Evolutionary game dynamics of combining the payoff-driven and conformity-driven update rules

2020 ◽  
Vol 140 ◽  
pp. 110146
Author(s):  
Jingyan Lin ◽  
Changwei Huang ◽  
Qionglin Dai ◽  
Junzhong Yang
Keyword(s):  
Evolutionary Game ◽  
Game Dynamics ◽  
Update Rules

Evolutionary game dynamics of combining the imitation and aspiration-driven update rules

2019 ◽  
Vol 100 (2) ◽  
Author(s):  
Xianjia Wang ◽  
Cuiling Gu ◽  
Jinhua Zhao ◽  
Ji Quan
Keyword(s):  
Evolutionary Game ◽  
Game Dynamics ◽  
Update Rules

scholarly journals Evolutionary games on cycles

2006 ◽  
Vol 273 (1598) ◽  
pp. 2249-2256 ◽  
Author(s):  
Hisashi Ohtsuki ◽  
Martin A Nowak
Keyword(s):  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Evolutionary Games ◽  
Weak Selection ◽  
Stochastic Effects ◽  
Frequency Dependent Selection ◽  
Game Dynamics ◽  
Case Selection ◽  
Mixed Populations ◽  
Update Rules

Traditional evolutionary game theory explores frequency-dependent selection in well-mixed populations without spatial or stochastic effects. But recently there has been much interest in studying the evolutionary game dynamics in spatial settings, on lattices and other graphs. Here, we present an analytic approach for the stochastic evolutionary game dynamics on the simplest possible graph, the cycle. For three different update rules, called ‘birth–death’ (BD), ‘death–birth’ (DB) and ‘imitation’ (IM), we derive exact conditions for natural selection to favour one strategy over another. As specific examples, we consider a coordination game and Prisoner's Dilemma. In the latter case, selection can favour cooperators over defectors for DB and IM updating. We also study the case where the replacement graph of evolutionary updating remains a cycle, but the interaction graph for playing the game is a complete graph. In this setting, all three update rules lead to identical conditions in the limit of weak selection, where we find the ‘1/3-law’ of well-mixed populations.

Lumping evolutionary game dynamics on networks

2016 ◽  
Vol 407 ◽  
pp. 328-338 ◽  
Author(s):  
G. Iacobelli ◽  
D. Madeo ◽  
C. Mocenni
Keyword(s):  
Evolutionary Game ◽  
Game Dynamics

scholarly journals Evolutionary game dynamics

2003 ◽  
Vol 40 (04) ◽  
pp. 479-520 ◽  
Author(s):  
Josef Hofbauer ◽  
Karl Sigmund
Keyword(s):  
Evolutionary Game ◽  
Game Dynamics

WITHDRAWN: Evolutionary Game Dynamics and Cancer

2019 ◽  
Author(s):  
Jorge M. Pacheco ◽  
Simon A. Levin ◽  
David Dingli
Keyword(s):  
Evolutionary Game ◽  
Game Dynamics

scholarly journals Steering eco-evolutionary game dynamics with manifold control

2020 ◽  
Vol 476 (2233) ◽  
pp. 20190643 ◽  
Author(s):  
Xin Wang ◽  
Zhiming Zheng ◽  
Feng Fu
Keyword(s):  
Evolutionary Dynamics ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Feedback Loops ◽  
Mathematical Framework ◽  
Stable Manifolds ◽  
Selection Gradient ◽  
Game Dynamics ◽  
Control Approach ◽  
Attraction Basin

Feedback loops between population dynamics of individuals and their ecological environment are ubiquitously found in nature and have shown profound effects on the resulting eco-evolutionary dynamics. By incorporating linear environmental feedback law into the replicator dynamics of two-player games, recent theoretical studies have shed light on understanding the oscillating dynamics of the social dilemma. However, the detailed effects of more general nonlinear feedback loops in multi-player games, which are more common especially in microbial systems, remain unclear. Here, we focus on ecological public goods games with environmental feedbacks driven by a nonlinear selection gradient. Unlike previous models, multiple segments of stable and unstable equilibrium manifolds can emerge from the population dynamical systems. We find that a larger relative asymmetrical feedback speed for group interactions centred on cooperators not only accelerates the convergence of stable manifolds but also increases the attraction basin of these stable manifolds. Furthermore, our work offers an innovative manifold control approach: by designing appropriate switching control laws, we are able to steer the eco-evolutionary dynamics to any desired population state. Our mathematical framework is an important generalization and complement to coevolutionary game dynamics, and also fills the theoretical gap in guiding the widespread problem of population state control in microbial experiments.

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