Co-evolution Of Systems Research Articles

Signatures of selection with cultural interference

Human evolution is intricately linked with culture, which permeates almost all facets of human life from health and reproduction, to the environments in which we live. Nevertheless, our understanding of the ways in which stably transmitted, evolutionarily relevant human cultural traits might interact with the human genome is incomplete, and methods to detect such interactions are limited. Here, we describe some rules of cultural transmission which could pertain to both humans and cultural nonhuman animals that could lead to the formation and maintenance of stable associations between cultural and genetic traits. Next, we show that, in the presence of such associations, a process analogous to genetic hitchhiking is possible in gene-culture systems. These could leave signatures in the human genome similar to, and perhaps indistinguishable from, those left by selection on genetic traits. Finally, we model selective interference between cultural and genetic traits. We show that selective interference between a cultural trait under selection and a genetic trait under selection can reduce the efficacy of natural selection in the human genome, both in terms of the probability of fixation of beneficial alleles and the dynamics of selective sweeps. We then show that the efficiency of selection at genetic loci can, however, be increased in the presence of strong cultural transmission biases. This implies that the signatures of gene-culture interactions in genetic data may be complex and wide-ranging in gene-culture coevolutionary systems.

Exploring the food-energy-water nexus in coupled natural-human systems under climate change with a fully integrated agent-based modeling framework

Managing water resources to meet increasing energy and food demands while maintaining environmental sustainability under climate change is a major challenge, especially when this nexus occurred in a coupled natural–human system (CNHS), where heterogeneous human activities affect the natural hydrologic cycle and vice versa. The relevant research has been limited by the lack of models that can effectively integrate human dynamics and hydrologic conditions with spatial details to examine co-evolutionary systems. To address this challenge, this paper develops a modeling framework that integrates an agent-based model (ABM; human behavior model) into a large-scale, process-based distributed hydrologic model to simulate human decisions endogenously in the hydrologic cycle. We then apply the Decision Scaling approach, an ex-post scenario analysis method, with our integrated model to study the bidirectional feedback of the CNHS under future changing climate conditions. With the Columbia River Basin (CRB) selected as the case study area, the calibration results show that the integrated model can simultaneously capture the historical irrigated water consumption and streamflow dynamics. Modeling results show that the trade-off between irrigated water consumption, hydropower generation, and streamflow will become more pronounced under hotter and wetter climate conditions at both the entire basin and regional (states and provinces) levels. Special attention should be given to “temperature thresholds” of different regions when the trade-off pattern started. The trade-off results can potentially inform the Columbia River Treaty renegotiation and provide insights for long-term water management policies.

Managing ecological thresholds of a risky commons.

Common resources are often overexploited and appear subject to critical transitions from one stable state to another antagonistic state. Many times resulting in tragedy of the commons (TOC)-exploitation of shared resources for personal gain/payoffs, leading to worse outcomes or extinction. An adequate response would be strategic interaction, such as inspection and punishment by institutions to avoid TOC. This strategic interaction is often coupled with dynamically changing common resources. However, effect of strategic interaction in complex, coupled socio-ecological systems is less studied. Here, we develop replicator equations using evolving games in which strategy and common resources co-evolve. We consider the shared commons as fish dynamics governed by the intrinsic growth rate, predation and harvesting. The joint dynamics exhibit an oscillatory TOC, revealing that institutions need to pay special attention to intrinsic growth rate and nonlinear interaction. Our research shows that the co-evolving system exhibits a broader range of dynamics when predation is present compared to the disengaged fishery system. We conclude that the usefulness, chances and challenges of modelling co-evolutionary games to create sustainable systems merit further research.

Stochastic Thermodynamics of Multiple Co-Evolving Systems-Beyond Multipartite Processes.

Many dynamical systems consist of multiple, co-evolving subsystems (i.e., they have multiple degrees of freedom). Often, the dynamics of one or more of these subsystems will not directly depend on the state of some other subsystems, resulting in a network of dependencies governing the dynamics. How does this dependency network affect the full system's thermodynamics? Prior studies on the stochastic thermodynamics of multipartite processes have addressed this question by assuming that, in addition to the constraints of the dependency network, only one subsystem is allowed to change state at a time. However, in many real systems, such as chemical reaction networks or electronic circuits, multiple subsystems can-or must-change state together. Here, we investigate the thermodynamics of such composite processes, in which multiple subsystems are allowed to change state simultaneously. We first present new, strictly positive lower bounds on entropy production in composite processes. We then present thermodynamic uncertainty relations for information flows in composite processes. We end with strengthened speed limits for composite processes.

Selection principles for Gaia

The Gaia hypothesis considers the life-environment coupled system as a single entity that acts to regulate and maintain habitable conditions on Earth. In this paper we discuss three mechanisms which could potentially lead to Gaia: Selection by Survival, Sequential Selection and Entropic Hierarchy. We use the Tangled Nature Model of co-evolution as a common framework for investigating all three, using an extended version of the standard model to elaborate on Gaia as an example of an entropic hierarchy. This idea, which combines sequential selection together with a reservoir of diversity that acts as a ‘memory’, implies a tendency towards growth and increasing resilience of the Gaian system over time. We then discuss how Gaian memory could be realised in practice via the microbial seed bank, climate refugia and lateral gene transfer and conclude by discussing testable implications of an entropic hierarchy for the study of Earth history and the search for life in the universe. This paper adds to the existing taxonomy of Gaia hypotheses to suggest an “Entropic Gaia” where we argue that increasing biomass, complexity and enhanced habitability over time is a statistically likely feature of a co-evolving system.

On coevolution: Asymmetry in the NKCS model

The NKCS model was introduced to explore coevolutionary systems, that is, systems in which multiple species are closely interconnected. The fitness landscapes of the species are coupled to a controllable amount, where the underlying properties of the individual landscapes are also controllable. Previous work has assumed symmetry with respect to the controlling parameters. This paper explores the effects of reducing that symmetry on the behaviour of the coevolutionary system, including varying genome complexity, the degree of landscape coupling, and the use of local learning. Significant changes in behaviour from the traditional model are seen across the parameter space. These findings are suggested as particularly pertinent to symbiotic relationships.

The challenges of documenting coevolution and niche construction: The example of domestic spaces.

This essay delves into some of the challenges of studying the coevolution of humans and domestic spaces. These constructed arenas center on food preparation, and as part of the heritable niche they can shift the opportunities for, and constraints on, social interaction and cooperation in evolutionary time. Domestic spaces are widely evidenced in the archeological record, but investigators have made little progress in demonstrating causal links between proposed feedback spirals and constructed spaces of any sort. Bridging fine-scale and large-scale processes in coevolutionary systems is a complex problem that must engage higher levels of generative evolutionary theory. Archaeology nonetheless stands to offer a great deal to larger research programs by documenting and analyzing the pathways of change based on site formation processes along with evidence from subsistence refuse and technology. Choice models remain valuable tools for investigating aspects of the fine-scale feedback processes involved.

Ecological Consequences of Intraspecific Variation in Coevolutionary Systems.

AbstractThe patterns and outcomes of coevolution are expected to depend on intraspecific trait variation. Various evolutionary factors can change this variation in time. As a result, modeling coevolutionary processes solely in terms of mean trait values may not be sufficient; one may need to study the dynamics of the whole trait distribution. Here, we develop a theoretical framework for studying the effects of evolving intraspecific variation in two-species coevolutionary systems. In particular, we build and study mathematical models of competition, exploiter-victim interactions, and mutualism in which the strength of within- and between-species interactions depends on the difference in continuously varying traits between individuals reproducing asexually. We use analytical approximations based on the invasion analysis and supplement them with numerical results. We find that intraspecific variation can be maintained if stabilizing selection is weak in at least one species. When intraspecific variation is maintained under competition or mutualism, coexistence in a stable equilibrium is promoted when between-species interactions mostly happen between individuals similar in trait values. In contrast, in exploiter-victim systems coexistence typically requires strong interactions between dissimilar exploiters and victims. We show that trait distributions can become multimodal. Our approach and results contribute to the understanding of the ecological consequences of intraspecific variation in coevolutionary systems by exploring its effects on population densities and trait distributions.

Eco-evolutionary dynamics with environmental feedback: Cooperation in a changing world

Eco-evolutionary game dynamics which characterizes the mutual interactions and the coupled evolutions of strategies and environments has been of growing interest in very recent years. Since such feedback loops widely exist in a range of coevolutionary systems, such as microbial systems, social-ecological systems and psychological-economic systems, recent modeling frameworks that unveil the oscillating dynamics of social dilemmas have great potential for practical applications. In this perspective paper, we overview the latest progress of evolutionary game theory in this direction. We describe both mathematical methods and interdisciplinary applications across different fields. The ideas worthy of further consideration are discussed in prospects, with the central role of promoting cooperations in a changing world.

Microbial Interaction with Clay Minerals and Its Environmental and Biotechnological Implications

Clay minerals are very common in nature and highly reactive minerals which are typical products of the weathering of the most abundant silicate minerals on the planet. Over recent decades there has been growing appreciation that the prime involvement of clay minerals in the geochemical cycling of elements and pedosphere genesis should take into account the biogeochemical activity of microorganisms. Microbial intimate interaction with clay minerals, that has taken place on Earth’s surface in a geological time-scale, represents a complex co-evolving system which is challenging to comprehend because of fragmented information and requires coordinated efforts from both clay scientists and microbiologists. This review covers some important aspects of the interactions of clay minerals with microorganisms at the different levels of complexity, starting from organic molecules, individual and aggregated microbial cells, fungal and bacterial symbioses with photosynthetic organisms, pedosphere, up to environmental and biotechnological implications. The review attempts to systematize our current general understanding of the processes of biogeochemical transformation of clay minerals by microorganisms. This paper also highlights some microbiological and biotechnological perspectives of the practical application of clay minerals–microbes interactions not only in microbial bioremediation and biodegradation of pollutants but also in areas related to agronomy and human and animal health.

The Empirical Investigation of Non-Linear Dynamics in the Social World. Ontology, Methodology and Data

Across much of social science, linear models hold sway, but they have significant limitations. This article makes the case for studying social processes as co-evolving systems, involving non-linear dynamics. Co-evolution of species in the natural world is a blind process. In the social world in contrast, purposeful interventions by social actors are omnipresent, in their struggles for positional advantage. The article brings together co-evolving networks and purposeful social action in the Contingent Historical Model. We seek to apply this model in ways that engage with both scholarly and policy concerns. If such investigations are to be fruitful, they must not only be elaborated theoretically, they must also be applied to empirical datasets. This article considers how this can be done, with what sorts of data sets and what forms of data analysis. It takes as its specific example the international datasets on patents, as revealing processes and patterns of technological innovation. It shows how such an approach can illuminate scholarly debates and develop indicators for policy makers. Finally, it offers an agenda for research into dynamic co-evolving systems across other empirical areas.

A Co-Evolutionary Systems Design Based on Integration of Machine Learning and Optimization

A Co-Evolutionary Systems Design Based on Integration of Machine Learning and Optimization

The coevolution of contagion and behavior with increasing and decreasing awareness.

Understanding the effects of individual awareness on epidemic phenomena is important to comprehend the coevolving system dynamic, to improve forecasting, and to better evaluate the outcome of possible interventions. In previous models of epidemics on social networks, individual awareness has often been approximated as a generic personal trait that depends on social reinforcement, and used to introduce variability in state transition probabilities. A novelty of this work is to assume that individual awareness is a function of several contributing factors pooled together, different by nature and dynamics, and to study it for different epidemic categories. This way, our model still has awareness as the core attribute that may change state transition probabilities. Another contribution is to study positive and negative variations of awareness, in a contagion-behavior model. Imitation is the key mechanism that we model for manipulating awareness, under different network settings and assumptions, in particular regarding the degree of intentionality that individuals may exhibit in spreading an epidemic. Three epidemic categories are considered-disease, addiction, and rumor-to discuss different imitation mechanisms and degree of intentionality. We assume a population with a heterogeneous distribution of awareness and different response mechanisms to information gathered from the network. With simulations, we show the interplay between population and awareness factors producing a distribution of state transition probabilities and analyze how different network and epidemic configurations modify transmission patterns.

Coevolutionary systems and PageRank

Coevolutionary systems have been used successfully in various problem domains involving situations of strategic decision-making. Central to these systems is a mechanism whereby finite populations of agents compete for reproduction and adapt in response to their interaction outcomes. In competitive settings, agents choose which solutions to implement and outcomes from their behavioral interactions express preferences between the solutions. Recently, we have introduced a framework that provides both qualitative and quantitative characterizations of competitive coevolutionary systems. Its two main features are: (1) A directed graph (digraph) representation that fully captures the underlying structure arising from pairwise preferences over solutions. (2) Coevolutionary processes are modeled as random walks on the digraph. However, one needs to obtain prior, qualitative knowledge of the underlying structures of these coevolutionary digraphs to perform quantitative characterizations on coevolutionary systems and interpret the results. Here, we study a deep connection between coevolutionary systems and PageRank to address this issue. We develop a principled approach to measure and rank the performance (importance) of solutions (vertices) in a given coevolutionary digraph. In PageRank formalism, B transfers part of its authority to A if A dominates B (there is an arc from B to A in the digraph). In this manner, PageRank authority indicates the importance of a vertex. PageRank authorities with suitable normalization have a natural interpretation of long-term visitation probabilities over the digraph by the coevolutionary random walk. We derive closed-form expressions to calculate PageRank authorities for any coevolutionary digraph. We can precisely quantify changes to the authorities due to modifications in restart probability for any coevolutionary system. Our empirical studies demonstrate how PageRank authorities characterize coevolutionary digraphs with different underlying structures.

Escalation of Memory Length in Finite Populations.

The escalation of complexity is a commonly cited benefit of coevolutionary systems, but computational simulations generally fail to demonstrate this capacity to a satisfactory degree. We draw on a macroevolutionary theory of escalation to develop a set of criteria for coevolutionary systems to exhibit escalation of strategic complexity. By expanding on a previously developed model of the evolution of memory length for cooperative strategies by Kristian Lindgren, we resolve previously observed limitations on the escalation of memory length by extending operators of evolutionary variation. We present long-term coevolutionary simulations showing that larger population sizes tend to support greater escalation of complexity than smaller ones do. Additionally, we investigate the sensitivity of escalation during transitions of complexity. The Lindgren model has often been used to argue that the escalation of competitive coevolution has intrinsic limitations. Our simulations show that coevolutionary arms races can continue to escalate in computational simulations given sufficient population sizes.

Opinion formation model with co-evolution of individual behavior and social environment

Entering the information era, the formation of public opinion is largely associated with the complex system constructed by the Internet, thereby possessing new characteristics. The formation of public opinion is the result of the interaction of individual behavior with social environment. In reality, the environmental factor and the individual behavior are usually related to each other and co-evolve with time. Based on the Ising model, in this paper established is an opinion formation model that includes the process of the accumulation and digestion of the social tension. In the model, a parameter named effective dissolving factor <i>c</i> is designed to represent the extent of the interaction between the system and the social environment. A two-dimensional dynamical system is involved in the model to describe the dynamics of individual behavior and social tension. The co-evolution behavior of the system is studied. Based on the Landau mean field theory, the stationary states of the dynamical system under different parameter values, i.e. the value of effective dissolving factor <i>c</i>, their stability and bifurcation of the system, are analyzed. Finally, the computer simulation method is used to verify the results. The research shows that with the co-evolution mechanism of the system, our model exhibits certain self-organization characteristics. When the effective dissolving factor <i>c</i> is smaller than the threshold value, the system will reach final consensus opinion, resulting in a macroscopically ordered state. Otherwise, when the dissolving factor <i>c</i> exceeds a threshold value, the system is stable in the disordered state. It is interesting to find that there is such a critical value of the parameter that it leads the system to be self-organized into a critical state from any initial state. The future detailed investigation on the criticality of the co-evolving system is also suggested, such as testing whether the system has evolved into the critical state according to the finite-sized scaling theory and calculating the critical exponent of the system. In addition, in this paper provided is a new perspective to tackle practical problems in public opinion. Based on the mechanism of the formation of public opinion revealed by our model, researchers are encouraged to conduct studies on how to monitor the state of public opinion more precisely and to predict the tipping point of the system evolution.

Defense Responses and Changes in Symbiotic Gut Microflora in the Colorado Potato Beetle Leptinotarsa decemlineata under the Effect of Endophytic Bacteria from the Genus Bacillus

Phytophagous insects and host plants have a complex of microsymbionts and make up a united co-evolving system with them. Microsymbiotic complexes are actively involved in stress responses of macrosymbionts. We established that a treatment of potato plants with endophytic bacterial strains Bacillus thuringiensis var. thuringiensis-5689, B. th. var. kurstaki-5351, and Bacillus subtilis 26D decreased the survival rate of the plant feeder, Colorado potato beetle Leptinotarsa decemlineata Say. The B. th. strains suppressed phenoloxidase and acetylcholinesterase activities in the beetle hemolymph. An antagonistic relationship was found between endophytic bacteria B. subtilis 26D and beetle symbiotic bacteria from the genera Acinetobacter and Enterobacter, with the former being able to suppress the growth of endophytic colonies. The recombinant B. subtilis strain 26D Cry, containing the B. th. var. kurstaki δ-endotoxin cry1Ia gene, combined the ability of the original B. subtilis 26D strain to suppress the development of beetle symbionts and immune responses with a production of the Cry toxin, thus leading to a high mortality of the phytophage.

Polyadic Entropy, Synergy and Redundancy among Statistically Independent Processes in Nonlinear Statistical Physics with Microphysical Codependence.

The information shared among observables representing processes of interest is traditionally evaluated in terms of macroscale measures characterizing aggregate properties of the underlying processes and their interactions. Traditional information measures are grounded on the assumption that the observable represents a memoryless process without any interaction among microstates. Generalized entropy measures have been formulated in non-extensive statistical mechanics aiming to take microphysical codependence into account in entropy quantification. By taking them into consideration when formulating information measures, the question is raised on whether and if so how much information permeates across scales to impact on the macroscale information measures. The present study investigates and quantifies the emergence of macroscale information from microscale codependence among microphysics. In order to isolate the information emergence coming solely from the nonlinearly interacting microphysics, redundancy and synergy are evaluated among macroscale variables that are statistically independent from each other but not necessarily so within their own microphysics. Synergistic and redundant information are found when microphysical interactions take place, even if the statistical distributions are factorable. These findings stress the added value of nonlinear statistical physics to information theory in coevolutionary systems.

A New Framework for Analysis of Coevolutionary Systems—Directed Graph Representation and Random Walks

Studying coevolutionary systems in the context of simplified models (i.e., games with pairwise interactions between coevolving solutions modeled as self plays) remains an open challenge since the rich underlying structures associated with pairwise-comparison-based fitness measures are often not taken fully into account. Although cyclic dynamics have been demonstrated in several contexts (such as intransitivity in coevolutionary problems), there is no complete characterization of cycle structures and their effects on coevolutionary search. We develop a new framework to address this issue. At the core of our approach is the directed graph (digraph) representation of coevolutionary problems that fully captures structures in the relations between candidate solutions. Coevolutionary processes are modeled as a specific type of Markov chains-random walks on digraphs. Using this framework, we show that coevolutionary problems admit a qualitative characterization: a coevolutionary problem is either solvable (there is a subset of solutions that dominates the remaining candidate solutions) or not. This has an implication on coevolutionary search. We further develop our framework that provides the means to construct quantitative tools for analysis of coevolutionary processes and demonstrate their applications through case studies. We show that coevolution of solvable problems corresponds to an absorbing Markov chain for which we can compute the expected hitting time of the absorbing class. Otherwise, coevolution will cycle indefinitely and the quantity of interest will be the limiting invariant distribution of the Markov chain. We also provide an index for characterizing complexity in coevolutionary problems and show how they can be generated in a controlled manner.

Modelling coevolution in ecological networks with adaptive dynamics

Coevolution can impose density‐dependent selection through reciprocal biotic interactions on the fitness of involved species, driving directional and disruptive trait evolution and rich evolutionary possibilities. Coevolution has since Darwin been considered a potential path leading to adaptive diversification that could explain the emergence of ecological networks of biotic interactions that harbour multiple interacting species (eg, pollination networks and food webs). Here, we present adaptive dynamics, a powerful tool of evolutionary invasion analysis that explores how quantitative traits undergo incremental evolution, to exploring the emergence of multi‐species networks through coevolution. Specifically, we exemplify the feasibility of using adaptive dynamics to investigate trait evolution in 4 ecological networks, driven, respectively, by resource competition, trophic interactions, as well as bipartite mutualistic and antagonistic interactions. We use a set of ordinary differential equations to describe, at different paces, the population dynamics and trait dynamics of involved species assemblages. Through computing ecological equilibrium, invasion fitness, selection gradient and evolutionary singularity, and testing for evolutionary stability and the coexistence criterion of mutual invasibility, we illustrate the typical evolutionary dynamics and the criteria of evolutionary stability and branching in these ecological networks. Results highlight the importance of the form of trait‐mediated interaction kernel (ie, interaction strength as a function of trait difference) to adaptive diversification in these coevolutionary systems. We conclude by advocating that biotic interactions between two species can indeed lead to diffuse and even escape‐and‐radiate coevolution, making the emerged ecological networks an ideal model for studying complex adaptive systems.