System dynamics of behaviour-evolutionary mix-game models

doi:10.1088/1674-1056/19/11/110514

Abstract In real financial markets there are two kinds of traders: one is fundamentalist, and the other is a trend-follower. The mix-game model is proposed to mimic such phenomena. In a mix-game model there are two groups of agents: Group 1 plays the majority game and Group 2 plays the minority game. In this paper, we investigate such a case that some traders in real financial markets could change their investment behaviours by assigning the evolutionary abilities to agents: if the winning rates of agents are smaller than a threshold, they will join the other group; and agents will repeat such an evolution at certain time intervals. Through the simulations, we obtain the following findings: (i) the volatilities of systems increase with the increase of the number of agents in Group 1 and the times of behavioural changes of all agents; (ii) the performances of agents in both groups and the stabilities of systems become better if all agents take more time to observe their new investment behaviours; (iii) there are two-phase zones of market and non-market and two-phase zones of evolution and non-evolution; (iv) parameter configurations located within the cross areas between the zones of markets and the zones of evolution are suited for simulating the financial markets.

Keywords: minority game model mix-game model behavioural evolution system dynamics

Received: 16 January 2010
Revised: 03 May 2010
Accepted manuscript online:

PACS:	02.50.Le	(Decision theory and game theory)
	02.60.Pn	(Numerical optimization)
	89.65.Gh	(Economics; econophysics, financial markets, business and management)

Fund: Project supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China.

Cite this article:

Gou Cheng-Ling(苟成玲), Gao Jie-Ping(高洁萍), and Chen Fang(陈芳) System dynamics of behaviour-evolutionary mix-game models 2010 Chin. Phys. B 19 110514

[1]	Arthur W B 1999 Science 284 107
[2]	Challet D and Zhang Y C 1997 Phyisca A 246 407
[3]	Johnson N F, Jefferies P and Hui P M 2003 Financial Market Complexity (Oxford: Oxford University Press)
[4]	Jefferies P and Johnson N F 2001 Oxford Center for Computational Finance, cond-mat/0207523v1
[5]	Gou C L, Guo X Q and Chen F 2008 http://dx.doi.org/10.1016/j.physa.2008.07.023
[6]	Andersen J V and Sornette D 2003 Eur. Phys. J. B 31 141
[7]	Shleifer A 2000 Inefficient Markets: An Introduction to Behavioral Financial (Oxford: Oxford University Press)
[8]	Lux T 1995 Economic Journal 105 881
[9]	Lux T and Marchesi M 1999 Nature 397 498
[10]	Challet D 2005 arXiv: physics/0502140v1
[11]	Slanina F and Zhang Y C 2001 Physica A 289 290
[12]	Yoon S M and Kim K 2005 arXiv: physics/0503016v1
[13]	Giardina I and Bouchaud J P 2003 Eur. Phys. J. B 31 421
[14]	Marsili M 2001 Physica A 299 93
[15]	Martino A D, Giardina I and Mosetti G 2003 J. Phys. A 36 8935
[16]	Coolen A C C 2005 The Mathematical Theory of Minority Games (Oxford: Oxford University Press)
[17]	Tedeschi A, Martino A D and Giardina I 2005 arXiv: cond-mat/0503762
[18]	Martino A D, Giardina I, Marsili M and Tedeschi A 2004 arXiv: cond-mat/04103649
[19]	Gou C L 2006 Chin. Phys. 15 1239
[20]	Gou C L 2006 JASSS 9 http://jasss.soc.surrey.ac.uk/9/3/6.html
[21]	Gou C L 2007 Physica A 378 459
[22]	Gou C L 2006 Physica A 371 633
[23]	Gou C L 2005 Proceeding of IEEE ICNN&B'05 1651
[24]	Chen F, Gou C L, Guo X Q and Gao J P 2008 Physica A 387 3594 endfootnotesize

[1]	Non-Markovian speedup dynamics control of the damped Jaynes-Cummings model with detuning Kai Xu(徐凯), Wei Han(韩伟), Ying-Jie Zhang(张英杰), Heng Fan(范桁). Chin. Phys. B, 2018, 27(1): 010302.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

System dynamics of behaviour-evolutionary mix-game models

Cite this article:

Online attention