Optimal Consumption under Uncertainty, Liquidity Constraints, and Bounded Rationality

Abstract

I study how boundedly rational agents can learn the solution to an infinite horizon optimal consumption problem under uncertainty and liquidity constraints. I present conditions for the existence of an optimal linear consumption rule and characterize it. Additionally, I use an empirically plausible theory of learning to generate a class of adaptive learning algorithms that converges to the optimal rule. This provides an adaptive and boundedly rational foundation for neoclassical consumption theory.

Keywords: Adaptive learning models, bounded rationality, dynamic programming, consumption function, behavioral economics, liquidity constraint, Markov process

JEL Classification: C6, D8, D9, E21