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Don't Let Your Robots Grow Up To Be Traders: Artificial Intelligence, Human Intelligence, and Asset-Market Bubbles

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Author Info

  • Ross M. Miller

    (Miller Risk Advisors)

Abstract

Researchers who have examined markets populated by "robot traders" have claimed that the high level of allocative efficiency observed in experimental markets is driven largely by the "intelligence" implicit in the rules of the market. Furthermore, they view the ability of agents (artificial or human) to process information and make rational decisions as unnecessary for the efficient operation of markets. This paper presents a new series of market experiments that show that markets populated with standard robot traders are no longer efficient if time is a meaningful element, as it is in all asset markets. While simple two- season markets with human subjects reliably converge to an efficient equilibrium, markets with minimally intelligent robot traders fail to attain this equilibrium. Instead, these markets overshoot the equilibrium and then crash below it. In addition to firmly establishing the role of trader intelligence in asset-market equilibrium, these experiments also provide insights into why bubbles and crashes are consistently observed in many asset-market laboratory experiments using human subjects.

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File URL: http://128.118.178.162/eps/exp/papers/0306/0306001.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Experimental with number 0306001.

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Length: 18 pages
Date of creation: 09 Jun 2003
Date of revision:
Handle: RePEc:wpa:wuwpex:0306001

Note: Type of Document - PDF/Acrobat; prepared on Windows 98SE; to print on PDF compatible; pages: 18 ; figures: Included
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Web page: http://128.118.178.162

Related research

Keywords: market bubbles; intertemporal competitive equilibrium; experimental markets; trading agents;

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References

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Cited by:
  1. Da Costa, Newton & Goulart, Marco & Cupertino, Cesar & Macedo, Jurandir & Da Silva, Sergio, 2013. "The disposition effect and investor experience," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1669-1675.

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