Penalty Game with Mission Success Rates and Randomizing Mixed Nash Equilibrium Strategies-Based on Monte Carlo Simulation

Gong, Yuqing; Niu, Xiaomeng; Yuan, Jingjing; Zhao, Juan; Zhang, Yanna

doi:10.2991/msam-17.2017.14

Cited by 1 publication

(3 citation statements)

References 7 publications

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“…The more asymmetric the information is, the more uncertain factors the game has, and the more risks client will bear. As in [6] S.H. Ghodspour and C.O.…”

Section: Introductionmentioning

confidence: 94%

“…It cannot be analyzed due to its structural uncertainty for a long time. In 1967-1968, Harsanyi propose a transformation to convert an incomplete information game into a complete information dynamic game as in [6]. In which "Nature" is a virtual game player without payoff and objective function.…”

Section: Harsanyi Transformation Of Incomplete Information Games and mentioning

confidence: 99%

“…To simulate, We use the method in [6]. To digitalize stratedgy, for logistics demand enterprises, 1 means supervision, and 0 indulgence.…”

Section: Simulations the Simulation Of Logistics Supervision Game In mentioning

confidence: 99%

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Incomplete Information Game and Simulation of Logistics Supervision

Gong¹,

Yanna²,

Zhao³

et al. 2017

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Abstract. This article aims to improve incomplete information game of logistics supervision and its practice feasibility. For the uncertainty of logistics enterprises' type, firstly we established an incomplete information game of logistics supervision. Second we carried on Harsanyi transformation to convert it into a complete but imperfect information game. Meanwhile we use Trigonometric distribution to solve uncertainty of logistics enterprises' type. By investigation, the most likely value is 0.77 in Trigonometric distribution. Lastly, Monte Carlo (MC) simulation is adopted to simulate players' behavior strategy and expected benefit. The expected benefits of logistics demand enterprises and 3PLs are 33.5667 and 16.5375 respectively. The simulation results show that the randomizing coincides with the probability distribution of the Mix Nash equilibrium above 98.7%.

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“…The more asymmetric the information is, the more uncertain factors the game has, and the more risks client will bear. As in [6] S.H. Ghodspour and C.O.…”

Section: Introductionmentioning

confidence: 94%

Section: Harsanyi Transformation Of Incomplete Information Games and mentioning

confidence: 99%