![](bga.png)
7 CONCLUSIONS
The recent financial crisis has stressed the need for
new research tools that can deal with the high level
of complexity of the economic world. Agent based
methods propose a powerful alternative to traditional
approaches developed in finance. Among others, Ar-
tificial Stock Markets offer a completely controlled
environment to test new regulations, new exchange
structures or new investment strategies.
We showed that to build a realistic artificial stock
market platform is a difficult task, but can be easily
realized using main MAS concepts: agents behaviour,
environment etc. We also discussed a series of soft-
ware engineering and architecture design issues aris-
ing when the ultimate goal is to develop a complete
API for market simulation. The purpose of the cur-
rent work is to provide a polymorphic platform: it
therefore can be used for a wide range of large scale
experiments, including or not artificial agents, sophis-
ticated behaviors, communication over the network...
The possibility to employ well known MAS ab-
stract models in the financial market modeling has
been considered. We have presented how these no-
tions have governed the development of the ATOM
(ArTifical Open Market) API.
REFERENCES
Arthur, B. (1994). Inductive reasoning and bounded ratio-
nality : the el-farol problem. American Economic Re-
view, 84:406–417.
Bouchaud, J.-P. and Potter, M. (2000). Theory of Financial
Risk. Cambridge University Press.
Brandouy, O. and Mathieu, P. (2007). A conceptual frame-
work for the evaluation of agent-base trading and tech-
nical analysis. Artificial Markets Modeling. Methods
and Applications. Lecture Notes in Economics and
Mathematical Systems, 599:63–79.
Brandouy, O., Mathieu, P., and Veryzhenko, I. (2009). Ex-
post optimal strategy for the trading of a single finan-
cial asset. SSRN eLibrary.
Cesa-Bianchi, N. and Lugosi, G. (2001). Worst-case bounds
for the logarithmic loss of predictors. Machine Learn-
ing, 43:247264.
Cont, R. (2001). Empirical properties of asset returns: styl-
ized facts and statistical issues. Quantitative Finance,
1:223–236.
Ferber, J. and Muller, J.-P. (1996). Influences and reaction:
a model of situated multiagent systems. Second Inter-
national Conference on Multiagent Systems ICMAS-
96, pages 72–79.
Gode, D. K. and Sunder, S. (1993). Allocative efficiency
of market with zero-intelligence traders: Market as a
partial substitute for individual rationality. Journal of
Political Economy, 101(1):119–137.
Gouaich, A., Michel, F., and Guiraud, Y. (2005). Mic :
A deployment environment for autonomous agents.
E4MAS 2004, LNAI 3374, Springer-Verlag, pages
109–126.
Inchiosa, M. E. and Parker, M. T. (2002). Overcoming
design and development challenges in agent-based
modeling using ascape. In Adaptive Agents, Intelli-
gence, and Emergent Human Organization: Captur-
ing Complexity through Agent-Based Modeling, num-
ber 3, pages 7304–7308, 317 Paseo de Peralta, Santa
Fe.
Jacobs, B. I., Levy, K. N., and Markowitz, H. M. (2004).
Financial market simulation. The Journal of Portfolio
Management, 30th Anniversary Issue:142–151.
Kirman, A. (1993). Ants, rationality, and recruitment.
Quarterly Journal of Economics, 108:137–156.
Le Baron, B. (2002). Building the santa fe artificial stock
market. Working Paper, Brandeis University.
Lux, T. and Marchsi, M. (1999). Scaling and criticality in
a stochastich multi-agent model of a financial market.
Nature, 397:498–500.
Markowitz, H. (1952). Portfolio selection. The Journal of
Finance, 7(1):77–91.
Michel, F. (2007). The irm4s model: The influence/reaction
principle for multi-agent based simulation. AA-
MAS’07. Sixth International Joint Conference on Au-
tonomous Agents and Multiagent Systems, pages 908–
910.
Mitchell, T. M., editor (1997). Machine Learning.
WCB/McGraw-Hill, New York, NY.
Raberto, M., Cincotti, S., Focardi, S., and Marchesi, M.
(2003). Traders’ long-run wealth in an artificial finan-
cial market. Computational Economics, 22:255–272.
Ricordel, P.-M. and Demazeau, Y. (2001). Volcano, a
vowels-oriented multi-agent platform. In CEEMAS,
pages 253–262.
Tkatch, I. and Alam, Z. S. (2009). Strategic order splitting
in automated markets. SSRN eLibrary.
Veryzhenko, I., Brandouy, O., and Mathieu, P. (2010).
Agent’s minimal intelligence calibration for realis-
tic market dynamics. Progress in Artificial Eco-
nomics Computational and Agent-Based Models. Lec-
ture Notes in Economics and Mathematical Systems
645, pages 3–14.
Witkam, J. (2003). Altreva adaptive modeler.
http://www.altreva.com.
Wooldridge, M. and Jennings, N. (1995). Intelligent agents:
Theory and practice. The Knowledge Engineering Re-
view, 10(2):115–152.
Woolridge, M. (2002). Introduction to Multiagent Systems.
Introduction to Multiagent Systems., New York, NY,
USA.
Zambonelli, F., Jennings, N. R., and Wooldridge., M.
(2003). Developing multiagent systems: The gaia
methodology. In ACM Transactions on Software En-
gineering Methodology, 12(3)(3):317–370.
KEY POINTS FOR REALISTIC AGENT-BASED FINANCIAL MARKET SIMULATIONS
83