavior in bacteria. 

   more » « less
5. Quantifying Evidence for—and against—Granger Causality with Bayes Factors

   https://doi.org/10.1080/00273171.2023.2214890  

   Oravecz, Zita; Vandekerckhove, Joachim (November 2024, Multivariate Behavioral Research)

   Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null -- we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study. 

   more » « less