Neurons, viscose fluids, freshwater polyp hydra-and self-organizing information systems

Heylighen, Francis; Gershenson, Carlos; Staab, S.; Flake, Gary William; Pennock, David M.; Fain, Daniel C.; Roure, David De; Aberer, Karl; Shen, Wei-Min; Dousse, Olivier; Thiran, Patrick

doi:10.1109/mis.2003.1217631

Cited by 58 publications

(15 citation statements)

References 28 publications

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“…Given that in a distributed complex adaptive system, there is no central mechanism to store and reapply the best result of previous experience, a mechanism that causes a dynamical system to increase the probability or reliability of visiting good configurations that have been visited in the past is significant for many types of engineered complex adaptive systems25–27. Importantly, the mechanism demonstrated above is extremely simple and completely distributed.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Optimization in “self-modeling” complex adaptive systems

2010

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When a dynamical system with multiple point attractors is released from an arbitrary initial condition, it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimizes these constraints by this method is unlikely or may take many attempts. Here, we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimize total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ' 'recalling' ' low energy states that have been previously visited but ' 'predicting' ' their location by generalizing over local attractor states that have already been visited. This ' 'self-modeling' ' framework, i.e., a system that augments its behavior with an associative memory of its own attractors, helps us better understand the conditions under which a simple locally mediated mechanism of self-organization can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph coloring and distributed task allocation problems.

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Section: Discussion and Related Workmentioning

confidence: 99%

Optimization in “self-modeling” complex adaptive systems

2010

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“…For example, in technological systems, it is often convenient or necessary to devolve control to numerous autonomous components or agents that each, in a fairly simple manner, acts to optimise a global performance criterion: e.g. communications routing agents act to minimise calls dropped, or processing nodes in a grid computing system each act to maximise the number of jobs processed (1,2). However, since each component in the network acts individually, i.e., using only local information, constraints between individuals can remain unsatisfied, resulting in poorly optimised global performance.…”

Section: Selfish Agents and Total Utilitymentioning

confidence: 99%

“If You Can't Be With the One You Love, Love the One You're With”: How Individual Habituation of Agent Interactions Improves Global Utility

et al. 2011

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Simple distributed strategies that modify the behaviour of selfish individuals in a manner that enhances cooperation or global efficiency have proved difficult to identify. We consider a network of selfish agents who each optimise their individual utilities by coordinating (or anti-coordinating) with their neighbours, to maximise the pay-offs from randomly weighted pair-wise games. In general, agents will opt for the behaviour that is the best compromise (for them) of the many conflicting constraints created by their neighbours, but the attractors of the system as a whole will not maximise total utility. We then consider agents that act as 'creatures of habit' by increasing their preference to coordinate (anti-coordinate) with whichever neighbours they are coordinated (anti-coordinated) with at the present moment. These preferences change slowly while the system is repeatedly perturbed such that it settles to many different local attractors. We find that under these conditions, with each perturbation there is a progressively higher chance of the system settling to a configuration with high total utility. Eventually, only one attractor remains, and that attractor is very likely to maximise (or almost maximise) global utility. This counterintutitve result can be understood using theory from computational neuroscience; we show that this simple form of habituation is equivalent to Hebbian learning, and the improved optimisation of global utility that is observed results from wellknown generalisation capabilities of associative memory acting at the network scale. This causes the system of selfish agents, each acting individually but habitually, to collectively identify configurations that maximise total utility. Selfish Agents and Total UtilityThis paper investigates the effect of a simple distributed strategy for increasing total utility in systems of selfishly optimising individuals. The broader topic concerns many different types of systems. For example, in technological systems, it is often convenient or necessary to devolve control to numerous autonomous components or agents that each, in a fairly simple manner, acts to optimise a global performance criterion: e.g. communications routing agents act to minimise calls dropped, or processing nodes in a grid computing system each act to maximise the number of jobs processed (1,2). However, since each component in the network acts individually, i.e., using only local information, constraints between individuals can remain unsatisfied, resulting in poorly optimised global performance. In an engineered system one could, in principle, mandate that all nodes act in accord with the globally optimal configuration of behaviours (assuming one knew what that was) -but this would defeat the scalability and robustness aims of complex adaptive systems. The question for engineered complex adaptive systems then, is the question of how to cause simple autonomous agents to act 'smarter' in a fully distributed manner such that they better satisfy constraints between agents an...

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“…More generally, the conditions where multi-agent systems can produce self-organised structure that enhances system-level or holistic function are not well understood. In this paper we wish to better understand the relationship between selforganisation in interaction networks amongst selfish agents and the potential for enhanced global adaptation [33,17,55,92,62,91,72]. …”

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confidence: 99%

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

2011

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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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Neurons, viscose fluids, freshwater polyp hydra-and self-organizing information systems

Cited by 58 publications

References 28 publications

Optimization in “self-modeling” complex adaptive systems

Optimization in “self-modeling” complex adaptive systems

“If You Can't Be With the One You Love, Love the One You're With”: How Individual Habituation of Agent Interactions Improves Global Utility

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

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