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Collective Decision Making (Fall 2023)
IE 598(Special Topics)

Office Hours: Wednesday (2:30-3:30)

Lectures: Tuesdays and Thursdays (2-3:15)


This course would primarily focus on decision-making in the presence of multiple agents with preferences. This would cover topics on optimized democracy (voting rules, axioms, liquid democracy), embedded ethics (biases in algorithms, societal effects of algorithms, algorithmic fairness),  similar paradigms in ML (federated learning), and related topics from economics and computation (fair division, general equilibrium theory, and stable matching). 

The course is highly-interdisciplinary, and the main takeaway is to familiarize students with a class of problems that are of interest to operations research, computer science, economics, and law, and how the combined literature can help us design efficient solutions.

Timeline and Grading

The first 2/3rd of the course will involve lectures from the instructor, covering fundamentals of the topic. The last  1/3rd of the course would involve presentation from the students on the more recent progress (recent influential papers) on the covered topics. The list of papers would be online after the first week of the lectures, and the assignment should be done in the first 3-4 weeks. The course will also involve 3 homeworks that need to be submitted and graded. The final grade will be determined based on the homeworks and the presentation and summarization of papers.

Reading Materials

The course will not follow any specific textbook. Pointers to existing lecture notes from various sources will be posted to the course website as the semester progresses. 

Some Relevant Textbooks:

  • Felix Brandt, Vincent Conitzer, Ulle Endriss, Jerome Lang, Ariel Procaccia, Handbook of Computational Social Choice (Book available for free online)

  • Solon Barocas, Moritz Hardt, Arvind Narayanan, Fairness and Machine Learning, Limitations and Opportunities (Book available for free online)

  • N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani (editors), Algorithmic Game Theory 2007. (Book available for free online

Tentative Schedule 

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