More memory under evolutionary learning may lead to chaos

Cees Diks, Cars Hommes, Paolo Zeppini

Research output: Contribution to journalArticle

16 Citations (Scopus)
89 Downloads (Pure)

Abstract

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
Volume392
Issue number4
DOIs
Publication statusPublished - 15 Feb 2013

Fingerprint

Evolutionary Learning
learning
chaos
Chaos
Imitation
fitness
Convex Combination
Increasing Functions
Chaotic Behavior
Nonlinear Function
Linear Function
Fitness
Bifurcation
routes
Fluctuations
Decrease
Model
Innovation
Strategy

Cite this

More memory under evolutionary learning may lead to chaos. / Diks, Cees; Hommes, Cars; Zeppini, Paolo.

In: Physica A: Statistical Mechanics and its Applications, Vol. 392, No. 4, 15.02.2013, p. 808–812.

Research output: Contribution to journalArticle

@article{f299458e561c41ddbdcd38e6e873c439,
title = "More memory under evolutionary learning may lead to chaos",
abstract = "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.",
author = "Cees Diks and Cars Hommes and Paolo Zeppini",
year = "2013",
month = "2",
day = "15",
doi = "10.1016/j.physa.2012.10.045",
language = "English",
volume = "392",
pages = "808–812",
journal = "Physica A: Statistical Mechanics and its Applications",
issn = "0378-4371",
publisher = "Elsevier",
number = "4",

}

TY - JOUR

T1 - More memory under evolutionary learning may lead to chaos

AU - Diks, Cees

AU - Hommes, Cars

AU - Zeppini, Paolo

PY - 2013/2/15

Y1 - 2013/2/15

N2 - 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.

AB - 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.

UR - http://dx.doi.org/10.1016/j.physa.2012.10.045

U2 - 10.1016/j.physa.2012.10.045

DO - 10.1016/j.physa.2012.10.045

M3 - Article

VL - 392

SP - 808

EP - 812

JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

SN - 0378-4371

IS - 4

ER -