当前位置: X-MOL 学术J. Royal Soc. Interface › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Learning enables adaptation in cooperation for multi-player stochastic games
Journal of The Royal Society Interface ( IF 3.9 ) Pub Date : 2020-11-01 , DOI: 10.1098/rsif.2020.0639
Feng Huang 1, 2 , Ming Cao 2 , Long Wang 1
Affiliation  

Interactions among individuals in natural populations often occur in a dynamically changing environment. Understanding the role of environmental variation in population dynamics has long been a central topic in theoretical ecology and population biology. However, the key question of how individuals, in the middle of challenging social dilemmas (e.g. the ‘tragedy of the commons’), modulate their behaviours to adapt to the fluctuation of the environment has not yet been addressed satisfactorily. Using evolutionary game theory, we develop a framework of stochastic games that incorporates the adaptive mechanism of reinforcement learning to investigate whether cooperative behaviours can evolve in the ever-changing group interaction environment. When the action choices of players are just slightly influenced by past reinforcements, we construct an analytical condition to determine whether cooperation can be favoured over defection. Intuitively, this condition reveals why and how the environment can mediate cooperative dilemmas. Under our model architecture, we also compare this learning mechanism with two non-learning decision rules, and we find that learning significantly improves the propensity for cooperation in weak social dilemmas, and, in sharp contrast, hinders cooperation in strong social dilemmas. Our results suggest that in complex social–ecological dilemmas, learning enables the adaptation of individuals to varying environments.

中文翻译:

学习使协作适应多人随机游戏

自然种群中个体之间的相互作用经常发生在动态变化的环境中。长期以来,了解环境变化在种群动态中的作用一直是理论生态学和种群生物学的中心话题。然而,个人如何在具有挑战性的社会困境(例如“公地悲剧”)中调整自己的行为以适应环境波动的关键问题尚未得到令人满意的解决。使用进化博弈论,我们开发了一个随机博弈框架,该框架结合了强化学习的自适应机制,以研究合作行为是否可以在不断变化的群体互动环境中进化。当玩家的行动选择只是受到过去增援的轻微影响时,我们构建了一个分析条件来确定合作是否比叛逃更有利。直观地说,这种情况揭示了环境为何以及如何调解合作困境。在我们的模型架构下,我们还将这种学习机制与两个非学习决策规则进行了比较,我们发现学习显着提高了弱社会困境中的合作倾向,与此形成鲜明对比的是,在强社会困境中阻碍了合作。我们的研究结果表明,在复杂的社会生态困境中,学习使个人能够适应不同的环境。我们还将这种学习机制与两种非学习决策规则进行了比较,我们发现学习显着提高了弱社会困境中的合作倾向,与此形成鲜明对比的是,在强社会困境中阻碍了合作。我们的研究结果表明,在复杂的社会生态困境中,学习使个人能够适应不同的环境。我们还将这种学习机制与两种非学习决策规则进行了比较,我们发现学习显着提高了弱社会困境中的合作倾向,与此形成鲜明对比的是,在强社会困境中阻碍了合作。我们的研究结果表明,在复杂的社会生态困境中,学习使个人能够适应不同的环境。
更新日期:2020-11-01
down
wechat
bug