More Like this

1. The gene's eye view, the Gouldian knot, Fisherian swords and the causes of selection

   https://doi.org/10.1098/rstb.2019.0354  

   Queller, David C. ( April 2020 , Philosophical Transactions of the Royal Society B: Biological Sciences)

   The biological units-of-selection debate has centred on questions of which units experience selection and adaptation. Here, I use a causal framework and the Price equation to develop the gene's eye perspective. Genes are causally special in being both replicators and interactors. Gene effects are tied together in a complex Gouldian knot of interactions, but Fisher deployed three swords to try to cut the knot. The first, Fisher's average excess, is non-causal, so not fully satisfactory in that respect. The Price equation highlights Fisher's other two swords, choosing to model only selection, and only the part that is transmissible across generations. The models developed here show that many causes of organismal fitness do not cause Gouldian complications. Only two kinds of elements must be added to the focal gene for a causal explanation of its selective change: co-replicators that are associated with the focal gene and co-interactors that interact non-additively with the focal gene. Identical equations for co-replication and co-interaction describe interactions between gene copies at a single locus or at separate loci, and also for genes situated within the same individual or in different individuals. These results resolve some of the objections to the gene's eye view. This article is part of the theme issue ‘Fifty years of the Price equation’. 

   more » « less
2. Did Human Culture Emerge in a Cultural Evolutionary Transition in Individuality?

   https://doi.org/10.1007/s13752-021-00382-x  

   Davison, Dinah R. ; Andersson, Claes ; Michod, Richard E. ; Kuhn, Steven L. ( December 2021 , Biological Theory)

   Abstract Evolutionary Transitions in Individuality (ETI) have been responsible for the major transitions in levels of selection and individuality in natural history, such as the origins of prokaryotic and eukaryotic cells, multicellular organisms, and eusocial insects. The integrated hierarchical organization of life thereby emerged as groups of individuals repeatedly evolved into new and more complex kinds of individuals. The Social Protocell Hypothesis (SPH) proposes that the integrated hierarchical organization of human culture can also be understood as the outcome of an ETI—one that produced a “cultural organism” (a “sociont”) from a substrate of socially learned traditions that were contained in growing and dividing social communities. The SPH predicts that a threshold degree of evolutionary individuality would have been achieved by 2.0–2.5 Mya, followed by an increasing degree of evolutionary individuality as the ETI unfolded. We here assess the SPH by applying a battery of criteria—developed to assess evolutionary individuality in biological units—to cultural units across the evolutionary history of Homo. We find an increasing agreement with these criteria, which buttresses the claim that an ETI occurred in the cultural realm. 

   more » « less
3. 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
4. CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems

   https://doi.org/10.1145/3580305.3599272  

   Jiang, Song ; Huang, Zijie ; Luo, Xiao ; Sun, Yizhou ( August 2023 , KDD’23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)

   Multi-agent dynamical systems refer to scenarios where multiple units (aka agents) interact with each other and evolve collectively over time. For instance, people’s health conditions are mutually influenced. Receiving vaccinations not only strengthens the longterm health status of one unit but also provides protection for those in their immediate surroundings. To make informed decisions in multi-agent dynamical systems, such as determining the optimal vaccine distribution plan, it is essential for decision-makers to estimate the continuous-time counterfactual outcomes. However, existing studies of causal inference over time rely on the assumption that units are mutually independent, which is not valid for multi-agent dynamical systems. In this paper, we aim to bridge this gap and study how to estimate counterfactual outcomes in multi-agent dynamical systems. Causal inference in a multi-agent dynamical system has unique challenges: 1) Confounders are timevarying and are present in both individual unit covariates and those of other units; 2) Units are affected by not only their own but also others’ treatments; 3) The treatments are naturally dynamic, such as receiving vaccines and boosters in a seasonal manner. To this end, we model a multi-agent dynamical system as a graph and propose a novel model called CF-GODE (CounterFactual Graph Ordinary Differential Equations). CF-GODE is a causal model that estimates continuous-time counterfactual outcomes in the presence of inter-dependencies between units. To facilitate continuous-time estimation,we propose Treatment-Induced GraphODE, a novel ordinary differential equation based on graph neural networks (GNNs), which can incorporate dynamical treatments as additional inputs to predict potential outcomes over time. To remove confounding bias, we propose two domain adversarial learning based objectives that learn balanced continuous representation trajectories, which are not predictive of treatments and interference. We further provide theoretical justification to prove their effectiveness. Experiments on two semi-synthetic datasets confirm that CF-GODE outperforms baselines on counterfactual estimation. We also provide extensive analyses to understand how our model works. 

   more » « less
5. Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles

   https://doi.org/10.1093/evolut/qpac012  

   Costello, Robin A. ; Cook, Phoebe A. ; Brodie, III, Edmund D. ; Formica, Vincent A. ( December 2022 , Evolution)

   Both individual and group behavior can influence individual fitness, but multilevel selection is rarely quantified on social behaviors. Social networks provide a unique opportunity to study multilevel selection on social behaviors, as they describe complex social traits and patterns of interaction at both the individual and group levels. In this study, we used contextual analysis to measure the consequences of both individual network position and group network structure on individual fitness in experimental populations of forked fungus beetles (Bolitotherus cornutus) with two different resource distributions. We found that males with high individual connectivity (strength) and centrality (betweenness) had higher mating success. However, group network structure did not influence their mating success. Conversely, we found that individual network position had no effect on female reproductive success but that females in populations with many social interactions experienced lower reproductive success. The strength of individual-level selection in males and group-level selection in females intensified when resources were clumped together, showing that habitat structure influences multilevel selection. Individual and emergent group social behavior both influence variation in components of individual fitness, but impact the male mating success and female reproductive success differently, setting up intersexual conflicts over patterns of social interactions at multiple levels.

    

   more » « less