Signaling
Signaling
复制标题
DOI：
10.1002/0471208051.fre002
复制
发表时间：
2018-12
期刊：
影响因子：
--
通讯作者：
Dominique Bergmann;Jr Leonard W. Ely;Biology • Member Bio-X • Member Professor-Biology-•-Member-Bio-X-•-Member-Professor-2291549713;Stanford Cancer Institute;Stanford Advisees;Zhainib Amir;Willian Goudinho Viana;Dania Nanes Sarfati;Rachel Ng;Omar Niagne;Anay Ram;Reddy;G. Amador;Siobhán L. Bridson;Joel Erberich;Hannah Fung;Dirk Spencer;Rachel Varnau;M. Vollbrecht;E. Saldivar
中科院分区：
文献类型：
--
作者：
Dominique Bergmann;Jr Leonard W. Ely;Biology • Member Bio-X • Member Professor-Biology-•-Member-Bio-X-•-Member-Professor-2291549713;Stanford Cancer Institute;Stanford Advisees;Zhainib Amir;Willian Goudinho Viana;Dania Nanes Sarfati;Rachel Ng;Omar Niagne;Anay Ram;Reddy;G. Amador;Siobhán L. Bridson;Joel Erberich;Hannah Fung;Dirk Spencer;Rachel Varnau;M. Vollbrecht;E. Saldivar
信息传递是生命的基本特征，包括生物体内部和生物体之间的信号传递。由于其互动的性质，信号可以用博弈论来研究。信号的博弈论模型在生物学、经济学和哲学中有着悠久的传统。长期以来，对这些博弈的分析主要依赖于静态均衡的概念，如帕累托最优纳什均衡或进化稳定策略。最近，各种类型的信号游戏已经被调查的帮助下，游戏动力学，其中包括动态模型的进化和个人学习。动态分析导致信号相互作用的结果更微妙的结论。在这里，我们探讨了不同类型的信号博弈，从没有利益冲突的博弈者之间的互动，到他们的利益严重失调的互动。我们认为这些游戏的背景下，进化动力学（无限和有限的人口模型）和学习动力学（强化学习）。一些结果是特定动力学模型的特定特征，而另一些结果在不同的模型中是相当稳健的。这表明，有一些定性方面是许多现实世界的信号相互作用所共有的。
Information transfer is a basic feature of life that includes signal- ing within and between organisms. Owing to its interactive nature, signaling can be investigated by using game theory. Game theoretic models of signaling have a long tradition in biology, economics, and philosophy. For a long time the analyses of these games has mostly relied on using static equilibrium concepts such as Pareto optimal Nash equilibria or evolutionarily stable strate- gies. More recently signaling games of various types have been investigated with the help of game dynamics, which includes dynamical models of evolution and individual learning. A dynamical analysis leads to more nuanced conclusions as to the outcomes of signaling interactions. Here we explore different kinds of signaling games that range from interactions without conflicts of interest between the players to interactions where their interests are seriously misaligned. We consider these games within the context of evolutionary dynamics (both infinite and finite population models) and learning dynamics (reinforcement learning). Some results are specific features of a particular dynamical model, whereas others turn out to be quite robust across different models. This suggests that there are certain qualitative aspects that are common to many real-world signaling interactions.