图 书 馆
学习理论在算法博弈论中的应用Applications of Learning Theory in Algorithmic Game Theory |
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| 课程网址: | http://videolectures.net/colt2015_roughgarden_game_theory/ |
| 主讲教师: | Tim Roughgarden |
| 开课单位: | 斯坦福大学计算机科学系 |
| 开课时间: | 2015-08-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Algorithmic game theory is a field that uses and extends tools from economics and game theory to reason about fundamental computer science problems. The field is important both for its applications, which span the gamut from network routing to online advertising, and for its remarkably diverse and rich connections to other areas of theoretical computer science, including complexity theory and approximation algorithms. In this talk, we survey two ways in which definitions and tools from learning theory have been crucial to recent advances in algorithmic game theory. First, we outline a theory of robust bounds on the "price of anarchy" --- meaning approximation guarantees for game-theoretic equilibria --- that apply to all outcome sequences generated by no-regret learners playing a multi-player game. Second, we explain how to use concepts from learning theory to make traditional (Bayesian) optimal auction theory operational, replacing the practically problematic "common prior" assumption with a data-driven approach. |
| 关 键 词: | 算法博弈; 计算科学; 学习理论 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-05-15：chenxin01 |
| 最后编审: | 2023-05-18：chenxin01 |
| 阅读次数: | 25 |