Abstract
For predicting the decision in prisoner’s dilemma game, researchers have developed lots of models. However, the existing models only consider the networks based on quantum probability amplitude and the accuracy can still be improved. Thus, in this paper, quantum-like evidential network (QLEN) is proposed, which is the evidential extension of the original quantum-like Bayesian network. A QLEN consists of a directed acyclic graph associated with quantum mass function. In addition, a full joint quantum mass function can be derived from QLEN which can be applied in decision making and inference. Moreover, based on QLEN, this paper presents a decision model for predicting the players’ decision in prisoner’s dilemma. The results show that, compared with the existing models, the proposed model is more efficient and accurate to make predictions in prisoner’s dilemma.
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Deng, J., Deng, Y. (2021). QLEN: Quantum-Like Evidential Networks for Predicting the Decision in Prisoner’s Dilemma. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds) Belief Functions: Theory and Applications. BELIEF 2021. Lecture Notes in Computer Science(), vol 12915. Springer, Cham. https://doi.org/10.1007/978-3-030-88601-1_30
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DOI: https://doi.org/10.1007/978-3-030-88601-1_30
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