The Santa Fe Bar Problem Revisited:
Theoretical and Practical Implications
Amy Greenwald, Bud Mishra, and Rohit Parikh
Abstract
This paper investigates the Santa Fe bar problem in detail from both a
theoretical and a practical perspective. Theoretically, it is shown
that traditional assumptions of economics, such as rationality, do not
give rise to desirable behavior in this problem. Specifically,
rationality and predictivity, two conditions sufficient for
convergence to Nash equilibrium, are inherently incompatible. On the
practical side, it is demonstrated via simulations that computational
learning algorithms in which agents are occasionally irrational do
yield near-equilibrium behavior.