Skip to main content
SpringerLink
Log in

Does Cognitive Capacity Matter When Learning Using Genetic Programming in Double Auction Markets?

  • Conference paper
Multi-Agent-Based Simulation X (MABS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5683))

Abstract

The relationship between human subjects’ cognitive capacity and their economic performances has been noticed in recent years due to the evidence found in a series of cognitive economic experiments. However, there are few agent-based models aiming to characterize such relationship. This paper attempts to bridge this gap and serve as an agent-based model with a focus on agents’ cognitive capacity. To capture the heterogeneity of human cognitive capacity, this paper employs genetic programming as the algorithm of the learning agents, and then uses population size as a proxy parameter of individual cognitive capacity. By modeling agents in this way, we demonstrate a nearly positive relationship between cognitive abilities and economic performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Simon, H.: The Sciences of the Artificial. MIT Press, Cambridge (1996)

    Google Scholar 

  2. Devetag, G., Warglien, M.: Games and phone numbers: Do short-term memory bounds affect strategic behavior? Journal of Economic Psychology 24, 189–202 (2003)

    Article  Google Scholar 

  3. Devetag, G., Warglien, M.: Playing the wrong game: An experimental analysis of relational complexity and strategic misrepresentation. Games and Economic Behavior 62, 364–382 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  4. Segal, N., Hershberger, S.: Cooperation and competition between twins: Findings from a prisoner’s dilemma game. Evolution and Human Behavior 20, 29–51 (1999)

    Article  Google Scholar 

  5. Casari, M., Ham, J., Kagel, J.: Selection bias, demographic effects, and ability effects in common value auction experiments. American Economic Review 97(4), 1278–1304 (2007)

    Article  Google Scholar 

  6. Jones, G.: Are smarter groups more cooperative? Evidence from prisoner’s dilemma experiments, 1959–2003. Journal of Economic Behavior and Organization (forthcoming)

    Google Scholar 

  7. Takashina, T., Watanabe, S.: The locality of information gathering in multiagent systems. In: Proceedings of Second International Conference on Multi-Agent Systems (ICMAS 1996), p. 461 (1996), http://homepage.mac.com/t_takashina/paper-dir/ICMAS96_takashina.pdf

  8. Savit, R., Manuca, R., Riolo, R.: Adaptive competition, market efficiency, and phase transitions. Physical Review Letters 82(10), 2203–2206 (1999)

    Article  Google Scholar 

  9. Casari, M.: Can genetic algorithms explain experimental anomalies? An application to common property resources. Computational Economics 24, 257–275 (2004)

    Article  MATH  Google Scholar 

  10. Rust, J., Miller, J., Palmer, R.: Behavior of trading automata in a computerized double auction market. In: Friedman, D., Rust, J. (eds.) Double Auction Markets: Theory, Institutions, and Laboratory Evidence. Addison Wesley, Redwood City (1993)

    Google Scholar 

  11. Rust, J., Miller, J.H., Palmer, R.: Characterizing effective trading strategies: Insights from a computerized double auction tournament. Journal of Economic Dynamics and Control 18, 61–96 (1994)

    Article  Google Scholar 

  12. Boender, Z.J., Ultee, J., Hovius, S.E.R.: Cognitive capacity: No association with recovery of sensibility by Semmes Weinstein test score after peripheral nerve injury of the forearm. Journal of Plastic, Reconstructive and Aesthetic Surgery (forthcoming)

    Google Scholar 

  13. Das, R., Hanson, J.E., Kephart, J.O., Tesauro, G.: Agent-human interactions in the continuous double auction. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI). Morgan-Kaufmann, San Francisco (2001)

    Google Scholar 

  14. Taniguchi, K., Nakajima, Y., Hashimoto, F.: A report of U-Mart experiments by human agents. In: Shiratori, R., Arai, K., Kato, F. (eds.) Gaming, Simulations, and Society: Research Scope and Perspective, pp. 49–57. Springer, Heidelberg (2004)

    Google Scholar 

  15. Grossklags, J., Schmidt, C.: Software agents and market (in)efficiency–A human trader experiment. IEEE Transactions on System, Man, and Cybernetics: Part C, Special Issue on Game-theoretic Analysis & Simulation of Negotiation Agents, IEEE SMC 36, 56–67 (2006)

    Google Scholar 

  16. Christal, R.E., Tirre, W., Kyllonen, P.: Two for the money: Speed and level scores from a computerized vocabulary test. In: Lee, G., Ulrich, T. (eds.) Proceedings, Psychology in the Department of Defense. Ninth Annual Symposium (USAFA TR 8-2). U.S. Air Force Academy, Colorado Springs (1984)

    Google Scholar 

  17. Detterman, D.K., Daniel, M.H.: Correlations of mental tests with each other and with cognitive variables are highest for low-IQ groups. Intelligence 13, 349–359 (1989)

    Article  Google Scholar 

  18. Hunt, E.: The role of intelligence in modern society. American Scientist, 356–368 (July/August 1995)

    Google Scholar 

  19. Conway, A., Kane, M., Engle, R.: Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences 7(12), 547–552 (2003)

    Article  Google Scholar 

  20. Gode, D., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy 101, 119–137 (1993)

    Article  Google Scholar 

  21. Cliff, D., Bruten, J.: Zero is not enough: On the lower limit of agent intelligence for continuous double auction markets. Tech. Rep. no. HPL-97-141, Hewlett-Packard Laboratories (1997), http://citeseer.ist.psu.edu/cliff97zero.html

  22. Zhan, W., Friedman, D.: Markups in double auction markets. Journal of Economic Dynamics and Control 31, 2984–3005 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  23. Gjerstad, S., Dickhaut, J.: Price formation in double auctions. Games and Economic Behavior 22, 1–29 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  24. Friedman, D.: A simple testable model of double auction markets. Journal of Economic Behavior and Organization 15, 47–70 (1991)

    Article  Google Scholar 

  25. Easley, D., Ledyard, J.: Theories of price formation and exchange in double oral auction. In: Friedman, D., Rust, J. (eds.) The Double Auction Market-Institutions, Theories, and Evidence. Addison-Wesley, Reading (1993)

    Google Scholar 

  26. Chan, N.T., LeBaron, B., Lo, A.W., Poggio, T.: Agent-based models of financial markets: A comparison with experimental markets. MIT Artificial Markets Project, Paper No. 124 (September 1999), http://citeseer.ist.psu.edu/chan99agentbased.html

  27. Miller, G.: The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review 63, 81–97 (1956)

    Article  Google Scholar 

  28. Arifovic, J.: Genetic algorithm and the cobweb model. Journal of Economic Dynamics and Control 18, 3–28 (1994)

    Article  MATH  Google Scholar 

  29. Gottfredson, L.S.: Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence 24(1), 13–23 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, Taiwan

    Shu-Heng Chen & Shu G. Wang

  2. Department of Economics, Tunghai University, Taiwan

    Chung-Ching Tai

Authors
  1. Shu-Heng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chung-Ching Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shu G. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Istituto di Scienze e Tecnologie della Cognizione - CNR, Via San Martino della Battaglia 44, 00185, Rome, Italy

    Gennaro Di Tosto

  2. NewVectors division of TechTeam Government Solutions, Inc., 3520 Greenn Court, Suite 250, 48105-1579, Ann Arbor, MI, USA

    H. Van Dyke Parunak

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, SH., Tai, CC., Wang, S.G. (2010). Does Cognitive Capacity Matter When Learning Using Genetic Programming in Double Auction Markets?. In: Di Tosto, G., Van Dyke Parunak, H. (eds) Multi-Agent-Based Simulation X. MABS 2009. Lecture Notes in Computer Science(), vol 5683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13553-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13553-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13552-1

  • Online ISBN: 978-3-642-13553-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation