Abstract
Agent-based Methodology (ABM) is becoming indispensable for the inter-disciplinary study of social and economic complex adaptive systems. The essence of ABM lies in the notion of autonomous agents whose behavior may evolve endogenously and can generate and mimic the corresponding complex system dynamics that the ABM is studying. Over the past decade, many computational intelligence (CI) methods have been applied to the design of autonomous agents, in particular, their adaptive scheme. This design issue is non-trivial since the chosen adaptive schemes usually have great impact on the generated system dynamics. Robert Lucas, one of the most influential modern economic theorists, has suggested using laboratories with human agents, also known as Experimental Economics, to help solving the design issue. While this is a promising approach, laboratories used in the current experimental economics is not computationally equipped to meet the demands of the task. This paper attempts to materialize Lucas’ suggestion by establishing a laboratory where human subjects are equipped with the computational power that satisfies the computational equivalence conditions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arifovic, J.: Genetic Algorithm Learning and the Cobweb Model. Journal of Economic Dynamics and Control 18(1), 3–28 (1994)
Arifovic, J.: Genetic Algorithms and Inflationary Economies. Journal of Monetary Economics 36(1), 219–243 (1995)
Arifovic, J., Maschek, M.: Expectations and Currency Crisis–An Experimental Approach. In: Paper presented at the 9th International Conference on Computing in Economics and Finance, University of Washington, Seattle, July 11-13 (2003)
Axelrod, R.: Advancing the Art of Simulation in the Social Sciences. In: Conte, R., Hegselmann, R., Terna, P. (eds.) Simulating Social Phenomena. Lecture Notes in Economic and Mathematical Systems, pp. 21–40 (1997)
Chen, S.-H., Yeh, C.-H.: Genetic Programming Learning and the Cobweb Model. In: Angeline, P. (ed.) Advances in Genetic Programming, vol. 2, ch. 22, pp. 443–466. MIT Press, Cambridge (1996)
Chen, S.-H., Liao, C.-C.: Agent-Based Computational Modeling of the Stock Price-Volume Relation (2004) (forthcoming in information sciences)
Chen, S.-H., Tai, C.-C.: Trading Restrictions, Price Dynamics and Allocative Efficiency in Double Auction Markets: Analysis Based on Agent-Based Modeling and Simulations (2003)
Chen, S.-H., Yeh, C.-H.: Evolving Traders and the Business School with Genetic Programming: A New Architecture of the Agent-Based Artificial Stock Market. Journal of Economic Dynamics and Control 25, 363–393 (2001)
Chen, S.-H., Yeh, C.-H.: On the Emergent Properties of Artificial Stock Markets. Journal of Economic Behavior and Organization 49, 217–129 (2002)
Cliff, D.: Minimal-Intelligence Agents for Bargaining Behaviors in Market- Based Environments, HP Technical Report, HPL-97-91 (1997)
Das, R., Hanson, J., Kephart, J., Tesauro, G.: Agent-Human Interactions in the Continuous Double Auction. In: Proceedings of the International Joint Conference on Artificial Intelligence (2001)
Dawid, H.: On the Convergence of Genetic Learning in a Double Auction Market. Journal of Economic Dynamics and Control 23, 1545–1567 (1999)
Feltovich, N.: Reinforcement-based vs. Belief-based Learning Models in Experimental Asymmetric-Information Games. Econometrica 68, 605–641 (2000)
Franke, R.: Coevolution and Stable Adjustments in the Cobweb Model. Journal of Evolutionary Economics 8(4), 383–406 (1998)
Gode, D.K., Sunders, S.: Allocative Efficiency of Market with Zero- Intelligence Trader: Market as a Partial Substitute for Individual Rationality. Journal of Political Economy 101(1), 119–137 (1993)
Grossklags, J., Schmidt, C.: Interaction of Human Traders and Artificial Agents on Double Auction Markets: Simulations and Laboratory Experiments. In: Chen, K., et al. (eds.) Proceedings of 7th Information Sciences, pp. 1269–1272 (2003)
He, M., Leung, H.-F., Jennings, N.: A Fuzzy Logic-Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions. IEEE Transactions on Knowledge and Data Engineering (2002)
Kirman, A.P., Vriend, N.: Evolving Market Structure: An ACE Model of Price Dispersion and Loyalty. Journal of Economic Dynamics and Control 25(3-4), 459–502 (2001)
Kurumatani, K., Yamamoto, T., Kawamura, H., Ohuchi, A.: Market Micro-Structure Analysis by Multi-Agent Simulation in an X-EconomyíX Comparison among Technical Indices. Information Sciences (2004) (forthcoming)
Lieberman, H.: Software Agents: The MIT Approach. In: Perram, J., Van de Velde, W. (eds.) MAAMAW 1996. LNCS, vol. 1038, Springer, Heidelberg (1996)
Lucas Jr., R.E.: Adaptive Behavior and Economic Theory. Journal of Business 59, 401–426 (1986)
Midgley, D., Marks, R., Cooper, L.: Breeding Competitive Strategies. Management Science 43 (3), 257–275 (1997)
Ockenfels, A., Roth, A.: The Timing of Bids in Internet Auctions: Market Design, Bidder Behavior, and Artificial Agents. Artificial Intelligence Magazine, 79–87 (Fall 2002)
Ringhut, E., Kooths, S.: Modeling Expectations with GENEFER - An Artificial Intelligence Approach. Computational Economics 21, 173–294 (2003)
Roth, A., Ockenfels, A.: Last Minute Bidding and the Rules of Ending Second Price Auctions: Evidence from eBay and Amazon Auctions on the Internet. American Economic Review 92(4), 1093–1103 (2002)
Rust, J., Miller, J., Palmer, R.: Behavior of Trading Automata in a Computerized Double Auction Market. In: Friedman, D., Rust, J. (eds.) The Double Auction Market: Institutions, Theories, and Evidence, vol. ch. 6, pp. 155–198. Addison Wesley, Reading (1993)
Rust, J., Miller, J., Palmer, R.: Characterizing Effective Trading Strategies: Insights from a Computerized Double Auction Market. Journal of Economic Dynamics and Control 18, 61–96 (1994)
Shachat, J., Swarthout, J.: Procurement Auctions for Differentiated Good. IBM Watson Research Center (2002) (working paper)
Smith, V.L.: Papers in Experimental Economics. Cambridge University Press, Cambridge (1991)
Smith, V.L., Suchanek, G.L.: Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets. Econometrica 56(6), 1119–1152 (1988)
Tay, N., Linn, S.: Fuzzy Inductive Reasoning, Expectation Formation and the Behavior of Security Prices. Journal of Economic Dynamics and Control 25, 321–361 (2001)
Varian, H.R.: Effect of the Internet on Financial Markets. School of Information Management and Systems, University of California, Berkeley (1988)
Wellman, M., Greenwald, A., Stone, P., Wurman, P.: The 2001 Trading Agent Experiment. In: Proceedings of Fourteenth Innovative Applications of Artificial Intelligence Conference, pp. 935–941 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, SH., Tai, CC. (2004). Toward a New Principle of Agent Engineering in Multiagent Systems: Computational Equivalence. In: Kurumatani, K., Chen, SH., Ohuchi, A. (eds) Multi-Agent for Mass User Support. MAMUS 2003. Lecture Notes in Computer Science(), vol 3012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24666-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-24666-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21940-8
Online ISBN: 978-3-540-24666-4
eBook Packages: Springer Book Archive