24,884 research outputs found
Modeling the ecology and evolution of biodiversity: Biogeographical cradles, museums, and graves
Individual processes shaping geographical patterns of biodiversity are increasingly understood, but their complex interactions on broad spatial and temporal scales remain beyond the reach of analytical models and traditional experiments. To meet this challenge, we built a spatially explicit, mechanistic simulation model implementing adaptation, range shifts, fragmentation, speciation, dispersal, competition, and extinction, driven by modeled climates of the past 800,000 years in South America. Experimental topographic smoothing confirmed the impact of climate heterogeneity on diversification. The simulations identified regions and episodes of speciation (cradles), persistence (museums), and extinction (graves). Although the simulations had no target pattern and were not parameterized with empirical data, emerging richness maps closely resembled contemporary maps for major taxa, confirming powerful roles for evolution and diversification driven by topography and climate
A Multi-scale View of the Emergent Complexity of Life: A Free-energy Proposal
We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run entropy) of its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast natural selection in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume; namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales
On the sympatric evolution and evolutionary stability of coexistence by relative nonlinearity of competition
If two species exhibit different nonlinear responses to a single shared
resource, and if each species modifies the resource dynamics such that this
favors its competitor, they may stably coexist. This coexistence mechanism,
known as relative nonlinearity of competition, is well understood
theoretically, but less is known about its evolutionary properties and its
prevalence in real communities. We address this challenge by using adaptive
dynamics theory and individual-based simulations to compare community
stabilization and evolutionary stability of species that coexist by relative
nonlinearity. In our analysis, evolution operates on the species'
density-compensation strategies, and we consider a trade-off between population
growth rates at high and low resource availability. We confirm previous
findings that, irrespective of the particular model of density dependence,
there are many combinations of overcompensating and undercompensating
density-compensation strategies that allow stable coexistence by relative
nonlinearity. However, our analysis also shows that most of these strategy
combinations are not evolutionarily stable and will be outcompeted by an
intermediate density-compensation strategy. Only very specific trade-offs lead
to evolutionarily stable coexistence by relative nonlinearity. As we find no
reason why these particular trade-offs should be common in nature, we conclude
that the sympatric evolution and evolutionary stability of relative
nonlinearity, while possible in principle, seems rather unlikely. We speculate
that this may, at least in part, explain why empirical demonstrations of this
coexistence mechanism are rare, noting, however, that the difficulty to detect
relative nonlinearity in the field [...]Comment: PLOS ONE, in pres
Lock-in & Break-out from Technological Trajectories: Modeling and policy implications
Arthur [1,2] provided a model to explain the circumstances that lead to
technological lock-in into a specific trajectory. We contribute substantially
to this area of research by investigating the circumstances under which
technological development may break-out of a trajectory. We argue that for this
to happen, a third selection mechanism--beyond those of the market and of
technology--needs to upset the lock-in. We model the interaction, or mutual
shaping among three selection mechanisms, and thus this paper also allows for a
better understanding of when a technology will lock-in into a trajectory, when
a technology may break-out of a lock-in, and when competing technologies may
co-exist in a balance. As a system is conceptualized to gain a (third) degree
of freedom, the possibility of bifurcation is introduced into the model. The
equations, in which interactions between competition and selection mechanisms
can be modeled, allow one to specify conditions for lock-in, competitive
balance, and break-out
Organic Selection and Social Heredity: The Original Baldwin Effect Revisited
The so-called “Baldwin Effect” has been studied for years
in the fields of Artificial Life, Cognitive Science, and Evolutionary
Theory across disciplines. This idea is often conflated
with genetic assimilation, and has raised controversy
in trans-disciplinary scientific discourse due to the many interpretations
it has. This paper revisits the “Baldwin Effect”
in Baldwin’s original spirit from a joint historical, theoretical
and experimental approach. Social Heredity – the inheritance
of cultural knowledge via non-genetic means in Baldwin’s
term – is also taken into consideration. I shall argue that the
Baldwin Effect can occur via social heredity without necessity
for genetic assimilation. Computational experiments are
carried out to show that when social heredity is permitted with
high fidelity, there is no need for the assimilation of acquired
characteristics; instead the Baldwin Effect occurs as promoting
more plasticity to facilitate future intelligence. The role
of mind and intelligence in evolution and its implications in
an extended synthesis of evolution are briefly discussed
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks
a variational approach to niche construction
In evolutionary biology, niche construction is sometimes described as a genuine evolutionary process whereby organisms, through their activities and regulatory mechanisms, modify their environment such as to steer their own evolutionary trajectory, and that of other species. There is ongoing debate, however, on the extent to which niche construction ought to be considered a bona fide evolutionary force, on a par with natural selection. Recent formulations of the variational free-energy principle as applied to the life sciences describe the properties of living systems, and their selection in evolution, in terms of variational inference. We argue that niche construction can be described using a variational approach. We propose new arguments to support the niche construction perspective, and to extend the variational approach to niche construction to current perspectives in various scientific fields
Robot life: simulation and participation in the study of evolution and social behavior.
This paper explores the case of using robots to simulate evolution, in particular the case of Hamilton's Law. The uses of robots raises several questions that this paper seeks to address. The first concerns the role of the robots in biological research: do they simulate something (life, evolution, sociality) or do they participate in something? The second question concerns the physicality of the robots: what difference does embodiment make to the role of the robot in these experiments. Thirdly, how do life, embodiment and social behavior relate in contemporary biology and why is it possible for robots to illuminate this relation? These questions are provoked by a strange similarity that has not been noted before: between the problem of simulation in philosophy of science, and Deleuze's reading of Plato on the relationship of ideas, copies and simulacra
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