|Title||:||Economics and Computation|
|Speaker||:||Swaprava Nath (CMU, USA)|
|Details||:||Fri, 7 Oct, 2016 3:00 PM @ BSB 361|
|Abstract:||:||How should a group of friends decide which movie to watch together or which restaurant to go for dinner? How should a municipal corporation decide which set of public projects to undertake? How can a organizational committee allocate its funds for a set of projects that yields the maximum social welfare? Can we provide a performance guarantee for these decisions? The research in the intersection of economics and computation has interesting solutions for them.
Artificial intelligence (AI) deals with building systems or machines that take efficient decisions like the humans. In a setting where multiple such decision making human/automated agents interact, we need to design both a robust system that provides certain performance guarantees as well as help the agents to take efficient collective decisions. My research considers the multi-agent systems from two complementary viewpoints: (1) design protocols that are robust against any strategic manipulations, and (2) use AI to assist the human agents make provably efficient collective decisions. In both these settings, individual agents have some private information which needs to be revealed in order to take an efficient decision. My research in the first theme considers how we can design mechanisms that motivates individuals to reveal their private information truthfully and provide the limits of achievability of certain desirable properties. In the second theme, even if the individuals had the best intention of taking the efficient collective decision, their limitations of information revelation restricts the efficiency. My research in this theme provides recommendation of the format of information extraction and gives provable guarantees to efficient decision making. In this talk, I am going to provide examples and present my recent results in each of these two themes.