国际学生入学条件
You will be asked to enter typical application information such as nationality, GPA, schools attended, etc.
You will be required to upload an accurate unofficial transcript for each school you have attended. Please do not send official copies to the CAM office. Please note that there are only up to three fields for listing schools attended. If you attended more than three, you must upload those transcripts in the writing sample portion of the application.
If you have appropriate supplemental documents such as an undergraduate research paper, awards, etc., upload them in the writing sample portion of the application.
A statement of purpose is required and can be uploaded directly into the application.
At least three letters of recommendation are required. Additional letters are allowed. Recommenders may submit their letters online. Once an application is submitted, recommenders receive an automated email soliciting their letter (you will be prompted to provide their contact information before you submit your application).
Neither GRE General nor GRE subject scores are accepted.
TOEFL iBT - 77 (Writing 20; Listening 15; Reading 20; Speaking 22)
IELTS - 7.0 or higher
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IDP—雅思考试联合主办方

雅思考试总分
7.0
- 雅思总分:7
- 托福网考总分:77
- 托福笔试总分:160
- 其他语言考试:NA
CRICOS代码:
申请截止日期: 请与IDP联系 以获取详细信息。
课程简介
Mechanism design is the problem of designing a game so as to guarantee that players playing rationally will produce outcomes that are desirable from the point of view of the mechanism designer.<br><br>Auctions can be viewed as instances of mechanisms. We may be interested in designing auctions that, for example, maximize the revenue of a seller or encourage truthful behavior on the part of buyers. Computer scientists have become interested in algorithmic mechanism design, where the focus is on designing mechanisms with good properties that can be implemented in polynomial time. More recently, there has been work designing extremely simple mechanisms, that may not be optimal but perform well (in some appropriate sense) in equilibrium.<br><br>Combining learning theory with game theory: Online learning theory provides powerful models (such as the famous multi-armed bandit problem) for reasoning about processes that adapt their behavior based on past observations. New research questions emerge when one places online learning in a game-theoretic context. For example, what can be said about the dynamics of systems composed of multiple learners interacting in either a competitive or a cooperative environment If the learner's observations depend partly on data provided by selfish users, can we design learning algorithms whose behavior is aligned with the users' incentives, and what impact does this have on convergence rates How should one design algorithms for settings where the learner cannot take actions directly, but instead depends on myopic agents who can be encouraged via incentive payments to explore the space of alternatives, as when an online retailer attempts to learn the quality of products by eliciting consumer ratings<br><br>Adding computation and language issues to game theory: We consider models of game theory that explicitly charge for computation and take seriously the language used by agents. This turns out to have a major impact on solution concepts like Nash equilibrium. The approach seems to capture important intuitions, and can deal with a number of well-known problematic examples. Moreover, there are deep connections between this approach and cryptographic protocol security.
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