Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Neural network design and the complexity of learning
Neural network design and the complexity of learning
Approximation capabilities of multilayer feedforward networks
Neural Networks
Methods to speed up error back-propagation learning algorithm
ACM Computing Surveys (CSUR)
Stated choice methods: analysis and application
Stated choice methods: analysis and application
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Handbook of Computational Economics
Handbook of Computational Economics
Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics)
A neural network model of cortical activity during reaching
Journal of Cognitive Neuroscience
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This article models the learning process of a population of randomly rematched tabula rasa neural network agents playing randomly generated 3 脙聴 3 normal form games of all strategic types. Evidence was found of the endogenous emergence of a similarity measure of games based on the number and types of Nash equilibria, and of heuristics that have been found effective in describing human behavior in experimental one-shot games. The neural network agents were found to approximate experimental human behavior very well across various dimensions such as convergence to Nash equilibria, equilibrium selection, and adherence to principles of dominance and iterated dominance. This is corroborated by evidence from five studies of experimental one-shot games, because the Spearman correlation coefficients of the probability distribution over the neural networks' and human subjects' actions ranged from 0.49 to 0.89.