Modelling financial investment planning from agent perspectives

Multi-agent Systems (MASs) offer strong models for representing complex and dynamic real-world environments. Taking financial investment planning as an example, this paper describes how to model complex systems from agent perspectives. Different agents and their behaviours are identified for financial investment planning. These agents are put together as an agent-based system. The experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. The system is very flexible and robust. The success of the system indicates that (MASs) can significantly facilitate the modelling of complex systems.

[1]  John A. Campbell,et al.  Genetic-Fuzzy Systems for Financial Decision Making , 1994, IEEE/Nagoya-University World Wisepersons Workshop.

[2]  Carlos Angel Iglesias,et al.  MIX: A General Purpose Multiagent Architecture , 1995, ATAL.

[3]  Franco Zambonelli,et al.  Developing multiagent systems: The Gaia methodology , 2003, TSEM.

[4]  Joachim von Puttkamer Editors , 2018, Journal of Modern European History.

[5]  Lotfi A. Zadeh,et al.  Fuzzy Logic for Business, Finance, and Management , 1997, Advances in Fuzzy Systems - Applications and Theory.

[6]  Gerhard Weiss,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 1999 .

[7]  Michael R. Genesereth,et al.  Software agents , 1994, CACM.

[8]  A. Stuart,et al.  Portfolio Selection: Efficient Diversification of Investments. , 1960 .

[9]  Hans-Arno Jacobsen,et al.  A generic architecture for hybrid intelligent systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[10]  Andreas Pyka,et al.  Agent-Based Modelling: A Methodology for Neo-Schumpeterian Economics , 2005 .

[11]  Peijun Guo,et al.  Portfolio selection based on fuzzy probabilities and possibility distributions , 2000, Fuzzy Sets Syst..

[12]  Stephen T. Welstead,et al.  Neural network and fuzzy logic applications in C/C++ , 1994, Wiley professional computing.

[13]  NICHOLAS R. JENNINGS,et al.  An agent-based approach for building complex software systems , 2001, CACM.

[14]  Michael Wooldridge,et al.  Agent-based software engineering , 1997, IEE Proc. Softw. Eng..

[15]  Rajiv Khosla,et al.  Engineering Intelligent Hybrid Multi-Agent Systems , 1997, Springer US.

[16]  Rob Gordon,et al.  Essential Jni: Java Native Interface , 1998 .

[17]  Zili Zhang,et al.  Result fusion in multi-agent systems based on OWA operator , 2000, Proceedings 23rd Australasian Computer Science Conference. ACSC 2000 (Cat. No.PR00518).

[18]  Katia P. Sycara,et al.  Middle-Agents for the Internet , 1997, IJCAI.

[19]  Antonio F. Gómez-Skarmeta,et al.  A multiagent architecture for fuzzy modeling , 1999, Int. J. Intell. Syst..

[20]  Katia Sycara,et al.  Intelligent agents in portfolio management , 1998 .