Scale invariant pareto optimality: a meta--formalism for characterizing and modeling cooperativity in evolutionary systems

This article describes a mathematical framework for characterizing cooperativity in complex systems subject to evolutionary pressures. This framework uses three foundational components that constitute a meta-formalism that can be utilized in a host of research and development settings to improve the management, control, and understanding of large numbers of interacting systems such as in communication, computer, and sensor networks. A new concept, Scale Invariant Pareto Optimality, provides a mathematical basis for the efficient tradeoffs of efficiency on many scales and the measurement of cooperativity in complex systems. A mathematically oriented definition of self-organized behavior is also described. Discussion and conjectures are offered. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning—Parameter learning, Connectionism and neural nets and; I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence—Coherence and coordination Genera...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Mark Fleischer
• Keywords: Artificial Intelligence, Complex Systems, GECCO 2005, Invariant Pareto Optimality