Course Identification
Algorithms, Games, and Economics
Lecturers and Teaching Assistants
Prof. Shahar Dobzinski
Course Schedule and Location
Wednesday, 10:15 - 12:00, Jacob Ziskind Building, Rm 155
06/11/2019
Field of Study, Course Type and Credit Points
Mathematics and Computer Science: Lecture; Elective; Regular; 2.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Prerequisites
Basic knowledge of algorithms, complexity, and probability. Familiarity with game theory and economics will not be assumed.
Attendance and participation
Estimated Weekly Independent Workload (in hours)
Syllabus
In this course we will study Algorithmic Game Theory -- a field in the intersection of computer science, game theory and economics. We will lean how to design algorithms that perform well even when in settings where the agents are selfish. Examples of such settings range from auctions and other resource allocation problems to recent markets for cryptocurrencies, such as Bitcoin. We will also learn how to apply the algorithmic point of view to analyze the performance of various economic markets. Toward the end of the course we will discuss recent research directions, e.g., using the algorithmic lenses to analyze problems from behavioral economics.
Learning Outcomes
Upon successful completion of this course students should be able to:
- Demonstrate familiarity with the basic notions of algorithmic game theory.
- Understand some current important research directions in the field.