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On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness

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  • Paul Brewer
  • Maria Huang
  • Brad Nelson
  • Charles Plott

Abstract

This research builds on the work of D.K. Gode and Shyam Sunder who demonstrated the existence of a strong relationship between market institutions and the ability of markets to seek equilibrium—even when the agents themselves have limited intelligence and behave with substantial randomness. The question posed is whether or not market institutions account for the operation of the law of supply and demand in markets populated by humans with no role required of human rationality. Are institutions responsible for the operations of the law of supply and demand or are behavioral principles also at work? Experiments with humans and simulations with robots both conducted in conditions in which major institutional and structural aids to convergence were removed, produced clear answers. Human markets converge, while robot markets do not. The structural and institutional features certainly facilitate convergence under conditions of substantial irrationality, but they are not necessary for convergence in markets in which agents have the rationality of humans. Copyright Kluwer Academic Publishers 2002

Suggested Citation

  • Paul Brewer & Maria Huang & Brad Nelson & Charles Plott, 2002. "On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness," Experimental Economics, Springer;Economic Science Association, vol. 5(3), pages 179-208, December.
  • Handle: RePEc:kap:expeco:v:5:y:2002:i:3:p:179-208
    DOI: 10.1023/A:1020871917917
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    2. Martin Barner & Francesco Feri & Charles R. Plott, 2005. "On the microstructure of price determination and information aggregation with sequential and asymmetric information arrival in an experimental asset market," Annals of Finance, Springer, vol. 1(1), pages 73-107, January.
    3. John Duffy & M. Utku Unver, 2003. "Asset Price Bubbles and Crashes with Near-Zero-Intelligence Traders: Towards an Understanding of Laboratory Findings," Computational Economics 0307001, University Library of Munich, Germany, revised 17 Mar 2004.
    4. Viktor Manahov & Mona Soufian & Robert Hudson, 2014. "The Implications Of Trader Cognitive Abilities On Stock Market Properties," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(1), pages 1-18, January.
    5. Ortmann, Andreas, 2003. "Charles R. Plott's collected papers on the experimental foundations of economic and political science," Journal of Economic Psychology, Elsevier, vol. 24(4), pages 555-575, August.
    6. Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading Without Resampling," Lecture Notes in Economics and Mathematical Systems, in: Klaus Schredelseker & Florian Hauser (ed.), Complexity and Artificial Markets, chapter 1, pages 3-14, Springer.
    7. Yeh, Chia-Hsuan, 2008. "The effects of intelligence on price discovery and market efficiency," Journal of Economic Behavior & Organization, Elsevier, vol. 68(3-4), pages 613-625, December.
    8. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    9. John A. List, 2004. "Testing Neoclassical Competitive Theory in Multilateral Decentralized Markets," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 1131-1156, October.
    10. Marco LiCalzi & Lucia Milone & Paolo Pellizzari, 2011. "Allocative Efficiency and Traders’ Protection Under Zero Intelligence Behavior," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 5-28, Springer.
    11. Brewer, Paul & Ratan, Anmol, 2019. "Profitability, efficiency, and inequality in double auction markets with snipers," Journal of Economic Behavior & Organization, Elsevier, vol. 164(C), pages 486-499.
    12. Ross M. Miller, 2012. "The Effect Of Boundary Conditions On Efficiency And Pricing In Double‐Auction Markets With Zero‐Intelligence Agents," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(3), pages 179-188, July.
    13. Marco LiCalzi & Paolo Pellizzari, 2006. "The Allocative Effectiveness of Market Protocols Under Intelligent Trading," Lecture Notes in Economics and Mathematical Systems, in: Charlotte Bruun (ed.), Advances in Artificial Economics, chapter 2, pages 17-29, Springer.
    14. Chia-Hsuan Yeh, 2007. "The role of intelligence in time series properties," Computational Economics, Springer;Society for Computational Economics, vol. 30(2), pages 95-123, September.
    15. John List, 2002. "Testing neoclassical competitive market theory in the field: Some pilot results," Framed Field Experiments 00172, The Field Experiments Website.
    16. Brewer, Paul J & Moberly, Raymond, 2021. "A comparison of zero and minimal intelligence agendas in Markov Chain voting models," OSF Preprints ajfdb, Center for Open Science.
    17. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).

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