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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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  • Miller, Ross M.

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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  • Miller, Ross M., 2008. "Don't let your robots grow up to be traders: Artificial intelligence, human intelligence, and asset-market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 68(1), pages 153-166, October.
    • Handle: RePEc:eee:jeborg:v:68:y:2008:i:1:p:153-166
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    1. HIGASHIDA Keisaku & TANAKA Kenta & MANAGI Shunsuke, 2018. "Losses on Asset Returns Caused by Perception Gaps of Fundamental Values: Evidence from laboratory experiments," Discussion papers 18008, Research Institute of Economy, Trade and Industry (RIETI).
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    4. 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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    More about this item

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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