Reinforcement Learning Rules in a Repeated Game
Computational Economics
Embodiment of Evolutionary Computation in General Agents
Evolutionary Computation
Metabolic Flux Estimation-A Self-Adaptive Evolutionary Algorithm with Singular Value Decomposition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Expert Systems with Applications: An International Journal
Modeling Autonomous Adaptive Agents with Functional Language for Simulations
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Incremental or radical? A study of organizational innovation: An artificial world approach
Expert Systems with Applications: An International Journal
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Complex adaptive systems have historically been studied using simplifications that mandate deterministic interactions between agents or instead treat their interactions only with regard to their statistical expectation. This has led to an anticipation, even in the case of agents employing inductive reasoning in light of limited information, that such systems may have equilibria that can be predicted a priori. This hypothesis is tested here using a simulation of a simple market economy in which each agent's behavior is based on the result of an iterative evolutionary process of variation and selection applied to competing internal models of its environment. The results indicate no tendency for convergence to stability or a long-term equilibrium and highlight fundamental differences between deterministic and stochastic models of complex adaptive systems