Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Network Modularity is essential for evolution of cooperation under uncertainty.

ABSTRACT: Cooperative behavior, which pervades nature, can be significantly enhanced when agents interact in a structured rather than random way; however, the key structural factors that affect cooperation are not well understood. Moreover, the role structure plays with cooperation has largely been studied through observing overall cooperation rather than the underlying components that together shape cooperative behavior. In this paper we address these two problems by first applying evolutionary games to a wide range of networks, where agents play the Prisoner's Dilemma with a three-component stochastic strategy, and then analyzing agent-based simulation results using principal component analysis. With these methods we study the evolution of trust, reciprocity and forgiveness as a function of several structural parameters. This work demonstrates that community structure, represented by network modularity, among all the tested structural parameters, has the most significant impact on the emergence of cooperative behavior, with forgiveness showing the largest sensitivity to community structure. We also show that increased community structure reduces the dispersion of trust and forgiveness, thereby reducing the network-level uncertainties for these two components; graph transitivity and degree also significantly influence the evolutionary dynamics of the population and the diversity of strategies at equilibrium.

SUBMITTER: Gianetto DA

PROVIDER: S-EPMC4388161 | biostudies-other | 2015

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Spontaneous evolution of modularity and network motifs.

Project description:Biological networks have an inherent simplicity: they are modular with a design that can be separated into units that perform almost independently. Furthermore, they show reuse of recurring patterns termed network motifs. Little is known about the evolutionary origin of these properties. Current models of biological evolution typically produce networks that are highly nonmodular and lack understandable motifs. Here, we suggest a possible explanation for the origin of modularity and network motifs in biology. We use standard evolutionary algorithms to evolve networks. A key feature in this study is evolution under an environment (evolutionary goal) that changes in a modular fashion. That is, we repeatedly switch between several goals, each made of a different combination of subgoals. We find that such "modularly varying goals" lead to the spontaneous evolution of modular network structure and network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Such switching between related goals may represent biological evolution in a changing environment that requires different combinations of a set of basic biological functions. The present study may shed light on the evolutionary forces that promote structural simplicity in biological networks and offers ways to improve the evolutionary design of engineered systems.

| S-EPMC1236541 | biostudies-literature

Modularity in the evolution of yeast protein interaction network.

Project description:Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.

| S-EPMC4403033 | biostudies-other

Evolution of conditional cooperation under multilevel selection.

Project description:We study the emergence of conditional cooperation in the presence of both intra-group and inter-group selection. Individuals play public goods games within their groups using conditional strategies, which are represented as piecewise linear response functions. Accordingly, groups engage in conflicts with a certain probability. In contrast to previous studies, we consider continuous contribution levels and a rich set of conditional strategies, allowing for a wide range of possible interactions between strategies. We find that the existence of conditional strategies enables the stabilization of cooperation even under strong intra-group selection. The strategy that eventually dominates in the population has two key properties: (i) It is unexploitable with strong intra-group selection; (ii) It can achieve full contribution to outperform other strategies in the inter-group selection. The success of this strategy is robust to initial conditions as well as changes to important parameters. We also investigate the influence of different factors on cooperation levels, including group conflicts, group size, and migration rate. Their effect on cooperation can be attributed to and explained by their influence on the relative strength of intra-group and inter-group selection.

| S-EPMC4786827 | biostudies-other

Causal Inference Under Interference And Network Uncertainty.

Project description:Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly [10, 36, 20], but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.

| S-EPMC6935347 | biostudies-literature

Evolution of cooperation and skew under imperfect information.

Project description:The evolution of cooperation in nature and human societies depends crucially on how the benefits from cooperation are divided and whether individuals have complete information about their payoffs. We tackle these questions by adopting a methodology from economics called mechanism design. Focusing on reproductive skew as a case study, we show that full cooperation may not be achievable due to private information over individuals' outside options, regardless of the details of the specific biological or social interaction. Further, we consider how the structure of the interaction can evolve to promote the maximum amount of cooperation in the face of the informational constraints. Our results point to a distinct avenue for investigating how cooperation can evolve when the division of benefits is flexible and individuals have private information.

| S-EPMC3443135 | biostudies-literature

Evolution of the global terrorist organizational cooperation network.

Project description:Terrorism has shown a trend of organizational cooperation in a large number of terrorist attacks around the world, posting a great challenge to counter-terrorism efforts. To investigate the trend and pattern of global terrorist organizational cooperations and to propose effective measures for effectively enforcing and restricting terrorist attacks, based on the Global Terrorism Database and the UN sanctions list of terrorist groups, this study constructs a cooperative evolutionary network of terrorist organizations from 119,803 terrorist attacks that occurred globally between 2001 and 2018. The evolution of worldwide terrorist cooperation is evaluated in terms of network characteristics, including key nodes, cohesion, and motifs. The network keeps expanding, with a large number of new nodes emerging each year. On average, there are 13 additional organizations entering in the collaboration network each year, with a yearly survival rate of about 34.66%, and the rank of node importance iterate and update frequently. Through k-core decomposition, for which the breakdown of the network has increased from three to five partitions, we find that the core of the terrorist organization's cooperation network changes much less frequently than the edges. The dominating modal structure of the network is the "star" motif (90%), and "triadic closed" motif (9%). We conclude that, over time, the cooperative network of terrorist groups has gradually evolved into a cluster of star-shaped networks, with various organizations serving as the centers of the networks and showing core-periphery structure in their individual communities. The core organizations are highly connected and stable, whereas the periphery organizations are loosely connected and highly variable.

| S-EPMC10824412 | biostudies-literature

Proteome evolution under essential resource limitation

Project description:Resource limitation is a major driver of ecological and evolutionary dynamics of organisms. Short-term responses to resource limitation include plastic changes in molecular phenotypes including protein expression. Yet little is known about the evolution of the molecular phenotype under longer-term resource limitation. Here, we combine experimental evolution of the green alga Chlamydomonas reinhardtii under multiple different non-substitutable resource limitation regimes with proteomic measurements to investigate evolutionary adaptation of the molecular phenotype. We demonstrate convergent proteomic evolution of core metabolic functions, including the Calvin-Benson cycle and gluconeogenesis, across different resource limitation selection environments. We did not observe proteomic changes consistent with optimized uptake of the different particular limiting resources.

2018-11-08 | PXD010847 | Pride

Primate modularity and evolution: first anatomical network analysis of primate head and neck musculoskeletal system.

Project description:Network theory is increasingly being used to study morphological modularity and integration. Anatomical network analysis (AnNA) is a framework for quantitatively characterizing the topological organization of anatomical structures and providing an operational way to compare structural integration and modularity. Here we apply AnNA for the first time to study the macroevolution of the musculoskeletal system of the head and neck in primates and their closest living relatives, paying special attention to the evolution of structures associated with facial and vocal communication. We show that well-defined left and right facial modules are plesiomorphic for primates, while anthropoids consistently have asymmetrical facial modules that include structures of both sides, a change likely related to the ability to display more complex, asymmetrical facial expressions. However, no clear trends in network organization were found regarding the evolution of structures related to speech. Remarkably, the increase in the number of head and neck muscles - and thus of musculoskeletal structures - in human evolution led to a decrease in network density and complexity in humans.

| S-EPMC5799162 | biostudies-other

Prepositioning network design for humanitarian relief purposes under correlated demand uncertainty.

Project description:Prepositioning relief network is an effective strategy to mitigate the impact of natural disasters and public health emergencies, such as the COVID-19 pandemic. However, designing a proper network is challenging due to limited information and, more importantly, the correlated demand uncertainty that exists among affected areas. We consider a network design problem for humanitarian relief purposes, where demand correlations exist and demand information is limited, i.e., only the mean demand and covariance matrix are known. Note that the covariance matrix can explicitly capture the correlated demand among areas. We formulate this problem as a mixed-integer two-stage distributionally robust location-inventory model, which is generally NP-hard and computationally intractable. The model is further reformulated as a mixed-integer conic problem based on copositive cones, and it is tractable with positive semidefinite relaxation. To accelerate the problem-solving process, we design an interpretable branching-and-pricing heuristic with a warm start. Both semi-case study and simulation results demonstrate that explicitly modelling demand correlation can decrease unmet demand.

| S-EPMC10265446 | biostudies-literature

Evolution of cooperation in a heterogeneous graph: fixation probabilities under weak selection.

Project description:It has been shown that natural selection favors cooperation in a homogenous graph if the benefit-to-cost ratio exceeds the degree of the graph. However, most graphs related to interactions in real populations are heterogeneous, in which some individuals have many more neighbors than others. In this paper, we introduce a new state variable to measure the time evolution of cooperation in a heterogeneous graph. Based on the diffusion approximation, we find that the fixation probability of a single cooperator depends crucially on the number of its neighbors. Under weak selection, a cooperator with more neighbors has a larger probability of fixation in the population. We then investigate the average fixation probability of a randomly chosen cooperator. If a cooperator pays a cost for each of its neighbors (the so called fixed cost per game case), natural selection favors cooperation if the benefit-to-cost ratio is larger than the average degree. In contrast, if a cooperator pays a fixed cost and all its neighbors share the benefit (the fixed cost per individual case), cooperation is favored if the benefit-to-cost ratio is larger than the harmonic mean of the degree distribution. Moreover, increasing the graph heterogeneity will reduce the effect of natural selection.

| S-EPMC3688584 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data