More memory under evolutionary learning may lead to chaos

Cees Diks, Cars Hommes, Paolo Zeppini

Research output: Contribution to journalArticlepeer-review

17 Citations (SciVal)
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We show that an increase of memory of past strategy performance in a simple agent-based innovation model, with agents switching between costly innovation and cheap imitation, can be quantitatively stabilising while at the same time qualitatively destabilising. As memory in the fitness measure increases, the amplitude of price fluctuations decreases, but at the same time a bifurcation route to chaos may arise. The core mechanism leading to the chaotic behaviour in this model with strategy switching is that the map obtained for the system with memory is a convex combination of an increasing linear function and a decreasing non-linear function.
Original languageEnglish
Pages (from-to)808–812
JournalPhysica A: Statistical Mechanics and its Applications
Issue number4
Publication statusPublished - 15 Feb 2013


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