Abstract
This paper presents a neural-evolutionary framework for the simulation of market models in a bounded rationality scenario. Each agent involved in the scenario make use of a population of neural networks in order to make a decision, while inductive learning is performed by means of an evolutionary algorithm. We show that good convergence to the game-theoretic equilibrium is reached within certain parameters.
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Matsumura de Araújo, R., Lamb, L.C. (2004). Neural-Evolutionary Learning in a Bounded Rationality Scenario. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_154
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DOI: https://doi.org/10.1007/978-3-540-30499-9_154
Publisher Name: Springer, Berlin, Heidelberg
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