Symbiosis Enables the Evolution of Rare Complexes in Structured Environments

Mills, Rob; Watson, Richard A.

doi:10.1007/978-3-642-21314-4_14

Cited by 7 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The grouping of evolved species or symbioses can be understood as a decomposition of the problem variables into sub-sets that are approximately independent from one another but strongly interdependent internally. The scalability of an associative optimisation process based on these principles is also shown to be algorithmically superior to non-associative evolution in a formal sense [85,46,84]. The associative memory principles discussed here may therefore help us better understand the mechanisms of major evolutionary transitions [42] (developed elsewhere [83,84]).…”

Section: Related Workmentioning

confidence: 99%

“…[12]) but in optimisation the ability to produce new combinations of successful features is highly-desirable [21,77,80,81]. Mills [46,47,44] shows that the automatic discovery and utilisation of modular structure in an optimisation problem, as facilitated by learned associations, can be used to provide significant optimisation performance (see also [26,27,84,93]). This result thereby shows that a distributed optimisation process, based on nothing more than repeated relaxation of state configurations plus local selfish reinforcement of connections has the effect not only of creating an associative memory of its past local optimisation behaviour but also generalising its past behaviour and enabling superior optimisation and, in the context of a multi-agent system, global adaptation.…”

Section: Memory Optimisation and Generalisationmentioning

confidence: 99%

See 1 more Smart Citation

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

2011

Self Cite

View full text Add to dashboard Cite

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...

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Memory Optimisation and Generalisationmentioning

confidence: 99%

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

2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…The evolution of markers in this way can be seen as construction of an individual's social environment [12]. Group selection can be facilitated by such a process, as shown here and in other work [13,14]. In conventional altruist/ cheat dynamics involving the evolution of assortative markers there exists a possibility of cheats evolving the phenotypic altruistic marker, such as a green beard [2], signalling altruistic intent without actually posessing a cooperative gene.…”

Section: Discussionmentioning

confidence: 97%

Moderate Contact between Sub-populations Promotes Evolved Assortativity Enabling Group Selection

Snowdon

Powers

Watson

2011

Advances in Artificial Life. Darwin Meets Von Neumann

Self Cite

View full text Add to dashboard Cite

Abstract. Group selection is easily observed when spatial group structure is imposed on a population. In fact, spatial structure is just a means of providing assortative interactions such that the benefits of cooperating are delivered to other cooperators more than to selfish individuals. In principle, assortative interactions could be supported by individually adapted traits without physical grouping. But this possibility seems to be ruled-out because any 'marker' that cooperators used for this purpose could be adopted by selfish individuals also. However, here we show that stable assortative marking can evolve when sub-populations at different evolutionarily stable strategies (ESSs) are brought into contact. Interestingly, if they are brought into contact too quickly, individual selection causes loss of behavioural diversity before assortative markers have a chance to evolve. But if they are brought into contact slowly, moderate initial mixing between sub-populations produces a pressure to evolve traits that facilitate assortative interactions. Once assortative interactions have become established, group competition between the two ESSs is facilitated without any spatial group structure. This process thus illustrates conditions where individual selection canalises groups that are initially spatially defined into stable groups that compete without the need for continued spatial separation.

show abstract

“…AA vs BB) as would be more conventional in a single-species assortative grouping model (where relatedness and inclusive fitness concepts straightforwardly apply) ( 12). By using a polyspecies model we can show that the process we model significantly increases the likelihood of reaching a higher-utility ESS even in cases where the basin of attraction for high-utility ESSs is initially very small ( 13). Note that we do not change the interaction coefficients between species but only change the co-location or interaction probability of species.…”

mentioning

confidence: 98%

“…These small groups form because the co-occurrence of species within each sub-function is more reliable than the co-occurrence of species in different sub-functions. In ( 13) we provide a model where we assume that relationships form in a manner that reflects species cooccurrence at ESSs and show that this is sufficient to produce the same effects on attractors as those shown here. Using this abstraction we are also able to assess the scalability of the effect and show that it can evolve rare, high-fitness complexes that are unevolvable via non-associative evolution.…”

mentioning

confidence: 99%

Can Selfish Symbioses Effect Higher-Level Selection?

Watson

Palmius

Mills

et al. 2011

Advances in Artificial Life. Darwin Meets Von Neumann

Self Cite

View full text Add to dashboard Cite

Abstract. The role of symbiosis in macro-evolution is poorly understood. On the one hand, symbiosis seems to be a perfectly normal manifestation of individual selection, on the other hand, in some of the major transitions in evolution it seems to be implicated in the creation of new higher-level units of selection. Here we present a model of individual selection for symbiotic relationships where individuals can genetically specify traits which partially control which other species they associate with -i.e. they can evolve species-specific grouping. We find that when the genetic evolution of symbiotic relationships occurs slowly compared to ecological population dynamics, symbioses form which canalise the combinations of species that commonly occur at local ESSs into new units of selection. Thus even though symbioses will only evolve if they are beneficial to the individual, we find that the symbiotic groups that form are selectively significant and result in combinations of species that are more cooperative than would be possible under individual selection. These findings thus provide a systematic mechanism for creating significant higher-level selective units from individual selection, and support the notion of a significant and systematic role of symbiosis in macro-evolution.

show abstract

Symbiosis Enables the Evolution of Rare Complexes in Structured Environments

Cited by 7 publications

References 14 publications

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

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

Moderate Contact between Sub-populations Promotes Evolved Assortativity Enabling Group Selection

Can Selfish Symbioses Effect Higher-Level Selection?

Contact Info

Product

Resources

About