Jean-Philippe Bouchaud - CFM & Académie des Sciences
Wednesday, November 29th, 10:45AM EST (Zoom meeting starts at 10:30 EDT)
RADICAL COMPLEXITY & BOUNDED RATIONALITY
Traditional economic theory assumes that agents are rational, or at least that they learn to be after interacting with their environment. As a schematic model of the complexity economic agents are confronted with, we introduce the ``SK-game'', a discrete time binary choice model inspired from mean-field spin-glasses. We show that even in a completely static environment, agents are unable to learn collectively optimal strategies. This is either because the learning process gets trapped by a sub-optimal fixed point, or because learning never converges and leads to a never ending evolution of agents intentions. Contrarily to the hope that learning might save the standard ``rational expectation'' framework in economics, we argue that complex situations are generically unlearnable and agents must do with {it satisficing} solutions, as argued long ago by Herbert Simon.