Traders imprint themselves by adaptively updating their own avatar
<|reference_start|>Traders imprint themselves by adaptively updating their own avatar: Simulations of artificial stock markets were considered as early as 1964 and multi-agent ones were introduced as early as 1989. Starting the early 90's, collaborations of economists and physicists produced increasingly realistic simulation platforms. Currently, the market stylized facts are easily reproduced and one has now to address the realistic details of the Market Microstructure and of the Traders Behaviour. This calls for new methods and tools capable of bridging smoothly between simulations and experiments in economics.   We propose here the following Avatar-Based Method (ABM). The subjects implement and maintain their Avatars (programs encoding their personal decision making procedures) on NatLab, a market simulation platform. Once these procedures are fed in a computer edible format, they can be operationally used as such without the need for belabouring, interpreting or conceptualising them. Thus ABM short-circuits the usual behavioural economics experiments that search for the psychological mechanisms underlying the subjects behaviour. Finally, ABM maintains a level of objectivity close to the classical behaviourism while extending its scope to subjects' decision making mechanisms.   We report on experiments where Avatars designed and maintained by humans from different backgrounds (including real traders) compete in a continuous double-auction market. We hope this unbiased way of capturing the adaptive evolution of real subjects behaviour may lead to a new kind of behavioural economics experiments with a high degree of reliability, analysability and reproducibility.<|reference_end|>
@article{daniel2005traders,
    title={Traders imprint themselves by adaptively updating their own avatar},
    author={Gilles Daniel, Lev Muchnik, Sorin Solomon},
    journal={arXiv preprint arXiv:cs/0509017},
    year={2005},
    archivePrefix={arXiv},
    eprint={cs/0509017},
    primaryClass={cs.MA cs.CE}
}