Can Selfish Symbioses Effect Higher-Level Selection?

Watson, Richard A.; Palmius, Niclas; Mills, Rob; Powers, Simon T.; Penn, Alexandra S.

doi:10.1007/978-3-642-21314-4_4

Cited by 15 publications

(19 citation statements)

References 18 publications

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“…In an evolutionary scenario, an individual-based simulation [85,83] supports the conclusion that individuals will evolve associations that reinforce locally stable equilibria as shown here. More exactly, this work shows that associations canalise common components of locally stable equilibria -in other words, they create groups that reflect the commonly occurring sub-patterns of configurations that are visited not the entire configuration patterns.…”

Section: Related Workmentioning

confidence: 69%

Global Adaptation in Networks of Selfish Components: Emergent Associative Memory at the System Scale

2011

Self Cite

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In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning.Keywords: self-organisation, adaptive networks, Hebbian learning, multi-agent systems, social networks, emergent computation, games on networks. Selfish changes to connections and global adaptationOne of the key open questions in the field of artificial life and adaptive systems is how, if at all, it is possible that a complex system that is not evolved can exhibit adaptation or increased functionality without design. has the potential to play a role in the formation of pre-biotic organisations, or in moving from one level of biological organisation to another [42], for example, but theory to understand exactly what this means or how it might work is limited. In contrast, theory to understand distributed adaptive processes in neural networks is well-developed, but generally assumed to be relevant only to brains and nervous systems 'programmed' to exhibit such adaptation. Here we show that organisational principles familiar in learning neural networks emerge spontaneously in distributed...

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Section: Related Workmentioning

confidence: 69%

Global Adaptation in Networks of Selfish Components: Emergent Associative Memory at the System Scale

2011

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“…In evolutionary systems, selection at one level of organisation can operate like unsupervised learning at a higher level of organisation (Box 2, iii) [69]. Abstract models incorporating these features show that individual-level selection can thus prime the systematic formation of adaptive higher-level evolutionary units without presupposing selection at the higher level [77,78]. New optimisation methods based on these principles demonstrate problem-solving capabilities that cannot be achieved with single-level adaptation [77,79].…”

Section: [ 4 _ T D $ D I F F ] Evo-ego: the Evolution Of Individualitmentioning

confidence: 99%

“…This includes new modes of reproduction modifying the heritability of collectives [40,78] (e.g., vertical transmission of symbionts, as in the origin of eukaryote organelles [83,84]), the origin of chromosomes (via physical linkage of previously independently replicating genetic material [85]), changing reproduction from migrant pool reproduction to group fissioning [71,76], or encapsulation in compartments (e.g., cell membranes, as in evolutionary transition from replicators on a surface to replicators in compartments) [72,84]. Evolutionary connectionism: a developing theory for the evolution of biological organisation based on the hypothesis that the positive feedback between network topology and behaviour, well understood in neural network models (e.g., Hebbian learning), is common to the evolution of developmental, ecological, and reproductive organisations [32,34,65,68,69,78]. Hebbian learning: learning that occurs by altering the strength of synaptic connections between neurons [6,14,29].…”

Section: Glossarymentioning

confidence: 99%

How Can Evolution Learn?

Watson

Szathmary

2016

Trends in Ecology & Evolution

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The theory of evolution links random variation and selection to incremental adaptation. In a different intellectual domain, learning theory links incremental adaptation (e.g., from positive and/or negative reinforcement) to intelligent behaviour. Specifically, learning theory explains how incremental adaptation can acquire knowledge from past experience and use it to direct future behaviours toward favourable outcomes. Until recently such cognitive learning seemed irrelevant to the 'uninformed' process of evolution. In our opinion, however, new results formally linking evolutionary processes to the principles of learning might provide solutions to several evolutionary puzzles -the evolution of evolvability, the evolution of ecological organisation, and evolutionary transitions in individuality. If so, the ability for evolution to learn might explain how it produces such apparently intelligent designs. Learning and EvolutionNew insights and new ways of understanding are often provided by analogies. Analogous reasoning is regarded as a core faculty of human cognition [1], and necessary for complex abstract causal reasoning [2]. In biology, analogy is sometimes considered to be the poor cousin of homology -similar, but not really the same. But in science more generally, analogies can be founded on perfect equivalences, for example, mathematical isomorphisms or algorithmic equivalence, thus enabling the transfer of ready-made results from one system or discipline to another, for example, between quasispecies theory and population genetics [3,4], electromagnetic fields and hydrodynamics [5], and magnetism and neural networks [6]. The previously casual analogy between learning systems and evolution by natural selection has recently been deepened to a level where such transfer can begin.How Intelligent is Evolution? Evolution is sometimes likened to an active problem solver, seeking out ingenious solutions to difficult environmental challenges. The solutions discovered by evolution can certainly appear ingenious. Mechanistically, however, there appear to be good reasons to doubt that cognitive problem solving and evolution are equivalent in any real sense. For example, cognitive problem solving can utilise past knowledge about a problem domain to 'anticipate' future outcomes and direct exploration of solutions, whereas evolutionary exploration is myopic and dependent on undirected variation. Intelligent problem solvers can also form high-level or modular representations of a problem, making it easier to reuse partial solutions in new contexts, whereas evolution merely plods on, filtering random replication errors.Yet, this is not the whole story. Whilst genetic variation might be undirected, the pattern of phenotypic variation is shaped and biased by the processes of development. Moreover, the organisation of developmental processes (from gene regulatory interactions to morphological body plans) is itself, in large part, a product of past evolution. This affords the possibility that random genetic changes might produce...

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“…The proposed mechanism is simpler than those suggested in previous models [10][11][12], and gives rise to qualitatively distinct results. In other work, we use a model that has an explicit population within each species, and each individual can evolve species-specific symbiotic associations (i.e., the associations are individual traits) [8]. We use this model to explore the types of population structure that lead to the evolution of adaptively significant associations.…”

Section: Discussionmentioning

confidence: 99%

“…This provides insight into why there is a distinction in the evolutionary outcomes with and without association formation. In other work in this volume [8], we show conditions under which individual selection leads to the reinforcement of associations between individuals of different species that co-occur in the ecosystem dynamics. In the present paper, we use a higher-level model where associations evolve between species according to their co-occurrence.…”

Section: Introductionmentioning

confidence: 99%