Abstract
We introduce human traders into an agent based financial market simulation prone to bubbles and crashes. We find that human traders earn lower profits overall than do the simulated agents (“robots”) but earn higher profits in the most crash-intensive periods. Inexperienced human traders tend to destabilize the smaller (10 trader) markets, but have little impact on bubbles and crashes in larger (30 trader) markets and when they are more experienced. Humans’ buying and selling choices respond to the payoff gradient in a manner similar to the robot algorithm. Similarly, following losses, humans’ choices shift towards faster selling.
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Acknowledgments
We are grateful to the National Science Foundation for support under grant SES-0436509, Pacific Rim Research Program for a mini grant and its seminar audiences at UCSC, the 2008 ESA conference at Caltech, 2008 Georgia State department seminar, and the 2009 CeNDEF workshop in Amsterdam. We thank Ralph Abraham and Paul Viotti for their invaluable assistance, and two anonymous referees for guidance on final revisions. We retain sole responsibility for remaining idiosyncrasies and errors.
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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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Feldman, T., Friedman, D. Human and Artificial Agents in a Crash-Prone Financial Market. Comput Econ 36, 201–229 (2010). https://doi.org/10.1007/s10614-010-9227-x
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DOI: https://doi.org/10.1007/s10614-010-9227-x