A neural network modeled by an adaptive Lotka-Volterra system
SIAM Journal on Applied Mathematics
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Neurons, Viscose Fluids, Freshwater Polyp Hydra-and Self-Organizing Information Systems
IEEE Intelligent Systems
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology)
Modular Interdependency in Complex Dynamical Systems
Artificial Life
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Overcoming hierarchical difficulty by hill-climbing the building block structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A building-block royal road where crossover is provably essential
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Binary Optimization: On the Probability of a Local Minimum Detection in Random Search
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Adaptive Networks: Theory, Models and Applications
Adaptive Networks: Theory, Models and Applications
Evolution of cooperation in a population of selfish adaptive agents
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Individual selection for cooperative group formation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Can selfish symbioses effect higher-level selection?
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Symbiosis enables the evolution of rare complexes in structured environments
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
The web as an adaptive network: coevolution of web behavior and web structure
Proceedings of the 3rd International Web Science Conference
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In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize 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, generalization, and optimization 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 organizations 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 behaviors 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 maximize 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 behaviors 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 generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational principles familiar in connectionist models of organismic learning.