کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5775964 | 1631759 | 2017 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Selection intensity and risk-dominant strategy: A two-strategy stochastic evolutionary game dynamics in finite population
ترجمه فارسی عنوان
شدت انتخاب و استراتژی غالب ریسک: دینامیک بازی دوجانبه تصادفی تکاملی در جمعیت محدود
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
دینامیک بازی تکاملی تصادفی، شدت انتخاب، تعادل نش، ریسک پذیری، آمادگی جسمانی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
چکیده انگلیسی
Stochastic evolutionary game dynamics with weak selection in finite population has been studied and it has been used to explain the emergence of cooperation. In this paper, following the previous studies, the diffusion approximation of a two-strategy stochastic evolutionary game dynamics in finite population that includes a small mutation rate between two strategies is investigated, where we assume that these two strategies are both strict Nash equilibrium (NE). Our main goal is to partially reveal the effect of selection intensity on the stochastic evolutionary game dynamics. Through the analysis of potential function of the stationary distribution, our main result shows that for all possible situations with that the selection intensity is not zero (that includes the strong selection), if a strategy is a risk-dominant NE, then its expected fitness with respect to the stationary distribution must be larger than that of other strategy. This result not only extends the previous results but also provides some useful insights for understanding the significance of selection intensity in stochastic evolutionary game dynamics in finite population.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 297, 15 March 2017, Pages 1-7
Journal: Applied Mathematics and Computation - Volume 297, 15 March 2017, Pages 1-7
نویسندگان
Yu Jie-Ru, Liu Xue-Lu, Zheng Xiu-Deng, Tao Yi,