6,417 research outputs found
Complex eco-evolutionary dynamics induced by the coevolution of predator–prey movement strategies
The coevolution of predators and prey has been the subject of much empirical and theoretical research that produced intriguing insights into the interplay of ecology and evolution. To allow for mathematical analysis, models of predator–prey coevolution are often coarse-grained, focussing on population-level processes and largely neglecting individual-level behaviour. As selection is acting on individual-level properties, we here present a more mechanistic approach: an individual-based simulation model for the coevolution of predators and prey on a fine-grained resource landscape, where features relevant for ecology (like changes in local densities) and evolution (like differences in survival and reproduction) emerge naturally from interactions between individuals. Our focus is on predator–prey movement behaviour, and we present a new method for implementing evolving movement strategies in an efficient and intuitively appealing manner. Throughout their lifetime, predators and prey make repeated movement decisions on the basis of their movement strategies. Over the generations, the movement strategies evolve, as individuals that successfully survive and reproduce leave their strategy to more descendants. We show that the movement strategies in our model evolve rapidly, thereby inducing characteristic spatial patterns like spiral waves and static spots. Transitions between these patterns occur frequently, induced by antagonistic coevolution rather than by external events. Regularly, evolution leads to the emergence and stable coexistence of qualitatively different movement strategies within the same population. Although the strategy space of our model is continuous, we often observe the evolution of discrete movement types. We argue that rapid evolution, coexistent movement types, and phase shifts between different ecological regimes are not a peculiarity of our model but a result of more realistic assumptions on eco-evolutionary feedbacks and the number of evolutionary degrees of freedom
Diversity, competition, extinction: the ecophysics of language change
As early indicated by Charles Darwin, languages behave and change very much
like living species. They display high diversity, differentiate in space and
time, emerge and disappear. A large body of literature has explored the role of
information exchanges and communicative constraints in groups of agents under
selective scenarios. These models have been very helpful in providing a
rationale on how complex forms of communication emerge under evolutionary
pressures. However, other patterns of large-scale organization can be described
using mathematical methods ignoring communicative traits. These approaches
consider shorter time scales and have been developed by exploiting both
theoretical ecology and statistical physics methods. The models are reviewed
here and include extinction, invasion, origination, spatial organization,
coexistence and diversity as key concepts and are very simple in their defining
rules. Such simplicity is used in order to catch the most fundamental laws of
organization and those universal ingredients responsible for qualitative
traits. The similarities between observed and predicted patterns indicate that
an ecological theory of language is emerging, supporting (on a quantitative
basis) its ecological nature, although key differences are also present. Here
we critically review some recent advances lying and outline their implications
and limitations as well as open problems for future research.Comment: 17 Pages. A review on current models from statistical Physics and
Theoretical Ecology applied to study language dynamic
Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets
This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical-econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models -based on classes of Neural Network (NN) techniques-that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests
Evolutionary games on multilayer networks: A colloquium
Networks form the backbone of many complex systems, ranging from the Internet
to human societies. Accordingly, not only is the range of our interactions
limited and thus best described and modeled by networks, it is also a fact that
the networks that are an integral part of such models are often interdependent
or even interconnected. Networks of networks or multilayer networks are
therefore a more apt description of social systems. This colloquium is devoted
to evolutionary games on multilayer networks, and in particular to the
evolution of cooperation as one of the main pillars of modern human societies.
We first give an overview of the most significant conceptual differences
between single-layer and multilayer networks, and we provide basic definitions
and a classification of the most commonly used terms. Subsequently, we review
fascinating and counterintuitive evolutionary outcomes that emerge due to
different types of interdependencies between otherwise independent populations.
The focus is on coupling through the utilities of players, through the flow of
information, as well as through the popularity of different strategies on
different network layers. The colloquium highlights the importance of pattern
formation and collective behavior for the promotion of cooperation under
adverse conditions, as well as the synergies between network science and
evolutionary game theory.Comment: 14 two-column pages, 8 figures; accepted for publication in European
Physical Journal
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