Lance Fortnow (Georgia Tech)
Abstract: Traditional microeconomic theory treats individuals and institutions as completely understanding the consequences of their decisions given the information they have available. These assumptions may not be valid as we might have to solve hard computational problems to optimize our choices. What happens if we restrict the computational power of economic agents?
There has been some work in economics treating computation as a fixed cost or simply considering the size of a program. This talk will explore a new direction bringing the rich tools of computational complexity into economic models, a tricky prospect where even basic concepts like "input size" are not well defined.
We show how to incorporate computational complexity into a number of economic models including game theory, prediction markets, forecast testing, preference revelation and awareness.
This talk will not assume any background in either economics or computational complexity.