摘要翻译：
研究了具有非对称信息的战略智能体的动态系统的贝叶斯学习问题。在文献中的一系列开创性论文中，这个问题已经在一个简化模型下进行了研究，在这个模型中，基于对系统状态的私人噪声观察和对过去玩家行为的公共观察，短视自私的玩家依次出现并在游戏中采取一次行动。研究表明，存在信息级联，用户丢弃自己的私有信息，模仿前人的行为。在这篇文章中，我们提供了一个框架来研究贝叶斯学习动力学在一个比上面描述的更一般的设置。特别是，我们的模型包含了一些情况，其中玩家是非近视的，策略性地参与游戏的整个持续时间，以及一些情况，其中一个内生过程选择哪个子集的玩家将在每个时间实例中行动。该框架基于一种序列分解方法，用于寻找具有非对称信息的一般动态博弈的结构化完美贝叶斯均衡(PBE)，其中用户特定状态演化为条件独立的马尔可夫过程，用户对其状态进行独立的噪声观测。利用这种方法，我们研究了一个特定的动态学习模型，其中参与者基于对每个人类型的估计来做出关于公共投资的决策。我们为这个问题描述了一组信息级联，其中学习对整个团队来说是停止的。我们证明了在这样的级联中，所有玩家对其他玩家类型的估计都冻结了，即使每个玩家都渐近地学习了自己的真实类型。
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英文标题：
《Decentralized Bayesian learning in dynamic games: A framework for
  studying informational cascades》
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作者：
Deepanshu Vasal and Achilleas Anastasopoulos
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
  We study the problem of Bayesian learning in a dynamical system involving strategic agents with asymmetric information. In a series of seminal papers in the literature, this problem has been investigated under a simplifying model where myopically selfish players appear sequentially and act once in the game, based on private noisy observations of the system state and public observation of past players' actions. It has been shown that there exist information cascades where users discard their private information and mimic the action of their predecessor. In this paper, we provide a framework for studying Bayesian learning dynamics in a more general setting than the one described above. In particular, our model incorporates cases where players are non-myopic and strategically participate for the whole duration of the game, and cases where an endogenous process selects which subset of players will act at each time instance. The proposed framework hinges on a sequential decomposition methodology for finding structured perfect Bayesian equilibria (PBE) of a general class of dynamic games with asymmetric information, where user-specific states evolve as conditionally independent Markov processes and users make independent noisy observations of their states. Using this methodology, we study a specific dynamic learning model where players make decisions about public investment based on their estimates of everyone's types. We characterize a set of informational cascades for this problem where learning stops for the team as a whole. We show that in such cascades, all players' estimates of other players' types freeze even though each individual player asymptotically learns its own true type.
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PDF链接：
https://arxiv.org/pdf/1607.06847