{"title":"Tutorial 1: Sequential decision making: Theories and applications","authors":"Yan Chen, Chih-Yu Wang","doi":"10.1109/APSIPA.2017.8281988","DOIUrl":null,"url":null,"abstract":"Traditionally, the network and system management problem is formulated as an optimization problem with the assumption that all inputs are given at first and the decisions are made at a given time simultaneously. However, such an assumption is not realistic in many real world problems. Sequential decision making, a more general decision structure, exists commonly in our daily life, such as answer or vote on Q&A sites, tweets and comments on Twitter, access point association in wireless communications, channel access in cognitive radio networks, and so on. These examples share several characteristics: information asymmetry, network externality, and decision dependence. Such characteristics are the keys to understand how agents may behave under certain decision structure. Existing simultaneous decision making models cannot capture these key characteristics and therefore lead to inaccurate prediction or inefficient configuration, eventually degrade the system performance. In this tutorial, we present a series of game-theoretic frameworks to analyze and manage how rational users make sequential decisions with asymmetric information under different settings. We will provide in-depth theoretic analysis and share our experience in data-driven experimental results on various applications.","PeriodicalId":91399,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), ... Asia-Pacific. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference","volume":"85 1","pages":"ix-xii"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), ... Asia-Pacific. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSIPA.2017.8281988","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

Traditionally, the network and system management problem is formulated as an optimization problem with the assumption that all inputs are given at first and the decisions are made at a given time simultaneously. However, such an assumption is not realistic in many real world problems. Sequential decision making, a more general decision structure, exists commonly in our daily life, such as answer or vote on Q&A sites, tweets and comments on Twitter, access point association in wireless communications, channel access in cognitive radio networks, and so on. These examples share several characteristics: information asymmetry, network externality, and decision dependence. Such characteristics are the keys to understand how agents may behave under certain decision structure. Existing simultaneous decision making models cannot capture these key characteristics and therefore lead to inaccurate prediction or inefficient configuration, eventually degrade the system performance. In this tutorial, we present a series of game-theoretic frameworks to analyze and manage how rational users make sequential decisions with asymmetric information under different settings. We will provide in-depth theoretic analysis and share our experience in data-driven experimental results on various applications.
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第1课:顺序决策:理论与应用
传统上,网络和系统管理问题被表述为一个优化问题,假设所有的输入都是首先给出的,决策是在给定的时间同时做出的。然而,这种假设在许多现实世界的问题中是不现实的。顺序决策是一种更为普遍的决策结构,在我们的日常生活中普遍存在,如问答网站上的回答或投票、Twitter上的tweet和评论、无线通信中的接入点关联、认知无线网络中的信道访问等。这些例子有几个共同的特点:信息不对称、网络外部性和决策依赖性。这些特征是理解agent在特定决策结构下如何行为的关键。现有的同步决策模型不能捕获这些关键特征,因此导致不准确的预测或低效的配置,最终降低系统性能。在本教程中,我们提出了一系列博弈论框架来分析和管理理性用户如何在不同设置下使用不对称信息做出顺序决策。我们将提供深入的理论分析,并分享我们在各种应用中数据驱动实验结果的经验。
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