NSF Award
2340221
Algorithmic-based advice, a.k.a robo-advice, is disrupting the access to and delivery of advice regarding industries ranging from medical services to personal-shopping to financial advising and wealth management. Understanding the processes through which robo-advising affects individual decision-making is critical if we are to design advisors to benefit investors through better and tailored advice and to appropriately shape public policy with respect to the use of robo-advising. This project aims to study the effects of adopting robo-advice on the quality of decisions individuals make. The project will further the scientific knowledge about the cognitive and social impact of new technologies in the realm of professional advice to non-experts.<br/><br/>This project aims to study the following aspects of the relationship between robo-advising and decision-making under risk: (i) the extent to which robo-advising might correct well-documented behavioral biases in decision-making under risk; (ii) the extent to which robo-advising can improve over that provided by human financial advisers; and (iii) how individual investors' algorithm aversion makes human and robo-advice complementary, in which case robo-advising may increase rather than decrease the scope for human financial advice. To address the first question, the authors will employ several empirical techniques that range from linear regression to machine learning and artificial intelligence semi-parametric methods like boosted regression trees (BRT). For the second question, the authors will analyze the results of a randomized control trial involving the introduction of a tool to relax the attention constraints of financial advisers at a brokerage house. To address the third question, the authors will assess (i) whether, once given the option to access robo-advice, investors access it autonomously or reach out to human advisers for guidance; and (ii) whether, once investors start to use robo-advice, they reduce their reliance on human advisers.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
