The article explores how to make smart investment decisions when we don't know the expected returns of risky assets. The researchers use a mix of Bayesian learning and dynamic programming to create equations that help with portfolio choices, liquidation, and transitions. They adapt existing models to handle situations where traditional methods don't work. The model they develop considers both asset liquidity and uncertainty about returns, offering a more comprehensive approach for investors.