scholarly journals Fixation probabilities for the Moran process in evolutionary games with two strategies: graph shapes and large population asymptotics

2018 ◽  
Vol 78 (4) ◽  
pp. 1033-1065
Author(s):  
Evandro P. de Souza ◽  
Eliza M. Ferreira ◽  
Armando G. M. Neves
Author(s):  
Katia Sycara ◽  
Paul Scerri ◽  
Anton Chechetka

In this chapter, we explore the use of evolutionary game theory (EGT) (Weibull, 1995; Taylor & Jonker, 1978; Nowak & May, 1993) to model the dynamics of adaptive opponent strategies for large population of players. In particular, we explore effects of information propagation through social networks in Evolutionary Games. The key underlying phenomenon that the information diffusion aims to capture is that reasoning about the experiences of acquaintances can dramatically impact the dynamics of a society. We present experimental results from agent-based simulations that show the impact of diffusion through social networks on the player strategies of an evolutionary game and the sensitivity of the dynamics to features of the social network.


Author(s):  
T. Monk ◽  
P. Green ◽  
M. Paulin

Evolutionary graph theory is the study of birth–death processes that are constrained by population structure. A principal problem in evolutionary graph theory is to obtain the probability that some initial population of mutants will fixate on a graph, and to determine how that fixation probability depends on the structure of that graph. A fluctuating mutant population on a graph can be considered as a random walk. Martingales exploit symmetry in the steps of a random walk to yield exact analytical expressions for fixation probabilities. They do not require simplifying assumptions such as large population sizes or weak selection. In this paper, we show how martingales can be used to obtain fixation probabilities for symmetric evolutionary graphs. We obtain simpler expressions for the fixation probabilities of star graphs and complete bipartite graphs than have been previously reported and show that these graphs do not amplify selection for advantageous mutations under all conditions.


2021 ◽  
Author(s):  
Yipei Guo ◽  
Ariel Amir

Adaptation dynamics on fitness landscapes is often studied theoretically in the strong-selection, weak-mutation (SSWM) regime. However, in a large population, multiple beneficial mutants can emerge before any of them fixes in the population. Competition between mutants is known as clonal interference, and how it affects the form of long-term fitness trajectories in the presence of epistasis is an open question. Here, by considering how changes in fixation probabilities arising from weak clonal interference affect the dynamics of adaptation on fitness-parameterized landscapes, we find that the change in the form of fitness trajectory arises only through changes in the supply of beneficial mutations (or equivalently, the beneficial mutation rate). Furthermore, a depletion of beneficial mutations as a population climbs up the fitness landscape can speed up the functional form of the fitness trajectory, while an enhancement of the beneficial mutation rate does the opposite of slowing down the form of the dynamics. Our findings suggest that by carrying out evolution experiments in both regimes (with and without clonal interference), one could potentially distinguish the different sources of macroscopic epistasis (fitness effect of mutations vs. change in fraction of beneficial mutations).


Author(s):  
Katia Sycara ◽  
Paul Scerri ◽  
Anton Chechetka

The chapter explores the use of evolutionary game theory (EGT) to model the dynamics of adaptive opponent strategies for a large population of players. In particular, it explores effects of information propagation through social networks in evolutionary games. The key underlying phenomenon that the information diffusion aims to capture is that reasoning about the experiences of acquaintances can dramatically impact the dynamics of a society. The chapter presents experimental results from agent-based simulations that show the impact of diffusion through social networks on the player strategies of an evolutionary game and the sensitivity of the dynamics to features of the social network.


Author(s):  
Katia Sycara ◽  
Paul Scerri ◽  
Anton Chechetka

In this chapter, we explore the use of evolutionary game theory (EGT) (Nowak & May, 1993; Taylor & Jonker, 1978; Weibull, 1995) to model the dynamics of adaptive opponent strategies for a large population of players. In particular, we explore effects of information propagation through social networks in evolutionary games. The key underlying phenomenon that the information diffusion aims to capture is that reasoning about the experiences of acquaintances can dramatically impact the dynamics of a society. We present experimental results from agent-based simulations that show the impact of diffusion through social networks on the player strategies of an evolutionary game and the sensitivity of the dynamics to features of the social network.


2015 ◽  
Vol 364 ◽  
pp. 242-248 ◽  
Author(s):  
Xuesong Liu ◽  
Qiuhui Pan ◽  
Yibin Kang ◽  
Mingfeng He

Author(s):  
Katia Sycara ◽  
Paul Scerri ◽  
Anton Chechetka

In this chapter, we explore the use of evolutionary game theory (EGT) (Nowak & May, 1993; Taylor & Jonker, 1978; Weibull, 1995) to model the dynamics of adaptive opponent strategies for a large population of pl ion propagation through social networks in evolutionary games. The key underlying phenomenon that the information diffusion aims to capture is that reasoning about the experiences of acquaintances can dramatically impact the dynamics of a society. We present experimental results from agent-based simulations that show the impact of diffusion through social networks on the player strategies of an evolutionary game and the sensitivity of the dynamics to features of the social network.


Algorithmica ◽  
2012 ◽  
Vol 69 (1) ◽  
pp. 78-91 ◽  
Author(s):  
Josep Díaz ◽  
Leslie Ann Goldberg ◽  
George B. Mertzios ◽  
David Richerby ◽  
Maria Serna ◽  
...  

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