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The Psychological Force Model for Lowest Unique Bid Auction

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Abstract

We study a type of complex system arising from economics, the lowest unique bid auction (LUBA) system which is a new generation of online markets. Different from the traditional auction in which the winner is who bids the highest price, in LUBA, the winner is whoever places the lowest of all unique bids. In this paper, we propose a multi-agent model to factually describes the human psychologies of the decision-making process in LUBA. The model produces bid-price distributions that are in excellent agreement with those from the real data, including the whole inverted-J shape which is a general feature of the real bid price distribution, and the exponential decreasing shape in the higher price range. This implies that it is possible for us to capture the essential features of human psychologies in the competitive environment as exemplified by LUBA and that we may provide significant quantitative insights into complex socio-economic phenomena.

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Notes

  1. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122923.

  2. http://homes.soic.indiana.edu/filiradi/Resources.html.

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Acknowledgments

We thank Professors Pigolotti and Radicchi for sharing the data related to LUBA research, some of which we used. Thanks also to Yougui Wang for opinions and Cancan Zhou for polishing in English. This work was supported by the NSFC under Grant No. 61174165 and Fundamental Research Funds for the Central Universities.

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    Authors and Affiliations

    1. School of Systems Science, Beijing Normal University, Beijing, China

      Rui Hu, Qinghua Chen & Tao Zheng

    2. School of Economics and Management, Xinjiang University, Ürümqi, China

      Jinzhong Guo

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    1. Rui Hu
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    2. Jinzhong Guo
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    3. Qinghua Chen
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    4. Tao Zheng
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    Corresponding author

    Correspondence to Qinghua Chen.

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    Rui Hu and Jinzhong Guo have contributed equally to this work.

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    Hu, R., Guo, J., Chen, Q. et al. The Psychological Force Model for Lowest Unique Bid Auction. Comput Econ 50, 655–667 (2017). https://doi.org/10.1007/s10614-016-9614-z

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