计算、经济学和数据科学课程详细信息

课程号 04833540 学分 2
英文名称 Computation, Economics and Data Science
先修课程 概率论,算法设计,离散数学。无经济学知识要求。
中文简介 本课程将会介绍算法、博弈论、经济学和学习理论之间的相互影响。课程主要关注以下三个主题:(1)博弈论的基础及其和对偶理论、在线学习的关系。(2)拍卖和机制设计的基础和根基,以及如何从PAC学习理论的角度出发去使用数据来帮助拍卖和机制的设计。(3)概率密度估计的基础及在经济学中的应用。
其中,主题(1)涵盖了战略行为、平衡、对偶理论、在线学习和无秩序价值的基础。主题(2)介绍机制设计、税收优化和PAC学习,同时会使用这些工具来学习机制设计的简单性、可学习性和近似折衷。主题(3)介绍经济学和统计学的基础,并应用于游戏和拍卖中的推断。本课程的大多数样例都会基于在线广告和在线市场设计的应用。
英文简介 The course will present topics at the intersection of Algorithms, Game Theory, Economics, and Learning. We will mainly focus on the following three topics: (i) Fundamentals of Game Theory and their connection to duality theory and online learning; (ii) The basics and foundations of auctions and mechanism design, and also how to design good auctions and mechanisms using data through the lens of provably-approximately-correct (PAC) learning; and (iii) The basics of density estimation and its applications in Econometrics.

Topic (i) will cover the basics of strategic behavior, equilibria, duality theory, online learning, and the price of anarchy. Topic (ii) will present the basics of mechanism design, revenue optimization, and PAC learning, and apply these tools to study the simplicity, learnability, and approximation tradeoffs in mechanism design. Topic (iii) will present the basics of Econometrics and Statistics, with applications to inference in games and auctions. Most examples of this course will be based on applications in online advertising and online market design.
开课院系 信息科学技术学院
通选课领域  
是否属于艺术与美育
平台课性质  
平台课类型  
授课语言 中文
教材
参考书
教学大纲
PART I: Fundamentals of Game Theory, Duality Theory, and Online Learning (16)

Introduction to Game Theory
We will cover the basics of strategic behavior, equilibria, and algorithm design in strategic settings.
Duality Theory
Minimax theorem and two-player zero-sum games
Online Learning
Online learning, online convex optimization, minimax theorem from no-regret learning
Nash’s Theorem, Complexity of Nash Equilibria
Online Learning and General Games
Correlated equilibrium via no-regret learning, price of anarchy

PART II: Mechanism Design (10)

Introduction to Mechanism Design
Truthfulness, revelation principle, Myerson’s lemma, Vickrey auction, Myerson’s optimal auction, revenue optimization
Simple vs Optimal Auctions
Single-sample mechanism, prophet inequality, and state of the art results on approximately optimal multi-item auctions

PART III: Machine Learning (22)

Intro to Statistical Learning Theory
Rademacher complexity, Growth number
Learning in Mechanism Design
Learning optimal auctions from samples
Ecnometrics and Statistics
Intro to estimation of distributions, estimation in games, learning value CDFs from auction bids
Revenue inference and A/B testing in auctions
文献阅读,课堂讨论和书面作业。
考勤和课堂讨论:30%,书面作业:70%。
教学评估 王亦洲:
学年度学期:17-18-3,课程班:计算、经济学和数据科学1,课程推荐得分:4.38,教师推荐得分:4.69,课程得分分数段:null;