Abstract
In theory, the key step of traditional Harsanyi transformation is “Nature” assigns types to real players according to certain probability distributions. In big data era, it is still a challenge to obtain the probability distributions in practice, partly because the history type data of a new player is still private. Considering it is easy to access some feature data of a new player as well as to obtain both the feature and type data of massive other players, this paper introduces the statistical learning method eXtreme Gradient Boosting (XGBoost) to propose an XGBoost-driven Harsanyi transformation, where XGBoost is used to predict a new player’s type distribution indirectly. To test the effect of XGBoost-driven Harsanyi transformation, an incomplete information Internet loan credit game (3ILCG) is modeled and analyzed. When the loan interest rate r = 0.2, the empirical analysis is executed on 24,000 training data and 6,000 testing data. The experiment shows the accuracy (A) and harmonic mean (F1) of the enterprise loan decision based on pxgb on 6,000 testing data are 0.900833 and 0.945864 respectively. The test experiment demonstrates the XGBoost-driven Harsanyi transformation can help the lending platform to make loan decisions scientifically in practice and improve the practice value of game theory.
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Thanks to the support from National Natural Science Foundation of China (Project Number: 61671338).
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Gong, YC., Zhang, YN., Yu, L. (2019). XGBoost-Driven Harsanyi Transformation and Its Application in Incomplete Information Internet Loan Credit Game. In: Li, DF. (eds) Game Theory. EAGT 2019. Communications in Computer and Information Science, vol 1082. Springer, Singapore. https://doi.org/10.1007/978-981-15-0657-4_8
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DOI: https://doi.org/10.1007/978-981-15-0657-4_8
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