Network homophily and the evolution of the pay-it-forward reciprocity

The pay-it-forward reciprocity is a type of cooperative behavior that people who have benefited from others return favors to third parties other than the benefactors, thus pushing forward a cascade of kindness. The phenomenon of the pay-it-forward reciprocity is ubiquitous, yet how it evolves to be part of human sociality has not been fully understood. We develop an evolutionary dynamics model to investigate how network homophily influences the evolution of the pay-it-forward reciprocity. Manipulating the extent to which actors carrying the same behavioral trait are linked in networks, the computer simulation model shows that strong network homophily helps consolidate the adaptive advantage of cooperation, yet introducing some heterophily to the formation of network helps advance cooperation's scale further. Our model enriches the literature of inclusive fitness theory by demonstrating the conditions under which cooperation or reciprocity can be selected for in evolution when social interaction is not confined exclusively to relatives.     

Social structure modulates the evolutionary consequences of social plasticity: A social network perspective on interacting phenotypes

Organisms express phenotypic plasticity during social interactions. Interacting phenotype theory has explored the consequences of social plasticity for evolution, but it is unclear how this theory applies to complex social structures. We adapt interacting phenotype models to general social structures to explore how the number of social connections between individuals and preference for phenotypically similar social partners affect phenotypic variation and evolution. We derive an analytical model that ignores phenotypic feedback and use simulations to test the predictions of this model. We find that adapting previous models to more general social structures does not alter their general conclusions but generates insights into the effect of social plasticity and social structure on the maintenance of phenotypic variation and evolution. Contribution of indirect genetic effects to phenotypic variance is highest when interactions occur at intermediate densities and decrease at higher densities, when individuals approach interacting with all group members, homogenizing the social environment across individuals. However, evolutionary response to selection tends to increase at greater network densities as the effects of an individual's genes are amplified through increasing effects on other group members. Preferential associations among similar individuals (homophily) increase both phenotypic variance within groups and evolutionary response to selection. Our results represent a first step in relating social network structure to the expression of social plasticity and evolutionary responses to selection.     

Exploring the causes and consequences of cooperative behaviour in wild animal populations using a social network approach

Understanding why individuals carry out behaviours that benefit others, especially genetically unrelated others, has been a major undertaking in many fields and particularly in biology. Here, we focus on the cooperation literature from natural populations and present the benefits of a social network approach in terms of how it can help to identify and understand factors that influence the maintenance and spread of cooperation, but are not easily captured when solely considering independent dyadic interactions. We describe how various routes to cooperation can be tested within the social network framework. Applying the social network approach to data from natural populations can help to uncover the evolutionary and ecological pressures that lead to differences in cooperation and other social processes.     

Dynamic changes in display architecture and function across environments revealed by a systems approach to animal communication*

Animal communication is often structurally complex and dynamic, with signaler and receiver behavior varying in response to multiple environmental factors. To date, studies assessing signal dynamics have mostly focused on the relationships between select signaling traits and receiver responses in a single environment. We use the wolf spider Schizocosa floridana to explore the relationships between courtship display form and function across two social contexts (female presence vs absence) and two light environments (light vs dark). We use traditional analytical methods to determine predictors of copulation success (i.e., signal function) and examine these predictors in a structural context by overlaying them on signal phenotype networks (Wilkins et al. 2015). This allows us to explore system design principles (degeneracy, redundancy, pluripotentiality), providing insight into hypotheses regarding complex signal evolution. We found that both social context and light environment affect courtship structure, although the predictors of mating success remain similar across light environments, suggesting system degeneracy. Contrastingly, the same display traits may serve different functions across social environments, suggesting pluripotentiality. Ultimately, our network approach uncovers a complexity in display structure and function that is missed by functional analyses alone, highlighting the importance of systems‐based methodologies for understanding the dynamic nature of complex signals.     

Learning and Behavior in Reef Fish: Fuel for Microevolutionary Change?

Small‐scale population differentiation among coral reef fishes may be more common than previously thought. New molecular technologies have informed patterns of differentiation, while experimental approaches focusing on larval abilities to limit distribution have explored processes leading to diversification. Building upon a recently published paper by Wismer et al. that examined population level differences in learning and cooperative behaviors in cleaner wrasse (Labroides dimidiatus), we use a phylogenetic framework to explore how social behaviors are distributed among wrasses in the Labrichthyines clade. Establishing links between social behavior and speciation across the phylogeny allows us explore how social behaviors such as learning and cooperation may also act as possible mechanisms driving diversification at the microevolutionary scale.     

From local to global dilemmas in social networks

Social networks affect in such a fundamental way the dynamics of the population they support that the global, population-wide behavior that one observes often bears no relation to the individual processes it stems from. Up to now, linking the global networked dynamics to such individual mechanisms has remained elusive. Here we study the evolution of cooperation in networked populations and let individuals interact via a 2-person Prisoner's Dilemma--a characteristic defection dominant social dilemma of cooperation. We show how homogeneous networks transform a Prisoner's Dilemma into a population-wide evolutionary dynamics that promotes the coexistence between cooperators and defectors, while heterogeneous networks promote their coordination. To this end, we define a dynamic variable that allows us to track the self-organization of cooperators when co-evolving with defectors in networked populations. Using the same variable, we show how the global dynamics--and effective dilemma--co-evolves with the motifs of cooperators in the population, the overall emergence of cooperation depending sensitively on this co-evolution.     

Altruism and the evolution of resource generalism and specialism

The evolution of resource specialism and generalism has attracted widespread interest. Evolutionary drivers affecting niche differentiation and resource specialization have focused on the role of trade-offs. Here, however, we explore how the role of cooperation, mediated through altruistic behaviors, and classic resource-consumer dynamics can influence the evolution of resource utilization. Using an evolutionary invasion approach, we investigate how critical thresholds in levels of altruism are needed for resource specialization to arise and be maintained. Differences between complementary (essential) and substitutable resources affect the evolution of resource generalists. The strength of resource preferences coupled with the levels of altruism are predicted to influence the evolution of generalism. Coupling appropriate evolutionary game and ecological dynamics lead to novel expectations in the feedbacks between social behaviors and population dynamics for understanding classic ecological problems.     

Complex cooperative networks from evolutionary preferential attachment

In spite of its relevance to the origin of complex networks, the interplay between form and function and its role during network formation remains largely unexplored. While recent studies introduce dynamics by considering rewiring processes of a pre-existent network, we study network growth and formation by proposing an evolutionary preferential attachment model, its main feature being that the capacity of a node to attract new links depends on a dynamical variable governed in turn by the node interactions. As a specific example, we focus on the problem of the emergence of cooperation by analyzing the formation of a social network with interactions given by the Prisoner's Dilemma. The resulting networks show many features of real systems, such as scale-free degree distributions, cooperative behavior and hierarchical clustering. Interestingly, results such as the cooperators being located mostly on nodes of intermediate degree are very different from the observations of cooperative behavior on static networks. The evolutionary preferential attachment mechanism points to an evolutionary origin of scale-free networks and may help understand similar feedback problems in the dynamics of complex networks by appropriately choosing the game describing the interaction of nodes.     

The evolution of altruistic social preferences in human groups

In this paper, we consider three hypotheses to account for the evolution of the extraordinary capacity for large-scale cooperation and altruistic social preferences within human societies. One hypothesis is that human cooperation is built on the same evolutionary foundations as cooperation in other animal societies, and that fundamental elements of the social preferences that shape our species'' cooperative behaviour are also shared with other closely related primates. Another hypothesis is that selective pressures favouring cooperative breeding have shaped the capacity for cooperation and the development of social preferences, and produced a common set of behavioural dispositions and social preferences in cooperatively breeding primates and humans. The third hypothesis is that humans have evolved derived capacities for collaboration, group-level cooperation and altruistic social preferences that are linked to our capacity for culture. We draw on naturalistic data to assess differences in the form, scope and scale of cooperation between humans and other primates, experimental data to evaluate the nature of social preferences across primate species, and comparative analyses to evaluate the evolutionary origins of cooperative breeding and related forms of behaviour.     

Why understanding multiplex social network structuring processes will help us better understand the evolution of human behavior

Social scientists have long appreciated that relationships between individuals cannot be described from observing a single domain, and that the structure across domains of interaction can have important effects on outcomes of interest (e.g., cooperation; Durkheim, 1893). One debate explicitly about this surrounds food sharing. Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains in the form of trade (Kaplan and Hill, 1985.). Multilayer networks, high‐dimensional networks that allow us to consider multiple sets of relationships at the same time, are ubiquitous and have consequences, so processes giving rise to them are important social phenomena. The analysis of multi‐dimensional social networks has recently garnered the attention of the network science community (Kivelä et al., 2014). Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions (Górski et al., 2018). Understanding the structuring processes that underlie multiplex networks will help understand increasingly rich data sets, giving more accurate and complete pictures of social interactions.     

Ranking Competitors Using Degree-Neutralized Random Walks

Competition is ubiquitous in many complex biological, social, and technological systems, playing an integral role in the evolutionary dynamics of the systems. It is often useful to determine the dominance hierarchy or the rankings of the components of the system that compete for survival and success based on the outcomes of the competitions between them. Here we propose a ranking method based on the random walk on the network representing the competitors as nodes and competitions as directed edges with asymmetric weights. We use the edge weights and node degrees to define the gradient on each edge that guides the random walker towards the weaker (or the stronger) node, which enables us to interpret the steady-state occupancy as the measure of the node''s weakness (or strength) that is free of unwarranted degree-induced bias. We apply our method to two real-world competition networks and explore the issues of ranking stabilization and prediction accuracy, finding that our method outperforms other methods including the baseline win–loss differential method in sparse networks.     

Recent trends in evolutionary ethics: greenbeards!

In recent years, there has been growing awareness among evolutionary ethicists that systems of cooperation based upon “weak” reciprocity mechanisms (such as tit-for-tat) lack scalability, and are therefore inadequate to explain human ultrasociality. This has produced a shift toward models that strengthen the cooperative mechanism, by adding various forms of commitment or punishment. Unfortunately, the most prominent versions of this hypothesis wind up positing a discredited mechanism as the basis of human ultrasociality, viz. a “greenbeard.” This paper begins by explaining what a greenbeard is, and why evolutionary theorists are doubtful that such a mechanism could play a significant role in explaining human prosociality. It goes on to analyze several recent philosophical works in evolutionary ethics, in order to show how the suggestion that morality acts as a commitment device tacitly relies upon a greenbeard mechanism to explain human cooperation. It concludes by showing how some early scientific models in the “evolution of cooperation” literature, which introduced punishment as a device to enhance cooperation, also tacitly relied upon a greenbeard mechanism.     

Evaluating reproductive decisions as discrete choices under social influence

Discrete choice, coupled with social influence, plays a significant role in evolutionary studies of human fertility, as investigators explore how and why reproductive decisions are made. We have previously proposed that the relative magnitude of social influence can be compared against the transparency of pay-off, also known as the transparency of a decision, through a heuristic diagram that maps decision-making along two axes. The horizontal axis represents the degree to which an agent makes a decision individually versus one that is socially influenced, and the vertical axis represents the degree to which there is transparency in the pay-offs and risks associated with the decision the agent makes. Having previously parametrized the functions that underlie the diagram, we detail here how our estimation methods can be applied to real-world datasets concerning sexual health and contraception.     

BaYaka forager food sharing networks in the Congo Basin: The roles of gender homophily and kin sharing

Objectives

Food sharing is a costly form of cooperation that was likely critical to human evolutionary success, including the emergence of human's life history strategy. Food sharing in human communities may be maintained through a number of pathways, including direct dyadic reciprocity, reputation-based processes, and kin-biased exchange. Differences in reproductive demands, labor, and cultural norms may also result in gendered differences in cooperative networks. Here, we examine cooperative networks in egalitarian BaYaka foragers from the Congo Basin.

Materials and Methods

We collected social network data from 112 adults in 41 households in this subsistence community. We implement a Bayesian latent network model to assess individual-, dyadic-, and block-level predictors of food sharing partners.

Results

Conditioning on covariates, we found limited evidence for direct dyadic reciprocity in food sharing. Despite local norms regarding prestige avoidance, we found status-based homophily. High-status individuals—council members and local healers—were more likely to share with one another. Importantly, our results highlight gender differences in patterns of food sharing, interacting with genetic relatedness. Women were more likely to share with one another, especially with kin as genetic relatedness increased.

Discussion

Our results align with evolutionary framing emphasizing kin selection in costly cooperation. The results showing that women cooperate with other women, particularly kin, also complement sex-based patterns in some other mammalian species, potentially reflecting the social support necessary to manage reproductive costs and childcare. BaYaka women's subsistence productivity and local cultural dynamics for autonomy and egalitarianism may likewise help facilitate women's preferential cooperation with one another.

     

Functional Constraints on Insect Immune System Components Govern Their Evolutionary Trajectories

Roles of constraints in shaping evolutionary outcomes are often considered in the contexts of developmental biology and population genetics, in terms of capacities to generate new variants and how selection limits or promotes consequent phenotypic changes. Comparative genomics also recognizes the role of constraints, in terms of shaping evolution of gene and genome architectures, sequence evolutionary rates, and gene gains or losses, as well as on molecular phenotypes. Characterizing patterns of genomic change where putative functions and interactions of system components are relatively well described offers opportunities to explore whether genes with similar roles exhibit similar evolutionary trajectories. Using insect immunity as our test case system, we hypothesize that characterizing gene evolutionary histories can define distinct dynamics associated with different functional roles. We develop metrics that quantify gene evolutionary histories, employ these to characterize evolutionary features of immune gene repertoires, and explore relationships between gene family evolutionary profiles and their roles in immunity to understand how different constraints may relate to distinct dynamics. We identified three main axes of evolutionary trajectories characterized by gene duplication and synteny, maintenance/stability and sequence conservation, and loss and sequence divergence, highlighting similar and contrasting patterns across these axes amongst subsets of immune genes. Our results suggest that where and how genes participate in immune responses limit the range of possible evolutionary scenarios they exhibit. The test case study system of insect immunity highlights the potential of applying comparative genomics approaches to characterize how functional constraints on different components of biological systems govern their evolutionary trajectories.     

Natural selection of cooperation and degree hierarchy in heterogeneous populations

One of the current theoretical challenges to the explanatory powers of Evolutionary Theory is the understanding of the observed evolutionary survival of cooperative behavior when selfish actions provide higher fitness (reproductive success). In unstructured populations natural selection drives cooperation to extinction. However, when individuals are allowed to interact only with their neighbors, specified by a graph of social contacts, cooperation-promoting mechanisms (known as lattice reciprocity) offer to cooperation the opportunity of evolutionary survival. Recent numerical works on the evolution of Prisoner's Dilemma in complex network settings have revealed that graph heterogeneity dramatically enhances the lattice reciprocity. Here we show that in highly heterogeneous populations, under the graph analog of replicator dynamics, the fixation of a strategy in the whole population is in general an impossible event, for there is an asymptotic partition of the population in three subsets, two in which fixation of cooperation or defection has been reached and a third one which experiences cycles of invasion by the competing strategies. We show how the dynamical partition correlates with connectivity classes and characterize the temporal fluctuations of the fluctuating set, unveiling the mechanisms stabilizing cooperation in macroscopic scale-free structures.     

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural 'knock-outs' in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.     

Social interaction in synthetic and natural microbial communities

Social interaction among cells is essential for multicellular complexity. But how do molecular networks within individual cells confer the ability to interact? And how do those same networks evolve from the evolutionary conflict between individual‐ and population‐level interests? Recent studies have dissected social interaction at the molecular level by analyzing both synthetic and natural microbial populations. These studies shed new light on the role of population structure for the evolution of cooperative interactions and revealed novel molecular mechanisms that stabilize cooperation among cells. New understanding of populations is changing our view of microbial processes, such as pathogenesis and antibiotic resistance, and suggests new ways to fight infection by exploiting social interaction. The study of social interaction is also challenging established paradigms in cancer evolution and immune system dynamics. Finding similar patterns in such diverse systems suggests that the same ‘social interaction motifs’ may be general to many cell populations.     

Evolution of Cooperation on Stochastic Dynamical Networks

Cooperative behavior that increases the fitness of others at a cost to oneself can be promoted by natural selection only in the presence of an additional mechanism. One such mechanism is based on population structure, which can lead to clustering of cooperating agents. Recently, the focus has turned to complex dynamical population structures such as social networks, where the nodes represent individuals and links represent social relationships. We investigate how the dynamics of a social network can change the level of cooperation in the network. Individuals either update their strategies by imitating their partners or adjust their social ties. For the dynamics of the network structure, a random link is selected and breaks with a probability determined by the adjacent individuals. Once it is broken, a new one is established. This linking dynamics can be conveniently characterized by a Markov chain in the configuration space of an ever-changing network of interacting agents. Our model can be analytically solved provided the dynamics of links proceeds much faster than the dynamics of strategies. This leads to a simple rule for the evolution of cooperation: The more fragile links between cooperating players and non-cooperating players are (or the more robust links between cooperators are), the more likely cooperation prevails. Our approach may pave the way for analytically investigating coevolution of strategy and structure.     

Learning to live in a global commons: socioeconomic challenges for a sustainable environment

Ecologists, economists and other social scientists have much incentive for interaction. First of all, ecological systems and socioeconomic systems are linked in their dynamics, and these linkages are key to coupling environmental protection and economic growth. Beyond this, however, are the obvious similarities in how ecological systems and socioeconomic systems function, and the common theoretical challenges in understanding their dynamics. This should not be surprising. Socioeconomic systems are in fact ecological systems, in which the familiar ecological phenomena of exploitation, cooperation and parasitism all can be identified as key features. Or, viewed from the opposite perspective, ecological systems are economic systems, in which competition for resources is key, and in which an evolutionary process shapes the individual agents to a distribution of specialization of function that leads to the emergence of flows and functionalities at higher levels of organization. Most fundamentally, ecological and socioeconomic systems alike are complex adaptive systems, in which patterns at the macroscopic level emerge from interactions and selection mechanisms mediated at many levels of organization, from individual agents to collectives to whole systems and even above. In such complex adaptive systems, robustness must be understood as emergent from selection processes operating at these many different levels, and the inherent nonlinearities can trigger sudden shifts in regimes that, in the case of the biosphere, can have major consequences for humanity. This lecture will explore the complex adaptive nature of ecosystems, and the implications for the robustness of ecosystem services on which we depend, and in particular examine the conditions under which cooperative behavior emerges. It will then turn attention to the socioeconomic systems in which environmental management is based, and ask what lessons can be learned from our examination of natural systems, and how we can modify social norms to achieve global cooperation in managing our common future. Of special interest will be issues of intragenerational and intergenerational equity, and the importance of various forms of discounting.