Tata Institute of Fundamental Research
A Computational Approach towards Incentives in Social Choice
STCS Seminar
Speaker:
Rohit Vaish
Organiser:
Kavitha Telikepalli
Date:
Monday, 26 Jul 2021, 10:30 to 11:30
Venue:
(Scan to add to calendar)
Abstract:
Group decision-making is a ubiquitous phenomenon with diverse applications ranging from political elections to recommender systems and from organ exchanges to online marketplaces. Social choice is a subfield of economics that provides a formal framework for studying group decision-making procedures. Classically, social choice theory has focused on establishing abstract results concerning the existence of procedures that provide the desired incentives to the participating agents. However, in order to be practically applicable, the mere existence of such procedures is not enough---efficient computation is important as well. In this talk, I will illustrate the role of computation in shaping agents' incentives via a case study in fair division. Specifically, I will talk about fair division of indivisible goods, which is a relevant model for assigning seats in university courses, allocating public housing units, and inheritance division. I will present an algorithmic framework that combines the local search paradigm with the classical Fisher market model from economics, and simultaneously achieves the seemingly incompatible goals of fairness and economic efficiency. I will conclude with an overview of my other work and future research directions. Bio: Rohit Vaish is a visiting fellow at Tata Institute of Fundamental Research (TIFR). Previously, he was a postdoctoral researcher at Rensselaer Polytechnic Institute (RPI) and, prior to that, received his PhD from Indian Institute of Science (IISc). His research is in computational social choice---a rapidly growing area at the intersection of theoretical computer science, artificial intelligence, and economics. He has worked on problems in voting, matching, fair division, and learning theory, and his research has been published in top journals like Artificial Intelligence (AIJ) and premier theory and AI conferences such as EC, SODA, AAAI, IJCAI, and NeurIPS among others. In addition, he is a recipient of Prof. R Narasimhan postdoctoral award at TIFR, a best paper award nomination at AAMAS 2018, and the INSPIRE faculty fellowship.
| Speaker: | Rohit Vaish |
| Organiser: | Kavitha Telikepalli |
| Date: | Monday, 26 Jul 2021, 10:30 to 11:30 |
| Venue: |
(Scan to add to calendar)
Group decision-making is a ubiquitous phenomenon with diverse applications ranging from political elections to recommender systems and from organ exchanges to online marketplaces. Social choice is a subfield of economics that provides a formal framework for studying group decision-making procedures. Classically, social choice theory has focused on establishing abstract results concerning the existence of procedures that provide the desired incentives to the participating agents. However, in order to be practically applicable, the mere existence of such procedures is not enough---efficient computation is important as well. In this talk, I will illustrate the role of computation in shaping agents' incentives via a case study in fair division. Specifically, I will talk about fair division of indivisible goods, which is a relevant model for assigning seats in university courses, allocating public housing units, and inheritance division. I will present an algorithmic framework that combines the local search paradigm with the classical Fisher market model from economics, and simultaneously achieves the seemingly incompatible goals of fairness and economic efficiency. I will conclude with an overview of my other work and future research directions. Bio: Rohit Vaish is a visiting fellow at Tata Institute of Fundamental Research (TIFR). Previously, he was a postdoctoral researcher at Rensselaer Polytechnic Institute (RPI) and, prior to that, received his PhD from Indian Institute of Science (IISc). His research is in computational social choice---a rapidly growing area at the intersection of theoretical computer science, artificial intelligence, and economics. He has worked on problems in voting, matching, fair division, and learning theory, and his research has been published in top journals like Artificial Intelligence (AIJ) and premier theory and AI conferences such as EC, SODA, AAAI, IJCAI, and NeurIPS among others. In addition, he is a recipient of Prof. R Narasimhan postdoctoral award at TIFR, a best paper award nomination at AAMAS 2018, and the INSPIRE faculty fellowship.