Replicator Dynamic Learning in Muth's Model of Price Movements
We investigate the stability properties of Muth's model of price movements when agents choose a production level using replicator dynamic learning. It turns out that when there is a discrete set of possible production levels, possible stable states and stability conditions differ between adaptive learning and replicator dynamic learning. Furthermore, we show that the stability disparities between the two types of learning are due to the way asymptotic stability is defined under the replicator dynamics.
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