Introduces a human-machine interaction framework, in which a robo-advisor interacts with a client to solve portfolio allocation problems. The risk-return tradeoff adapts to the client’s risk profile, which depends on idiosyncratic characteristics, market returns, and economic conditions. This paper demonstrates that the optimal portfolio personalization depends on a tradeoff faced by the robo-advisor between receiving information from the client in a timely manner and mitigating behavioral biases in the communicated risk profile. The authors argue that the optimal portfolio’s Sharpe ratio improves if the robo-advisor counters the client’s tendency to reduce market exposure during economic contractions when the market risk-return tradeoff is more favorable.